Integrating Emerging Technologies with Digital Twins for Heritage Building Conservation: An Interdisciplinary Approach with Expert Insights and Bibliometric Analysis

Abstract

This review paper presents an interdisciplinary exploration of integrating emerging technologies, including digital twins (DTs), building information modeling (BIM), 3D laser scanning, machine learning (ML), and the Internet of Things (IoT), in the conservation of heritage buildings. Through a comprehensive literature review spanning from 1996 to 2024, expert interviews, a bibliometric analysis, and content analysis, the study highlights a significant shift toward a preventive approach to conservation, focusing on less invasive methods to ensure long-term preservation. It highlights the revolutionary impact of detailed digital representations and real-time monitoring on enhancing conservation efforts. The findings underscore significant research gaps, such as the need for standardized information protocols and the integration of DTs with BIM, while pointing to the potential of AR and VR in enriching heritage experiences. The paper advocates for a multidisciplinary approach to effectively harness these technologies, offering innovative solutions for the sustainable preservation of cultural heritage.

Full text

Citation: Mazzetto, S. Integrating
Emerging Technologies with Digital
Twins for Heritage Building
Conservation: An Interdisciplinary
Approach with Expert Insights and
Bibliometric Analysis. Heritage 2024,7,
6432–6479. https://doi.org/10.3390/
heritage7110300
Academic Editor: Jason J. Jung
Received: 27 September 2024
Revised: 31 October 2024
Accepted: 15 November 2024
Published: 20 November 2024
Copyright: © 2024 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Review
Integrating Emerging Technologies with Digital Twins for
Heritage Building Conservation: An Interdisciplinary Approach
with Expert Insights and Bibliometric Analysis
Silvia Mazzetto
Sustainable Architecture Laboratory, Department of Architecture, College of Architecture and Design,
Prince Sultan University, Riyadh 12435, Saudi Arabia; smazzetto@psu.edu.sa
Abstract: This review paper presents an interdisciplinary exploration of integrating emerging tech-
nologies, including digital twins (DTs), building information modeling (BIM), 3D laser scanning,
machine learning (ML), and the Internet of Things (IoT), in the conservation of heritage buildings.
Through a comprehensive literature review spanning from 1996 to 2024, expert interviews, a biblio-
metric analysis, and content analysis, the study highlights a significant shift toward a preventive
approach to conservation, focusing on less invasive methods to ensure long-term preservation. It
highlights the revolutionary impact of detailed digital representations and real-time monitoring on
enhancing conservation efforts. The findings underscore significant research gaps, such as the need
for standardized information protocols and the integration of DTs with BIM, while pointing to the
potential of AR and VR in enriching heritage experiences. The paper advocates for a multidisciplinary
approach to effectively harness these technologies, offering innovative solutions for the sustainable
preservation of cultural heritage.
Keywords: digital twins; building information modeling; 3D laser scanning; machine learning;
internet of things; heritage conservation; augmented reality; virtual reality
1. Introduction
The preservation of heritage buildings is a critical link to cultural, historical, and
architectural lineage, serving as windows into the lives, technologies, and esthetics of
past civilizations [
1
,
2
]. The task of conserving these structures is fraught with unique
challenges, including their advanced age, the materials used in their construction, and the
need to maintain historical authenticity while accommodating modern functionalities [
3
,
4
].
Looking into the future, the integration of historical architectural assets into the fabric of
global and European development agendas becomes increasingly crucial [
5
,
6
]. Initiatives
such as the European Green Deal [
7
] and the 2030 Digital Compass [
8
] underscore the
importance of preserving these assets, not just for their intrinsic value but also as part of
broader efforts to address challenges like climate change, urbanization, and mass tourism.
The conservation community is gradually shifting from reactive restoration practices
towards a preventive paradigm, emphasizing the early identification and mitigation of
deterioration [
9
]. This approach seeks to extend the lifespan of heritage structures with
minimal interventions, thereby reducing costs and preserving their original integrity.
The integration of building information modeling (BIM) into this framework further
enhances the conservation process [
10
]. BIM’s detailed digital representations provide a
holistic view of a building’s structure, materials, and history, allowing for precise planning
and implementation of conservation efforts [
11
]. In addition, the advancements in 3D
laser scanning technology have revolutionized the documentation of heritage buildings in
combination with BIM, capturing their intricate details with unprecedented precision [
12
].
These high-resolution 3D models serve as an invaluable resource for conservation, enabling
the identification of vulnerabilities and informing restoration strategies [
13
]. Moreover, the
Heritage 2024,7, 6432–6479. https://doi.org/10.3390/heritage7110300 https://www.mdpi.com/journal/heritage

Heritage 2024,76433
adoption of machine learning and the IoT in the field of heritage conservation introduces
predictive and real-time monitoring capabilities [
14
,
15
]. Machine learning algorithms,
trained on data from IoT sensors and other sources like laser scanners, can predict deterio-
ration, guiding preemptive conservation actions [16,17].
The digital twin (DT) concept (Figure 1), characterized by the tripartite model of the
physical asset, its digital counterpart, and the dynamic data exchange between them, has
become a cornerstone of this technological integration [18].
Heritage 2024, 7, FOR PEER REVIEW 2
precision [12]. These high-resolution 3D models serve as an invaluable resource for con-
servation, enabling the identification of vulnerabilities and informing restoration strate-
gies [13]. Moreover, the adoption of machine learning and the IoT in the field of heritage
conservation introduces predictive and real-time monitoring capabilities [14,15]. Machine
learning algorithms, trained on data from IoT sensors and other sources like laser scanners,
can predict deterioration, guiding preemptive conservation actions [16,17].
The digital twin (DT) concept (Figure 1), characterized by the tripartite model of the
physical asset, its digital counterpart, and the dynamic data exchange between them, has
become a cornerstone of this technological integration [18].
Figure 1 illustrates the “Heritage Building Digital Twin (HBDT) Model” framework,
showcasing the integration of machine learning, IoT technologies, laser scanning, BIM,
and decision-making processes. This model supports the argument for a shift from reac-
tive to proactive conservation, as each component contributes to a more comprehensive
conservation strategy. For example, BIM’s digital representations facilitate precise plan-
ning, while IoT technologies enable real-time monitoring, addressing early deterioration
detection as discussed above. Despite its broad applicability, the precise definition and
implementation of DTs in heritage conservation remain subjects of ongoing research and
debate.
Figure 1. A framework of the Heritage Building Digital Twin (HBDT) Model as envisioned in this
study, incorporating machine learning, IoT technologies, laser scanning, BIM, and decision-making
processes.
Out of the above, this paper aims to explore the intersection of DT technology with
heritage conservation, examining innovative applications and methodologies that have
begun to emerge. Through a comprehensive review of current literature and identification
of emerging trends, this study highlights the potential of DTs, BIM, laser scanning, ma-
chine learning, and IoT technologies to revolutionize the field of heritage conservation.
This multifaceted approach, bridging traditional architectural and archeological practices
with the latest in computer science and engineering, offers a new paradigm for tackling
the complex challenges inherent in preserving heritage buildings. The potential of these
technologies to enhance damage assessment, improve public engagement through
Figure 1. A framework of the Heritage Building Digital Twin (HBDT) Model as envisioned in this study,
incorporating machine learning, IoT technologies, laser scanning, BIM, and decision-making processes.
Figure 1illustrates the “Heritage Building Digital Twin (HBDT) Model” framework,
showcasing the integration of machine learning, IoT technologies, laser scanning, BIM, and
decision-making processes. This model supports the argument for a shift from reactive to
proactive conservation, as each component contributes to a more comprehensive conserva-
tion strategy. For example, BIM’s digital representations facilitate precise planning, while
IoT technologies enable real-time monitoring, addressing early deterioration detection as
discussed above. Despite its broad applicability, the precise definition and implementation
of DTs in heritage conservation remain subjects of ongoing research and debate.
Out of the above, this paper aims to explore the intersection of DT technology with
heritage conservation, examining innovative applications and methodologies that have
begun to emerge. Through a comprehensive review of current literature and identification
of emerging trends, this study highlights the potential of DTs, BIM, laser scanning, machine
learning, and IoT technologies to revolutionize the field of heritage conservation. This
multifaceted approach, bridging traditional architectural and archeological practices with
the latest in computer science and engineering, offers a new paradigm for tackling the
complex challenges inherent in preserving heritage buildings. The potential of these tech-
nologies to enhance damage assessment, improve public engagement through augmented
and virtual reality, and navigate the ethical considerations of digital replication underscores
their transformative impact on heritage conservation.
This paper differentiates itself from the work of Annalaura Vuoto et al. [
19
] by extend-
ing the exploration of DT technology in the conservation of heritage buildings to include a
wider array of emerging technologies such as BIM, 3D laser scanning, machine learning,

Heritage 2024,76434
and the IoT. It explores the synergistic integration of these technologies with DTs to foster
more effective conservation strategies, highlighting their collective potential in proactive
preservation efforts. Unlike [
19
] systematic literature review, this approach is also enriched
by conducting interviews with experts in the field and employing bibliometric analysis
over a period of 28 years between 1996 and 2024 to map out the past and current research
landscape and trends.
2. Methodology
The methodological research approach, depicted in Figure 2, follows a meticulously
organized six-phase procedure designed to systematically gather literature related to
DT technology for heritage buildings from 1996 to 2024. Figure 2visually represents
the structured six-step framework guiding the literature review process in this study.
Beginning with the formulation of targeted search phrases, this methodology ensures
the precise identification of relevant studies at the intersection of DT technology and
heritage conservation. As shown in Figure 2, phases such as document screening and
bibliometric analysis allow for a comprehensive and unbiased selection of literature. Each
step contributes to refining the selection from an initial pool of 457 documents to 186,
as specified later in Table 1, underscoring the rigorous inclusion and exclusion criteria
employed to maintain methodological rigor. Initially, a collection of search phrases was
developed specifically designed to pinpoint the relevant cross-section between DT and
heritage buildings. Following this, the third phase involved conducting an extensive search
for literature across various databases, such as Web of Science, Scopus, and Google Scholar,
to accumulate a wide range of potential articles as the research started with 457 documents,
including journal papers, conferences, books, and theses, and ended up with 186 documents
based on the criteria shown in Table 1.
Heritage 2024, 7, FOR PEER REVIEW 4
in reducing biases and subjective interpretations, promoting a balanced and extensive
comprehension of the subject. Echoing previous research recommendations, this method
underscores the benefits of integrating different analytical techniques for a nuanced un-
derstanding of topics. The analysis began with a bibliometric review of the 186 articles
fiing the criteria, evaluating aspects such as publication frequency, contributing coun-
tries, and keyword trends. The next phase involved a qualitative assessment of article con-
tents, organizing the literature by construction phases and DT applications within those
contexts.
In analyzing the selected documents for this study, we adopted a methodological
approach that utilizes JSON (JavaScript Object Notation) data formats and Python pro-
gramming to process data extracted from the literature. This digital method facilitated the
efficient management of the extensive information from 186 documents in the primary
dataset. Python and JSON integration improved the precision and speed of the quantita-
tive analysis and enhanced the depth of the qualitative review. Through tailored scripts
and algorithms, the research integrates data from diverse sources, blending existing
knowledge with fresh insights.
Figure 2. Six-step methodological framework for literature review on DT for heritage buildings.
Figure 2. Six-step methodological framework for literature review on DT for heritage buildings.

Heritage 2024,76435
Table 1. Criteria for systematic review of DT applications in heritage building conservation.
Criteria Category Specific Criteria Inclusion Parameters Exclusion Parameters
Study Design - Empirical research
- Conceptual analysis
- Methodological approach
- Use of DT, laser scanning, machine
learning, IoT, and BIM in analysis or
synthesis—DT as a central theme
- Studies where DT is peripheral
- Literature reviews without
original analysis
Subject Focus
- DT applications
- DT, laser scanning, machine
learning, IoT, and BIM in heritage
conservation
- DT for structural analysis
- Studies focusing on DT for
management or conservation of
heritage assets
- DT in assessing structural integrity
- Studies with vague or indirect
relation to DT
- DT applied to non-heritage
contexts
Asset Type
- Heritage buildings
- Archeological sites
- Cultural landscapes
- Artifacts within historical context
- Studies centered on DT, laser
scanning, machine learning, IoT, and
BIM management of the above assets
- Analysis on DT’s role in conservation
- Studies on new constructions
- DT applied to non-cultural or
non-historical sites
Temporal Scope - Longitudinal studies
- Cross-sectional studies within
the date range
- Studies covering DT evolution
within the timeline
- Snapshots of DT applications at
specific time points
- Studies outside the set temporal
range
- Future predictions without
historical data
Methodological Rigor - Use of validated instruments
- Clear analytical frameworks
- Replicable study designs
- Studies with robust methodology
- Clear definition and application of
DT
- Studies with methodological
flaws
- Inadequate definition of DT
usage
Geographic Relevance
- Studies in areas with known
heritage sites
- DT applied in diverse cultural
settings
- DT, laser scanning, machine learning,
IoT, and BIM studies reflect the
geographical diversity of heritage sites
- Case studies from regions with high
heritage significance
- Studies with no clear
geographical linkage to heritage
sites
- DT studies in areas without
significant heritage presence
Table 1outlines the detailed criteria used for selecting documents in the systematic
review, specifying parameters such as study design, subject focus, asset type, and temporal
scope. Each category is broken down into specific inclusion and exclusion parameters,
ensuring that selected studies focus on DT applications for heritage buildings, incorporat-
ing essential technologies like laser scanning, BIM, and machine learning. For instance,
studies applying DT in non-heritage contexts were excluded, while those exploring its
role in structural analysis or conservation within historical sites were included. This table
clarifies the methodical filtering process, enhancing transparency in the study selection.
The literature search strategy aimed for inclusivity, extending beyond author-specified
keywords to include titles and abstracts and expanding to all document fields in further
searches. This approach is intended to maximize relevant paper inclusion, addressed by
thorough manual screening. The search strategy was designed with precision, utilizing
a strategic mix of key terms specifically focusing on DTs, BIM, laser scanning, machine
learning, and heritage buildings within the AEC (architecture, engineering, and construc-
tion) industry. Keywords employed in the search included “DT”, “BIM integration”, “Laser
Scanning in Construction”, “Machine Learning in Heritage Conservation”, “Heritage Build-
ing Restoration”, “Digital Preservation”, “3D Modelling in Heritage Buildings”, “BIM for
Heritage Conservation”, “DT Applications in Historic Buildings”, “Machine Learning for
Architectural Analysis”, and “Laser Scanning for Structural Analysis”. Boolean operators
“AND” and “OR” were strategically used to narrow the search to documents that contain
essential concepts jointly and to expand the search to cover a wide range of related subjects.
To conduct a thorough and impartial review, a tripartite analysis strategy combining
quantitative [
20
] and qualitative [
21
] methods was adopted. This approach is instrumental
in reducing biases and subjective interpretations, promoting a balanced and extensive
comprehension of the subject. Echoing previous research recommendations, this method
underscores the benefits of integrating different analytical techniques for a nuanced un-
derstanding of topics. The analysis began with a bibliometric review of the 186 articles
fitting the criteria, evaluating aspects such as publication frequency, contributing countries,

Heritage 2024,76436
and keyword trends. The next phase involved a qualitative assessment of article contents,
organizing the literature by construction phases and DT applications within those contexts.
In analyzing the selected documents for this study, we adopted a methodological
approach that utilizes JSON (JavaScript Object Notation) data formats and Python pro-
gramming to process data extracted from the literature. This digital method facilitated
the efficient management of the extensive information from 186 documents in the primary
dataset. Python and JSON integration improved the precision and speed of the quantitative
analysis and enhanced the depth of the qualitative review. Through tailored scripts and
algorithms, the research integrates data from diverse sources, blending existing knowledge
with fresh insights.
In step 4, as part of the in-depth study on DT’s effectiveness for heritage buildings,
a detailed survey was developed to gather expert opinions on various aspects of DT’s
application. The survey, designed with questions on a Likert scale from 1 to 5 in addition
to open-ended questions, aimed to evaluate DT’s effectiveness in heritage buildings. A
wide range of architecture, engineering, and construction (AEC) professionals, including
architects, engineers, and project managers, were invited to participate through professional
networks and social media, ensuring a diverse and informed set of responses. Experts,
chosen for their experience and engagement with heritage buildings, were given access to
an online survey platform (SurveyXact [
22
]), emphasizing ease of use and confidentiality.
The survey was available for two weeks, with reminders sent to encourage participation
and highlight the value of their insights for advancing DT’s understanding of heritage
buildings. Participants were informed through the consent form about the purpose of
the research, how their data would be used, and the steps taken to ensure confidentiality.
They were assured that all findings would be reported in aggregate form, without any
possibility of individual identification. Table 2shows the profile and roles of the experts
who answered the survey, as that was asked throughout the survey. Table 2details the
profiles and expertise of the 23 survey participants, including their roles in conservation
and specific technologies used. The table highlights diversity among participants, with
professionals from architecture, engineering, and conservation disciplines contributing
insights on DT, BIM, and laser scanning for heritage building preservation. For example,
the eight architects and architectural historians primarily focus on documentation and
restoration, using BIM and laser scanning. This diverse representation ensures that the
survey responses reflect a broad spectrum of perspectives on the applicability of these
technologies in heritage conservation.
Table 2. Expert profiles based on the 23 participants in the survey.
Expert Profile Role in Conservation Technologies Used Number of Experts
Architects and Architectural
Historians Restoration, preservation, and
documentation of heritage buildings BIM and Laser Scanning 8
Civil and Structural Engineers Assessing the physical condition of
heritage structures Machine Learning and Laser
Scanning 5
Conservation Specialists Conservation and restoration of
heritage sites DTs 6
Project Managers Overseeing conservation projects Various Technological Tools 4
After the survey closed, responses from the 23 experts (originally 150 were invited, as
in Table 3) were analyzed to calculate average effectiveness ratings for each DT application
area. Table 3presents the survey’s engagement metrics, showing that, out of 150 invited ex-
perts, 23 responded, resulting in a completion rate of 15.3%. While the sample size may limit
the generalizability of the findings, the participants’ collective expertise provides valuable
insights into the effectiveness of DT, laser scanning, machine learning, and BIM in heritage
conservation. This table underscores the survey’s reach and highlights potential areas for
increasing engagement in future studies. The analysis of survey responses was conducted
using a combination of statistical and qualitative methods using Python [
23
]. Thematic

Heritage 2024,76437
analysis [
24
] was applied to open-ended responses to extract common themes related to
the benefits, challenges, and applications of the technologies in heritage conservation.
Table 3. Demonstrates the overall engagement and response rate for the survey.
Total Invited Responses Received Completion Rate
150 23 15.3%
The survey comprised a series of structured questions designed to elicit both quantita-
tive and qualitative responses from the experts. The questions were divided into sections,
each focusing on one of the emerging technologies (laser scanning, machine learning, BIM,
and DTs) in the context of heritage building conservation. Examples of the questions
include the following:
1. Quantitative (Likert scale) Questions:
•
“Rate the effectiveness of laser scanning in capturing accurate architectural details
of heritage buildings (1–5).”
•
“How effective do you find machine learning algorithms in predicting structural
vulnerabilities in heritage buildings (1–5)?”
•
“Evaluate the utility of BIM in the planning and execution of heritage conserva-
tion projects (1–5).”
•
“Assess the impact of DTs in enhancing the maintenance and preservation of
heritage sites (1–5).”
2. Qualitative (open-ended) Questions:
•
“Describe a project where you utilized laser scanning for heritage conservation.
What were the key benefits and challenges?”
•
“In your experience, how does machine learning contribute to the conservation
of heritage buildings? Please provide examples.”
•
“Discuss how BIM has changed the approach to heritage building conservation
within your projects.”
•“Share your insights on the future potential of DTs in heritage conservation.”
One notable limitation of this survey lies in its relatively small sample size of 23 experts,
which, while providing valuable insights, may not capture the full spectrum of opinions
and experiences within the broader field of heritage conservation. The extent to which
these findings can be generalized to all professionals working with emerging technologies
in heritage building conservation is therefore constrained. Moreover, the diversity of the
respondents, in terms of their geographic locations, cultural backgrounds, and the specific
nature of their work within the conservation domain, might not be adequately represented.
Furthermore, the survey’s focus on specific technologies—laser scanning, machine
learning, BIM, and DTs—while necessary for depth of analysis may overlook the interplay
between these technologies and other traditional or emerging tools in heritage conservation.
The rapid pace of technological advancement means that the relevance of the survey find-
ings could diminish over time, as new tools emerge, and existing ones evolve. Additionally,
the reliance on self-reported effectiveness ratings and experiences introduces subjective
biases that could influence the overall interpretation of the technologies’ impact. These lim-
itations underscore the need for ongoing research, incorporating larger and more diverse
samples, and a dynamic approach to technology evaluation in the conservation field.
This step was followed by an in-depth examination of the selected literature, extracting
and preprocessing data, and then analyzing it with Python for a detailed and computational
understanding of the research landscape. The last step synthesizes the literature analysis
findings, summarizing key outcomes, pinpointing research gaps, and outlining future
research directions.

Heritage 2024,76438
3. Results
In this section, the paper explores the reviewed findings of DT for heritage buildings.
The investigation, propelled by the ambition to bridge the gap identified in the previous
literature, employs a combined methodological framework that encompasses both bib-
liometric and qualitative content analyses. This dual approach enables us to chart the
progression, delineate the current landscape, and unveil the burgeoning potential of DT
technology for heritage conservation.
3.1. Bibliometric Analysis
This section presents a bibliometric analysis of literature in the field of cultural heritage
preservation, mapping the evolution and current state of research. Through an examination
of publication trends over the years, this analysis identifies the most influential journals,
prolific authors, and leading countries contributing to the discourse. The study of publi-
cation counts per year within top journals offers insights into the scholarly community’s
focus areas, while the distribution of articles across countries highlights global participation
and interest. Additionally, a conceptual map synthesizes major themes and research gaps,
guiding future inquiries in cultural heritage preservation.
3.1.1. Publication Trends in Cultural Heritage Preservation
The initial subsection of the bibliometric study begins by analyzing the trends in
publications from 1996 to 2024, as depicted in Figure 3. This graph illustrates the growth in
the number of publications over the years, reflecting a burgeoning interest and expanding
research in the domain of cultural heritage preservation. The period from the early 2000s
shows a gradual increase in output, with a significant rise observed in the last decade, indi-
cating intensified scholarly activity and a potential increase in the field’s prioritization. The
peak in publication numbers around the early 2020s suggests a response to global cultural
trends or technological advancements that may have facilitated more extensive research
and discourse. Figure 3captures the steady rise in publications from 1996 through 2024,
highlighting surges over the last decade. This increase is indicative of growing scholarly
and societal interest, with peaks in the early 2020s possibly aligned with advancements in
conservation technologies or heightened global attention on cultural heritage. These trends
suggest a potential shift in prioritizing research that integrates heritage preservation with
modern, sustainable practices.
Heritage 2024, 7, FOR PEER REVIEW 8
Figure 3. Publication trends over years.
3.1.2. Leading Journals, Authors, and Countries in Cultural Heritage
Preservation Research
Continuing the bibliometric investigation, Figure 4 showcases the leading academic
journals contributing to the discourse on cultural heritage preservation. Figure 4 ranks the
top 10 journals publishing on cultural heritage preservation, with Sustainability leading,
reflecting a cross-disciplinary approach focused on sustainable conservation. Figure 5 fur-
ther breaks down publication paerns within these journals from 2015 to 2024, illustrating
how contributions fluctuate with emerging conservation technologies or evolving aca-
demic focus areas. Significant spikes may align with breakthroughs in preservation tech-
nology or policy changes, highlighting how scholarly priorities adapt over time. Figure 4
bar graph enumerates the top 10 journals by the number of articles published. The Sus-
tainability journal leads, indicative of a strong interdisciplinary focus on sustainable prac-
tices in heritage conservation. This is followed by Automation in Construction and the Jour-
nal of Cultural Heritage, highlighting the technological and cultural facets of the field, re-
spectively. Energies and other journals listed reveal the multifaceted nature of research,
spanning areas such as energy efficiency in heritage buildings and advanced remote sens-
ing techniques.
Investigating deeper into the specifics, Figure 5 dissects the annual publication
counts per year for the top 10 journals identified previously. This stacked bar chart reveals
the dynamic contribution paerns of each journal over time, illustrating fluctuations and
growth trends in published articles from 2015 to 2024. It highlights periods of increased
research activity and potential shifts in the thematic emphasis of each journal. For instance,
notable spikes in article counts for certain journals may correlate with key technological
advancements or policy changes in the field of heritage preservation. This temporal anal-
ysis offers an insightful perspective on how the focus of research within each journal
evolves and responds to the field’s emerging challenges and opportunities.
Figure 3. Publication trends over years.

Heritage 2024,76439
3.1.2. Leading Journals, Authors, and Countries in Cultural Heritage
Preservation Research
Continuing the bibliometric investigation, Figure 4showcases the leading academic
journals contributing to the discourse on cultural heritage preservation. Figure 4ranks the
top 10 journals publishing on cultural heritage preservation, with Sustainability leading, re-
flecting a cross-disciplinary approach focused on sustainable conservation. Figure 5further
breaks down publication patterns within these journals from 2015 to 2024, illustrating how
contributions fluctuate with emerging conservation technologies or evolving academic
focus areas. Significant spikes may align with breakthroughs in preservation technology or
policy changes, highlighting how scholarly priorities adapt over time. Figure 4bar graph
enumerates the top 10 journals by the number of articles published. The Sustainability
journal leads, indicative of a strong interdisciplinary focus on sustainable practices in
heritage conservation. This is followed by Automation in Construction and the Journal of
Cultural Heritage, highlighting the technological and cultural facets of the field, respectively.
Energies and other journals listed reveal the multifaceted nature of research, spanning areas
such as energy efficiency in heritage buildings and advanced remote sensing techniques.
Heritage 2024, 7, FOR PEER REVIEW 9
Figure 4. Top 10 journals.
Figure 5. Publication counts per year for the top 10 journals.
Moreover, Figure 6 enumerates the top 10 authors who have made significant schol-
arly contributions to the field of cultural heritage preservation. The horizontal bar chart
highlights the number of articles each author has published, serving as an indicator of
their impact and activity within this research community. The evaluation of the top au-
thors in Figure 6 is based on a comprehensive analysis of their scholarly productivity and
the impact of their research within the field of cultural heritage preservation. This assess-
ment considers both the number of articles published by each author and the citation met-
rics associated with their work, which collectively serve as indicators of their influence
and activity in the research community. By incorporating both productivity and citation
Figure 4. Top 10 journals.
Investigating deeper into the specifics, Figure 5dissects the annual publication counts
per year for the top 10 journals identified previously. This stacked bar chart reveals the
dynamic contribution patterns of each journal over time, illustrating fluctuations and
growth trends in published articles from 2015 to 2024. It highlights periods of increased
research activity and potential shifts in the thematic emphasis of each journal. For instance,
notable spikes in article counts for certain journals may correlate with key technological
advancements or policy changes in the field of heritage preservation. This temporal analysis
offers an insightful perspective on how the focus of research within each journal evolves
and responds to the field’s emerging challenges and opportunities.
Moreover, Figure 6enumerates the top 10 authors who have made significant schol-
arly contributions to the field of cultural heritage preservation. The horizontal bar chart
highlights the number of articles each author has published, serving as an indicator of their
impact and activity within this research community. The evaluation of the top authors
in Figure 6is based on a comprehensive analysis of their scholarly productivity and the
impact of their research within the field of cultural heritage preservation. This assessment
considers both the number of articles published by each author and the citation metrics asso-
ciated with their work, which collectively serve as indicators of their influence and activity

Heritage 2024,76440
in the research community. By incorporating both productivity and citation impact, the
analysis provides a clearer understanding of the contributions made by these researchers,
highlighting their significance in advancing knowledge and practices in cultural heritage
preservation. Figure 6showcases the most prolific authors in the field, providing a bench-
mark for influential voices shaping research trends and guiding conservation practices.
This chart not only signals the field’s active contributors but also underscores the collabo-
rative, cumulative effort driving innovation in heritage preservation. The authors listed
have contributed to advancing knowledge and understanding in this domain, with the
chart reflecting the extent of each individual’s academic engagement. Their collective work
forms the backbone of current trends and developments, influencing both the academic
and practical aspects of heritage preservation.
Heritage 2024, 7, FOR PEER REVIEW 9
Figure 4. Top 10 journals.
Figure 5. Publication counts per year for the top 10 journals.
Moreover, Figure 6 enumerates the top 10 authors who have made significant schol-
arly contributions to the field of cultural heritage preservation. The horizontal bar chart
highlights the number of articles each author has published, serving as an indicator of
their impact and activity within this research community. The evaluation of the top au-
thors in Figure 6 is based on a comprehensive analysis of their scholarly productivity and
the impact of their research within the field of cultural heritage preservation. This assess-
ment considers both the number of articles published by each author and the citation met-
rics associated with their work, which collectively serve as indicators of their influence
and activity in the research community. By incorporating both productivity and citation
Figure 5. Publication counts per year for the top 10 journals.
Furthermore, Figure 7provides a visual distribution of the percentage of articles
contributed by the top five countries in the field of cultural heritage preservation. The
gradient-colored bar chart delineates the proportionate academic output, with China lead-
ing significantly, followed by Italy, the United Kingdom, Spain, and India. These statistics
reveal the geographical diversity in research and the varying degrees of emphasis placed
on cultural heritage preservation across different nations. This information is critical for un-
derstanding the global landscape of cultural heritage research, including the identification
of regions with high research outputs and potential collaborative opportunities. Figure 7,
which outlines the percentage of articles by the top five contributing countries, reveals
China’s substantial output, followed by Italy, the UK, Spain, and India. This geographic dis-
tribution highlights the emphasis placed on heritage preservation in these countries, likely
driven by each nation’s wealth of heritage sites and their vested interest in preservation.
This insight into global academic participation signals potential international collaboration
opportunities in addressing shared conservation challenges.

Heritage 2024,76441
Heritage 2024, 7, FOR PEER REVIEW 10
impact, the analysis provides a clearer understanding of the contributions made by these
researchers, highlighting their significance in advancing knowledge and practices in cul-
tural heritage preservation. Figure 6 showcases the most prolific authors in the field,
providing a benchmark for influential voices shaping research trends and guiding conser-
vation practices. This chart not only signals the field’s active contributors but also under-
scores the collaborative, cumulative effort driving innovation in heritage preservation.
The authors listed have contributed to advancing knowledge and understanding in this
domain, with the chart reflecting the extent of each individual’s academic engagement.
Their collective work forms the backbone of current trends and developments, influencing
both the academic and practical aspects of heritage preservation.
Figure 6. Top 10 authors based on [12,14,18,19,25–30].
Furthermore, Figure 7 provides a visual distribution of the percentage of articles con-
tributed by the top five countries in the field of cultural heritage preservation. The gradi-
ent-colored bar chart delineates the proportionate academic output, with China leading
significantly, followed by Italy, the United Kingdom, Spain, and India. These statistics
reveal the geographical diversity in research and the varying degrees of emphasis placed
on cultural heritage preservation across different nations. This information is critical for
understanding the global landscape of cultural heritage research, including the identifica-
tion of regions with high research outputs and potential collaborative opportunities. Fig-
ure 7, which outlines the percentage of articles by the top five contributing countries, re-
veals China’s substantial output, followed by Italy, the UK, Spain, and India. This geo-
graphic distribution highlights the emphasis placed on heritage preservation in these
countries, likely driven by each nation’s wealth of heritage sites and their vested interest
in preservation. This insight into global academic participation signals potential interna-
tional collaboration opportunities in addressing shared conservation challenges.
Figure 6. Top 10 authors based on [12,14,18,19,25–30].
Heritage 2024, 7, FOR PEER REVIEW 11
Figure 7. Percentage of articles per top 5 countries.
3.1.3. Research Gaps in Heritage Preservation
Based on the bibliometric analysis, the conceptual map (Figure 8) captures the land-
scape of current research gaps in cultural heritage preservation, pointing to areas such as
advanced digital documentation techniques, the integration of digital twin technology
with building information modeling, and the ethical dimensions of technological applica-
tion. Highlighting the synergy between various advanced methodologies, it underscores
the need for a multidisciplinary approach to conservation practices. Figure 8 visually syn-
thesizes key research gaps within the domain, including digital documentation, digital
twin integration with building information modeling (BIM), and ethical considerations in
technological applications. This map emphasizes the necessity for interdisciplinary solu-
tions and provides direction for research that could close these gaps, integrating techno-
logical advancement with traditional conservation practices. The importance of energy
efficiency, the application of IoT in preventive conservation, and the potential of non-de-
structive technologies in the restoration of cultural heritage also feature prominently.
Figure 7. Percentage of articles per top 5 countries.
3.1.3. Research Gaps in Heritage Preservation
Based on the bibliometric analysis, the conceptual map (Figure 8) captures the land-
scape of current research gaps in cultural heritage preservation, pointing to areas such as
advanced digital documentation techniques, the integration of digital twin technology with
building information modeling, and the ethical dimensions of technological application.
Highlighting the synergy between various advanced methodologies, it underscores the
need for a multidisciplinary approach to conservation practices. Figure 8visually synthe-
sizes key research gaps within the domain, including digital documentation, digital twin
integration with building information modeling (BIM), and ethical considerations in tech-
nological applications. This map emphasizes the necessity for interdisciplinary solutions
and provides direction for research that could close these gaps, integrating technological

Heritage 2024,76442
advancement with traditional conservation practices. The importance of energy efficiency,
the application of IoT in preventive conservation, and the potential of non-destructive
technologies in the restoration of cultural heritage also feature prominently.
Heritage 2024, 7, FOR PEER REVIEW 11
Figure 7. Percentage of articles per top 5 countries.
3.1.3. Research Gaps in Heritage Preservation
Based on the bibliometric analysis, the conceptual map (Figure 8) captures the land-
scape of current research gaps in cultural heritage preservation, pointing to areas such as
advanced digital documentation techniques, the integration of digital twin technology
with building information modeling, and the ethical dimensions of technological applica-
tion. Highlighting the synergy between various advanced methodologies, it underscores
the need for a multidisciplinary approach to conservation practices. Figure 8 visually syn-
thesizes key research gaps within the domain, including digital documentation, digital
twin integration with building information modeling (BIM), and ethical considerations in
technological applications. This map emphasizes the necessity for interdisciplinary solu-
tions and provides direction for research that could close these gaps, integrating techno-
logical advancement with traditional conservation practices. The importance of energy
efficiency, the application of IoT in preventive conservation, and the potential of non-de-
structive technologies in the restoration of cultural heritage also feature prominently.
Figure 8. Conceptual map for cultural heritage preservation, outlining the key research gaps based
on the review documents.
The subsequent sections will offer a comprehensive analysis of these identified gaps.
Through in-depth discussions, each theme will be explored, situating current research
within a broader context and proposing directions for future inquiries.
3.2. Content Analysis
The bibliometric analysis enabled us to identify research gaps in the field of her-
itage conservation, as illustrated in Figure 9. These gaps are categorized into three main
areas: expert insights and professional perspectives, emerging technologies synthesis,
and bridging disciplines. The paper will explore each category in greater detail in the
subsequent sections.
Heritage 2024, 7, FOR PEER REVIEW 12
Figure 8. Conceptual map for cultural heritage preservation, outlining the key research gaps based
on the review documents.
The subsequent sections will offer a comprehensive analysis of these identified gaps.
Through in-depth discussions, each theme will be explored, situating current research
within a broader context and proposing directions for future inquiries.
3.2. Content Analysis
The bibliometric analysis enabled us to identify research gaps in the field of heritage
conservation, as illustrated in Figure 9. These gaps are categorized into three main areas:
expert insights and professional perspectives, emerging technologies synthesis, and
bridging disciplines. The paper will explore each category in greater detail in the subse-
quent sections.
Figure 9. Mindmap of “Content Analysis”, showcasing the hierarchical structure of expert insights,
emerging technologies, and interdisciplinary bridges within the context of heritage conservation
and digital replication technologies.
Expert Insights and Professional Perspectives
The survey conducted among professionals involved in heritage conservation re-
vealed insightful data on the perceived effectiveness of DT technology across various con-
servation activities. Participants from diverse disciplines such as architecture, engineering,
and conservation were asked to rate the effectiveness of DT on a Likert scale from one (not
effective) to five (highly effective) in areas such as structural analysis, historical research,
restoration planning, and visitor engagement.
As can be seen in Figure 10, the survey results were compiled into a statistical over-
view, which highlighted a strong consensus on the effectiveness of DT in structural anal-
ysis, with the majority rating it between four and five. Figures 9 and 10 dive into expert
perspectives, showing DT technology’s perceived effectiveness across fields like structural
analysis, restoration planning, and visitor engagement. These findings suggest that DT’s
strongest impact lies in structural analysis, while further research and collaboration are
needed to improve its efficacy in historical research and public engagement. This suggests
a high level of confidence in DT’s ability to provide precise and actionable insights into
the structural health of heritage buildings. However, in the field of historical research, DT
was rated slightly lower, averaging around 3.5, indicating moderate to high effectiveness.
The technology’s capability to visualize and simulate historical buildings and contexts,
although appreciated, pointed to a need for more interdisciplinary work to increase its
usefulness for historians. For restoration planning, DT received an effectiveness rating
that closely mirrored its reception in structural analysis, with many professionals empha-
sizing its ability to accurately assess the condition of buildings and facilitate the planning
of conservation interventions. Visitor engagement was the area with the broadest range of
responses, averaging a three. This variance could be aributed to the emergent state of
Figure 9. Mindmap of “Content Analysis”, showcasing the hierarchical structure of expert insights,
emerging technologies, and interdisciplinary bridges within the context of heritage conservation and
digital replication technologies.
Expert Insights and Professional Perspectives
The survey conducted among professionals involved in heritage conservation revealed
insightful data on the perceived effectiveness of DT technology across various conservation

Heritage 2024,76443
activities. Participants from diverse disciplines such as architecture, engineering, and
conservation were asked to rate the effectiveness of DT on a Likert scale from one (not
effective) to five (highly effective) in areas such as structural analysis, historical research,
restoration planning, and visitor engagement.
As can be seen in Figure 10, the survey results were compiled into a statistical overview,
which highlighted a strong consensus on the effectiveness of DT in structural analysis, with
the majority rating it between four and five. Figures 9and 10 dive into expert perspectives,
showing DT technology’s perceived effectiveness across fields like structural analysis,
restoration planning, and visitor engagement. These findings suggest that DT’s strongest
impact lies in structural analysis, while further research and collaboration are needed
to improve its efficacy in historical research and public engagement. This suggests a
high level of confidence in DT’s ability to provide precise and actionable insights into the
structural health of heritage buildings. However, in the field of historical research, DT
was rated slightly lower, averaging around 3.5, indicating moderate to high effectiveness.
The technology’s capability to visualize and simulate historical buildings and contexts,
although appreciated, pointed to a need for more interdisciplinary work to increase its
usefulness for historians. For restoration planning, DT received an effectiveness rating that
closely mirrored its reception in structural analysis, with many professionals emphasizing
its ability to accurately assess the condition of buildings and facilitate the planning of
conservation interventions. Visitor engagement was the area with the broadest range of
responses, averaging a three. This variance could be attributed to the emergent state of
applying DT in public engagement contexts. Professionals acknowledged the potential of
DT to revolutionize visitor experiences through interactive and immersive storytelling but
also recognized the current limitations in widespread implementation.
Heritage 2024, 7, FOR PEER REVIEW 13
applying DT in public engagement contexts. Professionals acknowledged the potential of
DT to revolutionize visitor experiences through interactive and immersive storytelling but
also recognized the current limitations in widespread implementation.
Figure 10. The effectiveness of DT through different fields related to heritage buildings.
At the outset in Figure 11, with 1–5 years of experience, ratings oscillate dramatically
between five (highly effective) and lower ratings like two (slightly effective), suggesting a
divided opinion among newcomers to the field. This variability reflects differing initial
expectations of DT’s capabilities or varied exposure to DT in practical scenarios. Figures
11 and 12 explore expert ratings on technologies such as laser scanning, BIM, and machine
learning. High ratings for laser scanning and BIM reflect their utility in creating detailed,
accessible models of heritage sites, essential for preservation. However, Figure 12’s varia-
bility in machine learning ratings signals ongoing skepticism and a need for refinement
before AI-driven approaches become mainstream in heritage conservation.
Figure 11. The relation between years of experience and DT effectiveness rating.
Interestingly, there are instances of high ratings [5] even among participants with 6–
15 years of experience, indicating that some mid-career professionals still find significant
value in DT applications. However, the general trend still shows a movement towards
Figure 10. The effectiveness of DT through different fields related to heritage buildings.
At the outset in Figure 11, with 1–5 years of experience, ratings oscillate dramatically
between five (highly effective) and lower ratings like two (slightly effective), suggest-
ing a divided opinion among newcomers to the field. This variability reflects differing
initial expectations of DT’s capabilities or varied exposure to DT in practical scenarios.
Figures 11 and 12 explore expert ratings on technologies such as laser scanning, BIM, and
machine learning. High ratings for laser scanning and BIM reflect their utility in cre-
ating detailed, accessible models of heritage sites, essential for preservation. However,
Figure 12’s
variability in machine learning ratings signals ongoing skepticism and a need
for refinement before AI-driven approaches become mainstream in heritage conservation.

Heritage 2024,76444
Heritage 2024, 7, FOR PEER REVIEW 13
applying DT in public engagement contexts. Professionals acknowledged the potential of
DT to revolutionize visitor experiences through interactive and immersive storytelling but
also recognized the current limitations in widespread implementation.
Figure 10. The effectiveness of DT through different fields related to heritage buildings.
At the outset in Figure 11, with 1–5 years of experience, ratings oscillate dramatically
between five (highly effective) and lower ratings like two (slightly effective), suggesting a
divided opinion among newcomers to the field. This variability reflects differing initial
expectations of DT’s capabilities or varied exposure to DT in practical scenarios. Figures
11 and 12 explore expert ratings on technologies such as laser scanning, BIM, and machine
learning. High ratings for laser scanning and BIM reflect their utility in creating detailed,
accessible models of heritage sites, essential for preservation. However, Figure 12’s varia-
bility in machine learning ratings signals ongoing skepticism and a need for refinement
before AI-driven approaches become mainstream in heritage conservation.
Figure 11. The relation between years of experience and DT effectiveness rating.
Interestingly, there are instances of high ratings [5] even among participants with 6–
15 years of experience, indicating that some mid-career professionals still find significant
value in DT applications. However, the general trend still shows a movement towards
Figure 11. The relation between years of experience and DT effectiveness rating.
Heritage 2024, 7, FOR PEER REVIEW 14
lower ratings as experience increases, with more ratings clustering around two and occa-
sional ones (not effective) and fours appearing towards the tail end of the experience spec-
trum.
Figure 12. The effectiveness ratings given by various participants for three different technologies:
laser scanning (blue), machine learning (green), and BIM (red), in the context of heritage conserva-
tion.
According to Figure 12, experts generally view both laser scanning and BIM as help-
ful technologies in the conservation of heritage buildings, as indicated by the high ratings
clustered towards the top of the scale. Laser scanning is perceived as particularly benefi-
cial, likely due to its ability to capture detailed and accurate representations of existing
structures, which is crucial for any restoration or conservation work. Precise 3D models
created through laser scanning are invaluable for architects and conservators in under-
standing the condition of heritage buildings and planning appropriate interventions. BIM
also receives high effectiveness ratings, reflecting its comprehensive approach to model-
ing and managing building information. Its utility for heritage buildings may stem from
its capacity to centralize and streamline data, which aids in the planning and execution
required for conservation projects. However, the occasional dips in ratings for both tech-
nologies suggest some concerns among experts. These could be due to the complexity of
integrating these high-tech tools into traditional conservation processes, the need for spe-
cialized training, or the high costs associated with their implementation.
Machine learning exhibits the most significant variability in effectiveness ratings, in-
dicating mixed opinions among experts. High ratings may be aributed to the potential
of machine learning to analyze large datasets, predict structural weaknesses, and optimize
maintenance schedules. Nevertheless, the lower ratings and variability suggest skepticism
or concern, which could be due to several factors such as the current immaturity of AI
applications in heritage contexts, a lack of reliable data to train algorithms, or challenges
in interpreting machine learning outputs in a way that is meaningful for heritage conser-
vation. The concerns reflected in the variability of ratings highlight the necessity for on-
going research and development, beer integration of these technologies into conserva-
tion workflows, and the importance of addressing any potential barriers to their effective
use, such as ensuring data quality, refining AI models for the specific nuances of heritage
buildings, and fostering a beer understanding of these tools among conservation profes-
sionals.
Regarding the open-ended questions, experts utilizing laser scanning for heritage
conservation projects often highlight its precision and ability to capture detailed measure-
ments of complex structures as key benefits. This technology allows for the creation of
accurate 3D models of heritage sites, enabling conservators to analyze the architectural
features and structural integrity without physically interacting with the fragile structures,
thus minimizing the risk of damage. However, challenges include the high cost of equip-
ment and the need for specialized training to interpret the data effectively. Additionally,
Figure 12. The effectiveness ratings given by various participants for three different technologies:
laser scanning (blue), machine learning (green), and BIM (red), in the context of heritage conservation.
Interestingly, there are instances of high ratings [
5
] even among participants with
6–15 years
of experience, indicating that some mid-career professionals still find significant
value in DT applications. However, the general trend still shows a movement towards lower
ratings as experience increases, with more ratings clustering around two and occasional
ones (not effective) and fours appearing towards the tail end of the experience spectrum.
According to Figure 12, experts generally view both laser scanning and BIM as helpful
technologies in the conservation of heritage buildings, as indicated by the high ratings
clustered towards the top of the scale. Laser scanning is perceived as particularly beneficial,
likely due to its ability to capture detailed and accurate representations of existing structures,
which is crucial for any restoration or conservation work. Precise 3D models created
through laser scanning are invaluable for architects and conservators in understanding
the condition of heritage buildings and planning appropriate interventions. BIM also
receives high effectiveness ratings, reflecting its comprehensive approach to modeling and
managing building information. Its utility for heritage buildings may stem from its capacity
to centralize and streamline data, which aids in the planning and execution required for
conservation projects. However, the occasional dips in ratings for both technologies suggest
some concerns among experts. These could be due to the complexity of integrating these
high-tech tools into traditional conservation processes, the need for specialized training, or
the high costs associated with their implementation.

Heritage 2024,76445
Machine learning exhibits the most significant variability in effectiveness ratings, indi-
cating mixed opinions among experts. High ratings may be attributed to the potential of
machine learning to analyze large datasets, predict structural weaknesses, and optimize
maintenance schedules. Nevertheless, the lower ratings and variability suggest skepticism
or concern, which could be due to several factors such as the current immaturity of AI
applications in heritage contexts, a lack of reliable data to train algorithms, or challenges in
interpreting machine learning outputs in a way that is meaningful for heritage conserva-
tion. The concerns reflected in the variability of ratings highlight the necessity for ongoing
research and development, better integration of these technologies into conservation work-
flows, and the importance of addressing any potential barriers to their effective use, such
as ensuring data quality, refining AI models for the specific nuances of heritage buildings,
and fostering a better understanding of these tools among conservation professionals.
Regarding the open-ended questions, experts utilizing laser scanning for heritage con-
servation projects often highlight its precision and ability to capture detailed measurements
of complex structures as key benefits. This technology allows for the creation of accurate
3D models of heritage sites, enabling conservators to analyze the architectural features and
structural integrity without physically interacting with the fragile structures, thus minimiz-
ing the risk of damage. However, challenges include the high cost of equipment and the
need for specialized training to interpret the data effectively. Additionally, managing the
massive volumes of data generated and ensuring its long-term storage and accessibility
remain significant concerns.
In the context of machine learning’s contribution to heritage building conservation,
professionals discuss its role in analyzing vast datasets derived from various sources, such
as laser scans, photographs, and environmental sensors. Machine learning algorithms can
identify patterns and anomalies that would be impossible for humans to detect manually,
such as predicting areas at risk of deterioration. Examples include the use of machine
learning to monitor changes over time in the structural health of buildings or to analyze his-
torical climate data to predict environmental conditions that could affect heritage sites. The
integration of machine learning accelerates decision-making processes and enhances pre-
dictive maintenance strategies, though it requires substantial datasets and interdisciplinary
collaboration to train effective models.
Regarding BIM’s impact on heritage conservation, experts note how BIM facilitates
a more integrated and holistic approach to the management and preservation of heritage
buildings. BIM enables stakeholders to collaborate more effectively and make informed
decisions by creating detailed digital representations that include geometric dimensions
and historical, material, and condition-related information. This has transformed the
conservation process, making it more dynamic and responsive to the needs of heritage
buildings. Challenges include the adaptation of BIM tools to accommodate the unique
aspects of heritage structures and the need for extensive documentation to populate BIM
models with accurate data.
Furthermore, when discussing the potential of DTs in heritage conservation, experts
envision a future where digital replicas of heritage sites are used for various purposes,
including virtual tourism, disaster risk assessment, and the monitoring of structural health
in real-time. DTs can offer immersive experiences that allow for the exploration of heritage
sites from anywhere in the world, potentially democratizing access to cultural heritage
and generating public interest and support for conservation efforts. They also hold the
promise of enabling more proactive conservation strategies by simulating the impact of
environmental changes and human activities on heritage sites. The challenges lie in creating
accurate and comprehensive DTs that are updated in real-time and ensuring that the
technology is accessible and beneficial to all stakeholders involved in heritage conservation.
Figure 13 illustrates the frequency of reported benefits and challenges associated with
four prominent technologies—laser scanning, machine learning, BIM, and digital twins
(DTs)—in heritage conservation. This bar chart highlights the significant advantages each
technology offers, such as increased precision and enhanced predictive analytics, as well

Heritage 2024,76446
as key obstacles, including financial demands and data management complexities. By
visualizing expert insights, this figure underscores the multifaceted impact of emerging
technologies and the areas where additional support and refinement are needed to optimize
their application in heritage conservation.
Heritage 2024, 7, FOR PEER REVIEW 16
Figure 13. Visualization of the frequency of benefits and challenges associated with four key tech-
nologies—laser scanning, machine learning, building information modeling (BIM), and digital
twins (DTs)—in heritage conservation, based on expert insights.
3.3. Emerging Technology Synthesis for Heritage Conservation
The integration of emerging technologies into heritage conservation represents a piv-
otal shift towards more informed, precise, and sustainable preservation practices. This
section explores the convergence of DTs, laser scanning, the IoT, machine learning, and
BIM within the context of safeguarding cultural heritage. This section explores how these
technologies, through a synergistic application, offer the potential to revolutionize the
field by enhancing documentation accuracy, enabling real-time monitoring, facilitating
predictive maintenance, and ensuring the participatory involvement of stakeholders.
3.3.1. Laser Scanning and DT Symbiosis
The convergence of laser scanning and DTs represents a frontier in heritage conser-
vation, offering a digital symbiosis that could revolutionize how we preserve historical
structures [31]. This fusion enables an unparalleled level of detail and accuracy in model-
ing heritage sites, ensuring that every nuance is captured and maintained for future gen-
erations [32]. However, the synergy between these technologies also presents unique chal-
lenges and research gaps that necessitate careful investigation and innovative solutions to
fully harness their combined potential [33]. Table 4 provides an overview of the existing
research gaps and outlines critical areas where further development and standardization
are necessary to enhance the effectiveness of technology in preserving heritage buildings.
Table 4 documents specific research gaps, focusing on areas like standardized protocols
for data integration, hybrid data processing, and automation in HBIM processes. Address-
ing these gaps could streamline technological integration, fostering a more cohesive and
efficient conservation workflow.
Figure 13. Visualization of the frequency of benefits and challenges associated with four key
technologies—laser scanning, machine learning, building information modeling (BIM), and dig-
ital twins (DTs)—in heritage conservation, based on expert insights.
Figure 13 results from the thematic analysis of the open-ended questions and responses
from experts in the field of heritage conservation, showcasing a detailed exploration into
the integration of advanced technologies. Through this analysis, the figure presents a series
of bar charts that illustrate the frequency of both benefits and challenges as highlighted
across four key technological areas: laser scanning, machine learning, building informa-
tion modeling (BIM), and digital twins (DTs). This visualization shows the significant
advantages such technologies offer (like increased precision, enhanced predictive analytics,
and improved collaborative processes) and brings to light the various obstacles encoun-
tered, including financial burdens, complexities in data management, and the need for
specialized expertise.
3.3. Emerging Technology Synthesis for Heritage Conservation
The integration of emerging technologies into heritage conservation represents a
pivotal shift towards more informed, precise, and sustainable preservation practices. This
section explores the convergence of DTs, laser scanning, the IoT, machine learning, and
BIM within the context of safeguarding cultural heritage. This section explores how these
technologies, through a synergistic application, offer the potential to revolutionize the
field by enhancing documentation accuracy, enabling real-time monitoring, facilitating
predictive maintenance, and ensuring the participatory involvement of stakeholders.

Heritage 2024,76447
3.3.1. Laser Scanning and DT Symbiosis
The convergence of laser scanning and DTs represents a frontier in heritage conser-
vation, offering a digital symbiosis that could revolutionize how we preserve historical
structures [
31
]. This fusion enables an unparalleled level of detail and accuracy in mod-
eling heritage sites, ensuring that every nuance is captured and maintained for future
generations [
32
]. However, the synergy between these technologies also presents unique
challenges and research gaps that necessitate careful investigation and innovative solutions
to fully harness their combined potential [
33
]. Table 4provides an overview of the existing
research gaps and outlines critical areas where further development and standardization
are necessary to enhance the effectiveness of technology in preserving heritage buildings.
Table 4documents specific research gaps, focusing on areas like standardized protocols
for data integration, hybrid data processing, and automation in HBIM processes. Address-
ing these gaps could streamline technological integration, fostering a more cohesive and
efficient conservation workflow.
Table 4. Identified research gaps in enhancing laser scanning and digital twin integration for heritage
conservation.
Research Gap Description References
Information Standardization
The lack of standardized information protocols for integrating laser
scanning data with DTs. [34,35]
Detailed Finite Element Modeling The need for detailed finite element models that accurately
represent the structural aspects of heritage buildings from laser
scanning data. [25]
Integration with 360◦Photography Challenges in enriching DTs with 360◦photography to capture the
essence and details of heritage buildings. [12]
Automation in HBIM Processes The necessity for automated processes in Heritage Building
Information Modeling (HBIM) to streamline data conversion and
management. [18]
Hybrid Processing of Laser Scanning Data The development of unified technologies for processing combined
laser scanning and photography data for historical buildings. [36]
3.3.2. Information Standardization
The adoption of DT technology in the construction sector, especially for heritage
buildings, is significantly impeded by the absence of uniform information protocols [
26
].
Heritage buildings, with their unique historical contexts and conservation needs, require
meticulously detailed data for their DTs to function effectively [
29
]. However, the current
landscape of data management lacks a cohesive framework, leading to disparate data
formats and standards that come from different types of laser scanners as well as sensors.
This inconsistency complicates the integration of physical and digital spaces, as DTs rely on
precise, real-time data to mirror and predict the physical state of a building accurately [
37
].
Furthermore, the management of heritage buildings involves various stakeholders,
including conservators, architects, and engineers, each contributing different types of
data [
38
]. The absence of standardized information protocols makes it challenging to
aggregate and synchronize these data effectively, thereby limiting the potential of DTs
to facilitate predictive maintenance, energy efficiency optimization, and structural health
monitoring [
39
]. As a result, the gap between the real and digital representations of heritage
buildings widens, undermining the utility of DTs in preserving and enhancing the value of
historical structures [40].
Addressing this challenge necessitates a concerted effort to develop and implement
industry-wide data standards that cater to the unique requirements of heritage building
management [
41
]. Such standards must encompass the technical aspects of data formats,
interoperability, and the understanding of historical significance and conservation prin-
ciples that are crucial for heritage buildings [
42
]. Only through such standardization can

Heritage 2024,76448
the construction sector fully harness the power of DTs, ensuring that these technological
marvels serve as effective tools in the stewardship of architectural heritage [43].
In Figure 14, the paper proposes a conceptual framework for integrating various data
formats (including laser scanning data) and stakeholder inputs into a unified digital repre-
sentation. The framework employs a knowledge graph, which standardizes data sources
and promotes interoperability among stakeholders, allowing heritage buildings’ complex
data to be unified into a cohesive digital representation. This integrated approach facilitates
efficient data handling and enhances the precision of digital twins (DTs) in reflecting the
current condition of heritage buildings. The process begins with stakeholders, such as
conservators, architects, and engineers, who provide data that is formatted appropriately.
These data, alongside the articulated conservation needs, feed into the development of
standardization protocols using what is called a knowledge graph [
44
]. These protocols
are essential for ensuring that the data from diverse sources and formats can be utilized
cohesively. The standardized data then informs the creation of interoperability frameworks,
which allow for the different data types and systems to work together effectively. The ulti-
mate goal of this workflow is to culminate in a unified digital representation, presumably
of a building or a set of buildings, which integrates all stakeholder inputs and conservation
needs into a single cohesive model.
Heritage 2024, 7, FOR PEER REVIEW 18
presumably of a building or a set of buildings, which integrates all stakeholder inputs and
conservation needs into a single cohesive model.
Figure 14. Proposed workflow for integrating stakeholder inputs into a unified digital representa-
tion for conservation.
To clarify more, the knowledge graph serves as a sophisticated framework for inte-
grating various types of data, including that from laser scanning, into a comprehensive
digital representation [45]. As a specific example, a knowledge graph that incorporates the
CIDOC (the International Commiee for Documentation Conceptual Reference Model,
which is an ontological framework designed to facilitate the integration, mediation, and
interchange of heterogeneous cultural heritage information) and Conceptual Reference
Model (CRM) protocol can be considered [46]. CIDOC CRM is designed to enable infor-
mation integration for cultural heritage data and provides a standardized way to describe
the implicit and explicit concepts and relationships used in cultural heritage documenta-
tion [47].
Laser scanning data, which offers high-fidelity three-dimensional representations of
heritage sites, can be encoded within a knowledge graph using CIDOC CRM to create
nodes that represent the physical structure of a site [48]. These nodes could be linked to
other nodes representing historical information, conservation requirements, and stake-
holder contributions [49]. This approach facilitates the construction of a comprehensive
knowledge graph that aggregates all essential documentation from numerous laser-
scanned sites into a singular, cohesive repository [50].
3.3.3. Detailed Finite Element Modeling
The digitalization of historic masonry buildings into accurate 3D finite element mod-
els (FEM) represents a significant challenge due to their unique architectural features and
the heterogeneity of their materials [25]. Traditional methods often fall short in capturing
the complex geometries and material inconsistencies inherent in these structures [51]. This
gap is notably bridged by the adoption of a parametric Scan-to-FEM approach (Figure 15),
which leverages advanced scanning technologies to map the intricate details of masonry
buildings. Figure 15 demonstrates the application of the parametric Scan-to-FEM ap-
proach in preserving heritage buildings, using the case study of St. Torcato Church. The
figure showcases the intricate digital capture of architectural details through point cloud
views, followed by a color-coded finite element model (FEM) that visualizes structural
analysis outcomes. This model serves as a valuable resource for identifying structural vul-
nerabilities and assessing potential restoration interventions, a critical step in the preven-
tive conservation of historic masonry buildings. This technique allows for the transfor-
mation of detailed scans into parametric models that can be directly used for structural
analysis and simulation purposes.
Despite its advantages, the current application of the parametric Scan-to-FEM ap-
proach reveals a critical gap in its capability to efficiently process and convert the exten-
sive data collected from scans into models that accurately reflect the structural behavior
of historic buildings [25]. This process often requires significant manual intervention to
correct inaccuracies and to input material properties that are not directly discernible from
scans [52]. Furthermore, the method’s effectiveness in predicting future scenarios and
Figure 14. Proposed workflow for integrating stakeholder inputs into a unified digital representation
for conservation.
To clarify more, the knowledge graph serves as a sophisticated framework for integrat-
ing various types of data, including that from laser scanning, into a comprehensive digital
representation [
45
]. As a specific example, a knowledge graph that incorporates the CIDOC
(the International Committee for Documentation Conceptual Reference Model, which is an
ontological framework designed to facilitate the integration, mediation, and interchange
of heterogeneous cultural heritage information) and Conceptual Reference Model (CRM)
protocol can be considered [
46
]. CIDOC CRM is designed to enable information integration
for cultural heritage data and provides a standardized way to describe the implicit and
explicit concepts and relationships used in cultural heritage documentation [47].
Laser scanning data, which offers high-fidelity three-dimensional representations of
heritage sites, can be encoded within a knowledge graph using CIDOC CRM to create nodes
that represent the physical structure of a site [
48
]. These nodes could be linked to other
nodes representing historical information, conservation requirements, and stakeholder
contributions [
49
]. This approach facilitates the construction of a comprehensive knowledge
graph that aggregates all essential documentation from numerous laser-scanned sites into
a singular, cohesive repository [50].
3.3.3. Detailed Finite Element Modeling
The digitalization of historic masonry buildings into accurate 3D finite element models
(FEM) represents a significant challenge due to their unique architectural features and the
heterogeneity of their materials [
25
]. Traditional methods often fall short in capturing the
complex geometries and material inconsistencies inherent in these structures [
51
]. This
gap is notably bridged by the adoption of a parametric Scan-to-FEM approach (Figure 15),
which leverages advanced scanning technologies to map the intricate details of masonry

Heritage 2024,76449
buildings. Figure 15 demonstrates the application of the parametric Scan-to-FEM approach
in preserving heritage buildings, using the case study of St. Torcato Church. The figure
showcases the intricate digital capture of architectural details through point cloud views,
followed by a color-coded finite element model (FEM) that visualizes structural analysis
outcomes. This model serves as a valuable resource for identifying structural vulnera-
bilities and assessing potential restoration interventions, a critical step in the preventive
conservation of historic masonry buildings. This technique allows for the transformation
of detailed scans into parametric models that can be directly used for structural analysis
and simulation purposes.
Heritage 2024, 7, FOR PEER REVIEW 19
assessing the potential impact of structural interventions remains limited by its current
reliance on generalized assumptions about material characteristics and behavior [53].
Figure 15. Integrated digital assessment of St. Torcato Church. (a) Presents two-point cloud views
of St. Torcato Church, illustrating the detailed digital capture of the architectural features. (b) Dis-
plays finite element modeling (FEM) of the church, color-coded to indicate structural analysis out-
comes across multiple viewpoints. The figure is reproduced from [25].
Addressing this gap necessitates further research and development towards more
sophisticated algorithms that can automate the identification and classification of materi-
als and structural damages from scan data [54]. Additionally, the integration of machine
learning techniques could enhance the model’s predictive accuracy by enabling it to learn
from a database of documented interventions and their outcomes [55]. Ultimately, ad-
vancing the parametric Scan-to-FEM approach promises to significantly improve the
preservation and restoration efforts of historic masonry buildings by providing a more
reliable foundation for decision-making regarding structural interventions [25].
3.3.4. Integration with 360° Photography
The fusion of laser scanning and 360° photography stands out as a pivotal advance-
ment in the field of digital preservation, particularly for heritage buildings [12]. Figure 16
highlights various advanced imaging tools employed in heritage conservation, including
terrestrial laser scanners, aerial scanning devices, and 360° photography systems. These
tools offer a comprehensive suite of data collection capabilities, enabling conservation
teams to capture high-resolution 3D models alongside panoramic photographic detail. By
combining precise geometric data with rich visual context, these technologies significantly
enhance the accuracy and effectiveness of digital conservation models. While laser scan-
ning offers a robust foundation for capturing the geometric intricacies of structures, the
incorporation of 360° photography (Figure 16) is essential for adding a layer of visual and
textural detail that is often missed by traditional scanning techniques. This combination
results in enriched digital models that facilitate a comprehensive understanding of the
physical condition of heritage sites and significantly support the development of preven-
tive conservation plans.
Figure 15. Integrated digital assessment of St. Torcato Church. (a) Presents two-point cloud views of
St. Torcato Church, illustrating the detailed digital capture of the architectural features. (b) Displays
finite element modeling (FEM) of the church, color-coded to indicate structural analysis outcomes
across multiple viewpoints. The figure is reproduced from [25].
Despite its advantages, the current application of the parametric Scan-to-FEM ap-
proach reveals a critical gap in its capability to efficiently process and convert the extensive
data collected from scans into models that accurately reflect the structural behavior of
historic buildings [
25
]. This process often requires significant manual intervention to cor-
rect inaccuracies and to input material properties that are not directly discernible from
scans [
52
]. Furthermore, the method’s effectiveness in predicting future scenarios and
assessing the potential impact of structural interventions remains limited by its current
reliance on generalized assumptions about material characteristics and behavior [53].
Addressing this gap necessitates further research and development towards more
sophisticated algorithms that can automate the identification and classification of materials
and structural damages from scan data [
54
]. Additionally, the integration of machine learn-
ing techniques could enhance the model’s predictive accuracy by enabling it to learn from a
database of documented interventions and their outcomes [
55
]. Ultimately, advancing the
parametric Scan-to-FEM approach promises to significantly improve the preservation and
restoration efforts of historic masonry buildings by providing a more reliable foundation
for decision-making regarding structural interventions [25].

Heritage 2024,76450
3.3.4. Integration with 360◦Photography
The fusion of laser scanning and 360
◦
photography stands out as a pivotal advance-
ment in the field of digital preservation, particularly for heritage buildings [12]. Figure 16
highlights various advanced imaging tools employed in heritage conservation, including
terrestrial laser scanners, aerial scanning devices, and 360
◦
photography systems. These
tools offer a comprehensive suite of data collection capabilities, enabling conservation
teams to capture high-resolution 3D models alongside panoramic photographic detail. By
combining precise geometric data with rich visual context, these technologies significantly
enhance the accuracy and effectiveness of digital conservation models. While laser scan-
ning offers a robust foundation for capturing the geometric intricacies of structures, the
incorporation of 360
◦
photography (Figure 16) is essential for adding a layer of visual and
textural detail that is often missed by traditional scanning techniques. This combination
results in enriched digital models that facilitate a comprehensive understanding of the
physical condition of heritage sites and significantly support the development of preventive
conservation plans.
Heritage 2024, 7, FOR PEER REVIEW 20
Figure 16. Advanced imaging equipment for digital heritage conservation. Top row showcases laser
scanning systems: (a) a FARO Focus terrestrial laser scanner for high-precision 3D modeling; and
(b) an unmanned aerial vehicle (UAV) equipped for aerial scanning, providing expansive site cov-
erage. Top and boom rows illustrate portable mobile mapping systems: (c) a handheld device for
agile and flexible data collection; and (d) a trolley system for steady and systematic scanning in
larger areas. The final set (e) presents panoramic photography systems: on the left, a dual fisheye
lens sensor device for 360-degree imaging, and on the right, a DSLR camera with a fisheye lens for
wide-angle shots. Based on [12].
While there have historically been challenges in merging geometric data with high-
resolution photographic images [56]. Recent publications indicate that effective methods
for integrating laser scanner data with photographic input now exist [57]. Therefore, it is
important to acknowledge these developments and recognize that the integration process
is evolving with the availability of new techniques and technologies [12]. However, this
challenge stems from the difficulty in aligning detailed photographic textures with their
corresponding 3D models, a process that requires precision to ensure the accurate repre-
sentation of the building’s features [58]. Moreover, there is a critical need for advanced
tools that can handle the vast amounts of data generated from both scanning and photog-
raphy without compromising on detail or accuracy [59].
To address these issues, further innovation is needed to develop more efficient algo-
rithms for data fusion that can automatically align and integrate 360° photographs with
laser scans [60]. Additionally, the exploration of new data compression techniques could
facilitate the handling and storage of enriched models, making them more accessible for
stakeholders involved in the conservation process [61]. Enhancing the integration of laser
scanning and 360° photography will undoubtedly elevate the quality of DTs for heritage
buildings, offering a more dynamic and informative tool for preventive conservation ef-
forts [62].
3.3.5. Automation in HBIM Processes
The development of Heritage Building Information Modeling (HBIM) platforms is
notably labor-intensive, particularly during the initial stages of converting raw data into
functional models [63]. This complexity underscores the pressing need for automation
within HBIM workflows to streamline the creation and management of digital represen-
tations of heritage buildings [64]. Artificial intelligence (AI) emerges as a potent tool in
Figure 16. Advanced imaging equipment for digital heritage conservation. Top row showcases laser
scanning systems: (a) a FARO Focus terrestrial laser scanner for high-precision 3D modeling; and
(b) an unmanned aerial vehicle (UAV) equipped for aerial scanning, providing expansive site coverage.
Top and bottom rows illustrate portable mobile mapping systems: (c) a handheld device for agile and
flexible data collection; and (d) a trolley system for steady and systematic scanning in larger areas.
The final set (e) presents panoramic photography systems: on the left, a dual fisheye lens sensor
device for 360-degree imaging, and on the right, a DSLR camera with a fisheye lens for wide-angle
shots. Based on [12].
While there have historically been challenges in merging geometric data with high-
resolution photographic images [
56
]. Recent publications indicate that effective methods
for integrating laser scanner data with photographic input now exist [
57
]. Therefore, it is
important to acknowledge these developments and recognize that the integration process
is evolving with the availability of new techniques and technologies [
12
]. However, this
challenge stems from the difficulty in aligning detailed photographic textures with their
corresponding 3D models, a process that requires precision to ensure the accurate represen-
tation of the building’s features [
58
]. Moreover, there is a critical need for advanced tools
that can handle the vast amounts of data generated from both scanning and photography
without compromising on detail or accuracy [59].

Heritage 2024,76451
To address these issues, further innovation is needed to develop more efficient al-
gorithms for data fusion that can automatically align and integrate 360
◦
photographs
with laser scans [
60
]. Additionally, the exploration of new data compression techniques
could facilitate the handling and storage of enriched models, making them more accessible
for stakeholders involved in the conservation process [
61
]. Enhancing the integration of
laser scanning and 360
◦
photography will undoubtedly elevate the quality of DTs for her-
itage buildings, offering a more dynamic and informative tool for preventive conservation
efforts [62].
3.3.5. Automation in HBIM Processes
The development of Heritage Building Information Modeling (HBIM) platforms is
notably labor-intensive, particularly during the initial stages of converting raw data into
functional models [
63
]. This complexity underscores the pressing need for automation
within HBIM workflows to streamline the creation and management of digital representa-
tions of heritage buildings [
64
]. Artificial intelligence (AI) emerges as a potent tool in this
domain, offering promising avenues for automating tasks such as data processing, feature
extraction, and even the interpretation of architectural elements [65].
A significant gap in current HBIM processes is the manual effort required to interpret
and model the data obtained from laser scanning [
66
]. This gap can be effectively addressed
by integrating AI with laser scanning technologies [
67
]. While AI algorithms can be
trained to recognize and classify architectural features automatically from point cloud
data, significantly reducing the time and effort involved in manual data processing, it is
important to note that this approach is still not fully effective due to the inherent complexity
of certain architectural features. The variability in design, detail, and historical context can
pose challenges for accurate recognition and classification. However, as advancements in
AI technology continue and as more robust training datasets are developed, these methods
are likely to become increasingly effective in the future, ultimately providing extraordinary
support for the digitization of cultural heritage [
68
]. Moreover, the use of AI can enhance
the accuracy of the models by minimizing human errors in the interpretation of complex
geometries [69].
Incorporating DTs into the HBIM framework represents another frontier for automa-
tion [
70
]. DTs can offer dynamic, up-to-date models of heritage buildings through providing
real-time data from laser scans and other monitoring technologies [
71
]. This integration
facilitates the maintenance and preservation of heritage sites by providing detailed insights
into their current state and enables the simulation of potential conservation interven-
tions [
72
]. The predictive capabilities of DTs, powered by AI, can transform how heritage
buildings are managed, allowing for proactive rather than reactive conservation strate-
gies [
62
]. However, the integration of AI, laser scanning, and DTs within HBIM poses
challenges, including the need for substantial computational resources and the develop-
ment of specialized AI models that can understand and interpret the unique characteristics
of heritage buildings [
73
]. Overcoming these challenges requires focused research on devel-
oping lightweight, efficient algorithms and harnessing cloud computing technologies to
process and store the vast amounts of data generated by these processes [
74
]. Advancing
automation in HBIM through AI, laser scanning, and DTs holds the key to more sustainable
and effective preservation of cultural heritage [
29
]. Figure 17 outlines an AI-powered HBIM
workflow designed to streamline the creation of digital twins for heritage buildings. The
process begins with raw data acquisition through laser scanning, followed by AI-driven
processing for feature extraction, which enhances the efficiency and accuracy of HBIM
models. Integrating these models with real-time updates allows heritage managers to adopt
proactive conservation strategies and plan interventions based on real-time data insights.

Heritage 2024,76452
Heritage 2024, 7, FOR PEER REVIEW 22
Figure 17. Proposed workflow for integrating AI with HBIM in the context of creating DTs for her-
itage buildings.
3.4. BIM and DT as a Heritage Conservation Nexus
The fusion of BIM and DTs forms a progressive nexus for the conservation of heritage
buildings, offering a holistic framework to capture, manage, and utilize detailed data
throughout a building’s life cycle. This synthesis is at the heart of this section. Here, the
paper explores the gaps shown in Table 5, which stands as a testament to the potential of
integrating high-resolution data capture and analysis with dynamic, interactive models
for comprehensive building management. Table 5 presents a structured overview of the
research gaps in integrating building information modeling (BIM) and digital twin (DT)
technologies for heritage conservation. Each entry highlights challenges, such as the need
for seamless workflows from Scan-to-BIM-to-DT and the inclusion of cultural significance
in HBIM models. These research gaps underscore the complexities in adapting BIM and
DT technologies to meet the unique demands of heritage conservation, emphasizing areas
where further innovation is needed.
Tab l e 5 . Overview of the research gaps areas in the nexus of building information modeling and
DTs in the context of heritage building conservation.
Research Gap Description References
Scan-to-BIM-to-DT Process
The challenge of managing high levels of detail through the design, construc-
tion, and management phases, with a need for a process that allows users to
interact with DT for improved building comfort and efficiency.
[27]
DT-HBIM for Preventive Conservation Proposing a methodology to integrate cultural significance into HBIM models
to support preventive conservation using DT principles. [76]
3.4.1. Scan-to-BIM-to-DT Process
The Scan-to-BIM-to-DT process represents a transformative approach to managing
heritage buildings by providing high levels of detail and information across all phases,
from design through to construction and maintenance [27,77]. This method allows for en-
hanced interaction with the DT, facilitating improved building comfort, efficiency, and
Figure 17. Proposed workflow for integrating AI with HBIM in the context of creating DTs for
heritage buildings.
The workflow diagram in Figure 17 proposes an AI-driven solution to enhance HBIM
by starting with the acquisition of raw data through laser scanning. These data undergo
AI-powered processing and feature extraction, enhancing the efficiency of creating HBIM
models while maintaining the inherent accuracy of laser scanner raw data. The AI algo-
rithms facilitate the interpretation of the existing data rather than improving its accuracy,
streamlining the process of transforming detailed point cloud information into compre-
hensive HBIM models. The enhanced models facilitate the seamless integration of DTs,
enabling real-time updates and predictions [
26
]. This integration underpins the devel-
opment of proactive conservation strategies, addressing the challenges of computational
resource demands and specialized AI model development [
75
]. Focused research is recom-
mended to overcome these challenges, with solutions such as lightweight algorithms and
cloud computing paving the way for advanced automation in HBIM [67].
3.4. BIM and DT as a Heritage Conservation Nexus
The fusion of BIM and DTs forms a progressive nexus for the conservation of heritage
buildings, offering a holistic framework to capture, manage, and utilize detailed data
throughout a building’s life cycle. This synthesis is at the heart of this section. Here, the
paper explores the gaps shown in Table 5, which stands as a testament to the potential of
integrating high-resolution data capture and analysis with dynamic, interactive models
for comprehensive building management. Table 5presents a structured overview of the
research gaps in integrating building information modeling (BIM) and digital twin (DT)
technologies for heritage conservation. Each entry highlights challenges, such as the need
for seamless workflows from Scan-to-BIM-to-DT and the inclusion of cultural significance
in HBIM models. These research gaps underscore the complexities in adapting BIM and
DT technologies to meet the unique demands of heritage conservation, emphasizing areas
where further innovation is needed.

Heritage 2024,76453
Table 5. Overview of the research gaps areas in the nexus of building information modeling and DTs
in the context of heritage building conservation.
Research Gap Description References
Scan-to-BIM-to-DT Process
The challenge of managing high levels of detail through the design,
construction, and management phases, with a need for a process
that allows users to interact with DT for improved building comfort
and efficiency.
[27]
DT-HBIM for Preventive Conservation Proposing a methodology to integrate cultural significance into
HBIM models to support preventive conservation using DT
principles. [76]
3.4.1. Scan-to-BIM-to-DT Process
The Scan-to-BIM-to-DT process represents a transformative approach to managing
heritage buildings by providing high levels of detail and information across all phases,
from design through to construction and maintenance [
27
,
77
]. This method allows for
enhanced interaction with the DT, facilitating improved building comfort, efficiency, and
cost management [
78
]. Despite its potential, adapting these sophisticated technologies to
heritage buildings introduces complexities not typically encountered in modern construc-
tion projects [
79
]. The challenge lies in ensuring that such advanced methodologies respect
and preserve the historical accuracy and integrity of heritage structures [
80
]. This gap
between technological potential and practical application in heritage conservation points
to a critical area for further research and development [81].
In the field of construction management, the introduction of a DT-BIM hybrid model
using artificial intelligence marks a significant advancement in addressing resource short-
ages and optimizing decision-making processes [
82
]. This hybrid model has the potential to
revolutionize the management of construction projects, including those involving heritage
buildings, by offering a more streamlined and efficient approach to resource allocation
and project dispatch [
83
]. However, the unique characteristics of heritage buildings, which
often require the use of traditional materials and methods, pose a significant challenge to
the direct application of such a model [
84
]. The specific needs and constraints of heritage
building conservation have yet to be fully addressed by this model, highlighting a notable
gap in the field that demands attention [85].
Furthermore, the application of DTs for the life cycle assessment of infrastructure
projects, as advocated in sustainability practices, presents an exciting opportunity for the
conservation of heritage buildings [
86
]. DTs, seen as an advanced form of BIM, can play a
crucial role in sustainability and vulnerability audits, potentially transforming practices
in heritage building conservation [
87
]. Nonetheless, this approach necessitates a careful
balance between embracing technological advancements and preserving the historical and
cultural value of heritage structures [
88
]. The integration of DTs into heritage conservation
practices requires a good understanding of both the technological and historical aspects,
pointing to a substantial area for exploration and application [89].
The integration of BIM, the IoT, and DTs, with potential forays into the metaverse,
identifies a promising research direction with implications for heritage building conserva-
tion [
90
]. The challenge lies in how these integrations can be applied to the conservation
of heritage buildings, where documentation may be incomplete or non-standardized and
construction techniques differ significantly from modern methods [
91
]. This situation calls
for innovative approaches to document and manage heritage buildings effectively, using
these technologies while addressing the gap between current capabilities and the needs of
heritage conservation [92].
OpenBIM’s approach to supporting dynamic asset management through real-time
data integration offers insights into how DT and IoT can be better integrated within the
context of heritage building management [
93
]. The potential of OpenBIM to enhance the
management and preservation of heritage buildings is significant, yet the application of
such technologies in contexts where as-built information is scarce or non-standard presents

Heritage 2024,76454
distinct challenges [
94
]. These challenges include the need for specialized methodologies
to capture and integrate historical and architectural nuances into DT models, suggesting a
pressing need for targeted research and development efforts [95].
Furthermore, the concept of data-driven construction, characterized by the use of DT
information systems for closed-loop control, introduces a novel approach to construction
management [
96
]. This approach holds considerable promise for enhancing the conserva-
tion and management of heritage buildings. However, defining the specific dimensions of
information necessary for effective DT workflows in heritage conservation is an area that
remains underexplored [
97
]. Addressing this gap requires a concerted effort to develop
frameworks and methodologies that can accommodate the unique attributes of heritage
buildings, thereby advancing the field towards more integrated and effective conservation
practices [98].
Figure 18 presents a sophisticated digital workflow that can enhance the efficiency of
heritage buildings inspired by [
27
]. The workflow begins with BIM ADD-IN, a tool that
initiates the reconstruction process, seamlessly integrating with BIM MODELGENERATIVE
techniques to accurately replicate the historic structures [
27
]. This is coupled with the
INDOOR 4DBIM TOOL, providing the Forge API to provide detailed indoor environment
modeling. Data flows into the HOMEBIM Live APP, which then synergizes with the
HOMEBIM Cloud Platform, establishing a robust ecosystem for managing and analyzing
building information.
Heritage 2024, 7, FOR PEER REVIEW 24
the INDOOR 4DBIM TOOL, providing the Forge API to provide detailed indoor environ-
ment modeling. Data flows into the HOMEBIM Live APP, which then synergizes with the
HOMEBIM Cloud Platform, establishing a robust ecosystem for managing and analyzing
building information.
Figure 18. Digital workflow proposed for heritage buildings based on [27].
Central to this operation are the BIM PLATFORM ONLINE and COMFORT PLAT-
FORM ONLINE, which together orchestrate a comprehensive strategy for assessing and
improving thermal comfort, crucial for the preservation and sustainable use of heritage
buildings. Sensors meticulously capture on-site indoor and outdoor conditions, feeding
data back to the HOMEBIM platform, where it is processed and analyzed. This interplay
of data from various sources, including consumption metrics and costs, is pivotal in adapt-
ing retrofiing strategies for heritage buildings [27]. It informs decision-making processes,
ensuring that interventions respect architectural integrity while promoting energy effi-
ciency [27]. This digital workflow, as depicted in Figure 18, provides a blueprint for the
application of advanced technologies in the conservation of historic structures, aiming to
reduce energy demand and CO
2
emissions in line with Italy’s energy efficiency directives,
thus fostering sustainable heritage building management. Figure 18 illustrates an ad-
vanced digital workflow tailored for managing heritage buildings, featuring integration
between BIM and sensor-based platforms for real-time data collection and analysis. This
setup enables continuous monitoring of environmental conditions and energy consump-
tion, which are essential for designing retrofiing strategies that balance preservation
needs with sustainability goals. By using such workflows, heritage buildings can be man-
aged to reduce their carbon footprint while maintaining architectural integrity.
3.4.2. DT-HBIM for Preventive Conservation
Preventive conservation refers to the strategies and actions designed to avoid the de-
terioration or damage of cultural heritage artifacts and historical structures [28]. It encom-
passes a broad range of activities, such as environmental control, risk management, and
regular maintenance, aiming to extend the lifespan of these assets without altering their
original state [99]. An example of preventive conservation is the controlled environment
in museums that stabilizes temperature and humidity levels to prevent the degradation
of artworks and artifacts [100].
Within this context, Digital Twin Historic Building Information Modeling (DT-HBIM)
plays a crucial role in enhancing the effectiveness of preventive conservation strategies for
historical buildings [101]. DT-HBIM creates a virtual counterpart of a physical building,
integrating real-time data from various sensors to simulate and analyze the building’s be-
havior under different conditions [102]. This allows for the early detection of potential
Figure 18. Digital workflow proposed for heritage buildings based on [27].
Central to this operation are the BIM PLATFORM ONLINE and COMFORT PLAT-
FORM ONLINE, which together orchestrate a comprehensive strategy for assessing and
improving thermal comfort, crucial for the preservation and sustainable use of heritage
buildings. Sensors meticulously capture on-site indoor and outdoor conditions, feeding
data back to the HOMEBIM platform, where it is processed and analyzed. This interplay of
data from various sources, including consumption metrics and costs, is pivotal in adapting
retrofitting strategies for heritage buildings [
27
]. It informs decision-making processes,
ensuring that interventions respect architectural integrity while promoting energy effi-
ciency [
27
]. This digital workflow, as depicted in Figure 18, provides a blueprint for the
application of advanced technologies in the conservation of historic structures, aiming to
reduce energy demand and CO
2
emissions in line with Italy’s energy efficiency directives,
thus fostering sustainable heritage building management. Figure 18 illustrates an advanced
digital workflow tailored for managing heritage buildings, featuring integration between

Heritage 2024,76455
BIM and sensor-based platforms for real-time data collection and analysis. This setup
enables continuous monitoring of environmental conditions and energy consumption,
which are essential for designing retrofitting strategies that balance preservation needs
with sustainability goals. By using such workflows, heritage buildings can be managed to
reduce their carbon footprint while maintaining architectural integrity.
3.4.2. DT-HBIM for Preventive Conservation
Preventive conservation refers to the strategies and actions designed to avoid the
deterioration or damage of cultural heritage artifacts and historical structures [
28
]. It
encompasses a broad range of activities, such as environmental control, risk management,
and regular maintenance, aiming to extend the lifespan of these assets without altering their
original state [
99
]. An example of preventive conservation is the controlled environment in
museums that stabilizes temperature and humidity levels to prevent the degradation of
artworks and artifacts [100].
Within this context, Digital Twin Historic Building Information Modeling (DT-HBIM)
plays a crucial role in enhancing the effectiveness of preventive conservation strategies for
historical buildings [
101
]. DT-HBIM creates a virtual counterpart of a physical building,
integrating real-time data from various sensors to simulate and analyze the building’s
behavior under different conditions [
102
]. This allows for the early detection of potential
issues that could lead to deterioration, enabling timely interventions [
102
]. Consequently,
DT-HBIM supports the sustainable preservation of cultural heritage and optimizes the
maintenance and management processes, ensuring that preventive measures are both
effective and efficient [102].
3.5. IoT as the Connective Tissue for Dynamic DT
This subsection explores the role of the IoT as a foundational element in the evolution
and operational efficiency of DTs for heritage buildings. It discusses the integration of IoT
within DT models, emphasizing the critical task of accurately mirroring the ever-changing
conditions of heritage structures.
3.5.1. Integration of IoT with DT Models
The integration of IoT with DT models holds immense importance for the conservation
and maintenance of heritage buildings, bridging critical gaps in real-time monitoring and
predictive maintenance [
103
]. This synergy is pivotal for the sustainable preservation of
cultural heritage, enabling stakeholders to understand and anticipate the needs of these
structures more effectively [
104
]. One of the most significant challenges in this integration
lies in the nuanced application of IoT sensors within heritage sites [
105
]. These sensors must
be deployed in a manner that respects the esthetic and structural integrity of the buildings,
often requiring innovative solutions to avoid altering their historical fabric [
106
]. Moreover,
the development of DT models that can dynamically reflect the real-time conditions of a
heritage building based on IoT sensor data demands sophisticated software capable of
complex data analysis and predictive modeling [
107
]. The integration of IoT with DT mod-
els, as described, is essential for the proactive conservation of heritage sites, especially for
real-time monitoring and predictive maintenance (see Table 6). This table offers a detailed
summary of sensor technologies and their applications, enabling conservation profession-
als to select tools tailored to specific preservation needs. For instance, 3D laser scanners
and ground-penetrating radar (GPR) facilitate structural documentation and subsurface
imaging, aiding in assessing the physical health of heritage sites non-invasively. Environ-
mental sensors, pivotal in monitoring microclimate factors like humidity and temperature,
support the preservation of fragile materials by controlling critical environmental variables.
By providing insights into real-world applications, Table 6underscores the technological
advancements in IoT-enabled DT models that can dynamically reflect real-time conditions,
helping conservators make data-driven decisions.

Heritage 2024,76456
Predictive maintenance, as a specific application of this integration, exemplifies its
potential benefits [
108
]. For instance, in the case of the ancient Alhambra Palace in Spain,
predictive maintenance could involve the use of IoT sensors to monitor the humidity and
temperature levels continuously [
109
]. These sensors could detect subtle changes in the
microclimate that may predict the growth of mold or the deterioration of delicate frescoes
before visible signs appear [
110
]. Advanced DT models would then simulate these condi-
tions and predict their impact on the building, allowing conservators to implement targeted
interventions, such as adjusting environmental controls or conducting minor repairs to
prevent significant damage [
111
]. However, the gaps in achieving seamless integration and
effective predictive maintenance are evident [
112
]. These include the need for more ad-
vanced algorithms for data analysis, capable of accurately predicting deterioration patterns,
and strategies for non-intrusive sensor placement that do not compromise the building’s
historical value [
113
]. Moreover, ensuring data security and fostering interdisciplinary
collaboration remain crucial challenges that need addressing to fully realize the potential
of IoT and DT models in heritage building conservation [
14
]. Table 6summarizes sensor
technologies in heritage conservation, enabling quick identification of suitable tools for
specific preservation tasks. It highlights seminal studies for deeper exploration and fosters
an informed selection of methods for structural health monitoring and preventive conser-
vation. Notably, 3D laser scanners capture precise geometric information, which can be
integrated with data from various sensors to provide a holistic view of heritage sites.
Table 6. Overview of sensor technologies in heritage conservation.
Sensor Type Application Outcome Reference
3D Laser Scanners Detailed geometric
documentation and structural
analysis
Accurate 3D models for assessing structural
health and planning conservation works [30,114–116]
Wireless Sensor Networks (WSN) Real-time health monitoring of
architectural heritage
Non-invasive monitoring of environmental
parameters crucial for the preservation of
heritage buildings [117,118]
Infrared Thermography (IRT) Detection of moisture, insulation
failures, and thermal anomalies
Identifying areas at risk of deterioration due
to environmental factors, aiding in
preventive conservation [119]
Environmental Sensors Monitoring microclimate
conditions within heritage sites
Ensuring the preservation of materials by
controlling temperature, humidity, and other
environmental factors [120]
Ground-Penetrating Radar (GPR) Sub-surface imaging of
foundations and buried structures
Non-invasive exploration of structural
integrity and identification of hidden
features without physical excavation [121]
Digital Image Correlation (DIC) Monitoring of deformations and
displacement over time
Provides a detailed analysis of structural
movement, critical for assessing the stability
and integrity of heritage structures [122]
Fiber Optic Sensors Long-term structural health
monitoring
Real-time, continuous monitoring of strains
and stresses within structural elements,
allowing for early detection of deterioration [123]
Ultrasonic Sensors
Material characterization and flaw
detection
Assessing the condition of materials and
detecting voids, cracks, and other defects in
building elements [124]
3.5.2. Energy Efficiency in IoT for Cultural Heritage (CH)
Creating an energy-efficient IoT architecture specifically tailored for the preventive con-
servation of cultural heritage represents a significant gap in the current field of technology
application within heritage preservation [
125
]. Addressing this gap involves the develop-
ment of IoT solutions that minimize energy consumption while maximizing the monitoring
and maintenance capabilities essential for the sustainable conservation of cultural heritage
sites [
86
]. The core of this challenge lies in designing IoT devices and systems that operate
on minimal power without compromising their ability to collect and transmit essential

Heritage 2024,76457
data [
126
]. This requires advancements in low-power sensor technology, energy-efficient
data transmission protocols, and innovative power management strategies [127].
Energy efficiency is a crucial factor in IoT architectures for cultural heritage con-
servation. The section on energy-efficient IoT systems emphasizes the development of
devices that consume minimal power without sacrificing data-gathering effectiveness. Such
advancements include low-power wide-area networks (LPWAN) and energy-harvesting
sensors, which prolong device operational life and reduce maintenance frequency—a
priority in the context of non-intrusive heritage preservation. Table 6reinforces this by
listing low-power solutions like wireless sensor networks (WSNs) and fiber optic sensors,
which provide continuous monitoring capabilities for heritage sites without excessive
power demands.
For instance, deploying sensors that can harvest energy from environmental sources,
such as solar, thermal, or kinetic energy, could significantly reduce the reliance on traditional
battery power and enhance the sustainability of IoT deployments in cultural heritage
sites [128].
Additionally, the architecture must incorporate energy-efficient communication proto-
cols that optimize data transmission to conserve power [
129
]. This could involve the use
of low-power wide-area network (LPWAN) technologies, which are designed to transmit
small amounts of data over long distances with minimal energy consumption [
130
]. Im-
plementing such protocols ensures that IoT devices can remain operational for extended
periods, reducing the need for frequent maintenance or battery replacement, which is par-
ticularly crucial in the context of cultural heritage conservation, where minimal intrusion
is desired [
131
]. Furthermore, the development of smart algorithms and edge computing
capabilities is another key component of an energy-efficient IoT architecture [
132
]. The
amount of data that needs to be transmitted to the cloud or central servers can be reduced
by processing data locally on the IoT device or nearby edge computing nodes, thereby
conserving energy [
133
]. Moreover, smart algorithms can enable devices to operate in a
low-power sleep mode when active monitoring is not required, waking only to report
significant changes or anomalies [
134
]. This intelligent management of operational states
further enhances the energy efficiency of the system [135].
In addition, the integration of a centralized management platform that can analyze
data from multiple sensors and devices in real-time, applying predictive analytics to opti-
mize the conservation process, is essential [
136
]. Such a platform should also be capable of
managing the energy consumption of the IoT architecture, dynamically adjusting settings
and protocols based on the current needs and priorities of the conservation efforts [
137
].
Lastly, the development of this energy-efficient IoT architecture must involve a multi-
disciplinary approach that includes conservation scientists, IoT engineers, and energy
management experts [
138
]. Collaboration between these fields can lead to innovative solu-
tions that balance the conservation needs of cultural heritage sites with the sustainability
goals of modern technology applications [96].
3.6. Machine Learning for Predictive Conservation
Machine learning for predictive conservation harnesses data-driven algorithms to
forecast deterioration and inform the maintenance of cultural heritage assets. Machine
learning for predictive conservation represents another significant innovation, blending
advanced computational techniques with traditional conservation practices (see Table 7).
Table 7provides a comprehensive review of ML applications in heritage conservation,
including damage assessment, predictive modeling, and virtual restoration. For instance,
convolutional neural networks (CNNs) are applied to classify and localize damage, while
generative adversarial networks (GANs) facilitate the virtual restitution of missing or
damaged heritage elements. These techniques exemplify how ML can predict potential
deterioration and assist in preemptive interventions, thus preserving cultural assets more
effectively. The range of ML applications in Table 7highlights the importance of inter-

Heritage 2024,76458
disciplinary collaboration among ML experts, conservation scientists, and historians in
achieving sustainable heritage conservation.
By consolidating such technology summaries and empirical findings, Tables 6and 7
contribute valuable reference points for the application of IoT, DT, and ML technologies in
the proactive preservation and sustainable management of cultural heritage sites.
This approach utilizes historical data and sensor readings to train models that can
predict potential issues, allowing for timely interventions and preservation efforts. It
represents a proactive shift in heritage conservation, blending technology and tradition
to safeguard cultural treasures for future generations, which will be discussed in this
subsection.
3.6.1. Restitution of Damaged Heritage
The development of comprehensive machine learning (ML) models to effectively uti-
lize data for predicting scenarios like material deterioration or structural damage in heritage
buildings represents a significant advancement in the field of heritage conservation [139].
Key to the success of these models is the integration of diverse data types, including
time-series data from environmental sensors, visual data from drones, laser scanners,
or robotic inspections, and textual data from historical and conservation records [
140
].
Advanced techniques in data preprocessing and feature engineering are crucial to preparing
this heterogeneous data for analysis [
141
]. Deep learning models, especially those utilizing
convolutional neural networks (CNNs) for image analysis and recurrent neural networks
(RNNs) for time-series forecasting, are at the forefront of this research [
142
]. However,
the challenge extends beyond model selection and training; it includes ensuring that the
models are interpretable by conservation experts and adaptable to the evolving conditions
of heritage sites [143].
Parallel to predicting material and structural issues, ML, particularly generative ad-
versarial networks (GANs), offers novel possibilities in the restitution of damaged heritage
structures [
144
]. GANs, which consist of two neural networks—the generator and the
discriminator—working in tandem, can be trained on images of undamaged sections
of buildings or sculptures to generate realistic reconstructions of missing or damaged
parts [
145
]. This aids in the virtual restoration of cultural heritage for educational as well as
research purposes and provides invaluable insights for physical restoration projects [
146
].
The accuracy and realism of the reconstructions generated by GANs depend on the quality
and extent of the training data, as well as the model’s ability to understand and replicate
complex historical styles and construction techniques [147].
The development and refinement of these ML models require a multidisciplinary
approach, combining expertise in machine learning, computer vision, conservation science,
and architectural history [
148
]. Challenges such as data scarcity, especially high-quality
images of damaged or destroyed heritage sites, and the need for models to appreciate
the context and historical significance of the artifacts they are reconstructing must be
addressed [
149
]. Additionally, ethical considerations around the use of AI in altering
images or creating reconstructions of cultural heritage need careful deliberation to ensure
that the digital restoration respects the integrity and authenticity of the original works [
150
].
Table 7provides a comprehensive summary of recent studies focusing on the ap-
plication of machine learning techniques in the conservation of heritage buildings. It
showcases the innovative use of these technologies across various aspects of heritage
conservation, including structural health monitoring, predictive maintenance, automated
damage assessment, and the reconstruction of historical buildings.

Heritage 2024,76459
Table 7. Overview of recent studies on machine learning applications in heritage buildings conserva-
tion.
Reference
Main Findings
[139]Reviews various ML techniques for assessing the health condition of heritage buildings, including predictive models for damage
scenarios and mechanical properties of materials.
[144] Uses conditional generative adversarial networks to predict missing/damaged parts of historical buildings.
[151] Demonstrates the effectiveness of CNN and SVM models in classifying damage severity levels in heritage buildings.
[152]Proposes SVM for automatically recognizing elements in existing buildings to create semantic information models from point cloud
data.
[153]
Uses deep learning methods, including transfer learning with pre-trained networks, for the classification and localization of defects in
cultural heritage buildings in Iran.
[154]Develops learning models to analyze data from the digital documentation of heritage structures, proposing an ontology for heritage
buildings and damage due to disasters.
[155]Discusses the development and application of machine learning in the fields of energy conservation and indoor environment,
including predictive modeling for indoor culturable fungi concentration.
[156]Proposes a method to support preventive conservation programs through the analysis of maintenance requests using LSTM neural
networks, achieving a prediction accuracy of 96.6%.
[157] Describes the use of machine learning algorithms for analyzing BIM data to improve decision-making in energy renovation projects.
[158]
Surveys the application of machine learning to cultural heritage, analyzing the adoption and adaptation of ML algorithms for various
CH applications.
[159]Discusses the potential of parametric modeling techniques in the restoration and reconstruction processes of heritage buildings
through a BIM software plug-in.
3.6.2. Ontology-Based Conservation
Creating ontology-based ML frameworks for the information and knowledge manage-
ment in architectural heritage conservation offers a sophisticated approach to preserving
cultural landmarks [
144
]. Such frameworks provide ontologies as structured systems
that define relationships between various concepts and entities relevant to heritage con-
servation [
160
]. Conservationists can significantly improve the decision-making process,
informed by a deep understanding of each site’s unique characteristics and conservation
needs, by integrating these ontologies with ML algorithms [
161
]. This integration aids in
organizing and accessing a vast array of knowledge, from physical elements and conserva-
tion techniques to historical significance and environmental impacts, in a clear, hierarchical
manner [162].
The development of these frameworks involves creating detailed ontologies that en-
compass the broad spectrum of factors in architectural heritage conservation [
163
]. This in-
cludes categorizing building materials, structural components, decorative features, and con-
servation methods, as well as considering historical, legal, and environmental aspects [
164
].
Such comprehensive ontologies serve as powerful tools for managing conservation-related
knowledge, facilitating the automated analysis of data through ML algorithms [
165
]. This
allows for the identification of patterns and trends that may not be evident to human
analysts, such as predicting the effectiveness of conservation techniques under specific
conditions or identifying risks from changing legal frameworks [
166
]. However, challenges
arise in the development and application of ontology-based ML frameworks, primarily
due to the interdisciplinary expertise required and the extensive process of data collection
and digitization [
167
]. These challenges underscore the need for collaboration among
experts in ontology development, ML, architectural history, and conservation science [
168
].
Additionally, the frameworks must accommodate the complexity of architectural heritage
conservation, processing data from diverse sources like textual documents, architectural
plans, and sensor readings, thereby enabling collaborative knowledge sharing among
various stakeholders [169].
In addressing the integration of qualitative and quantitative data for the conservation
of cultural heritage, an ontological model for 3D semantic annotation aims to bridge the gap

Heritage 2024,76460
between data acquisition and expert knowledge interpretation [
170
]. This multidisciplinary
approach integrates semantic, spatial, and morphological dimensions to describe conser-
vation states comprehensively [
171
]. For example, using reality-based 3D annotations to
incorporate expert analyses directly into a heritage structure’s 3D representation allows for
dynamic monitoring of conservation states over time [
172
]. The integration of semantic
annotation with CIDOC-CRM and the use of image-based modeling and 3D point clouds
enrich the documentation process, enabling a detailed analysis that combines diverse data
types and expert insights [173].
3.7. Enriching Heritage Experience with AR and VR
3.7.1. Underwater Archeological Sites
Exploring underwater archeological sites through virtual reality (VR) and augmented
reality (AR) technologies marks an important shift in research and public engagement with
submerged cultural heritage [
174
]. These technologies afford immersive experiences that
dramatically improve access to and comprehension of historical sites located beneath water
bodies, traditionally restricted to the public and specialists due to the harsh conditions and
advanced equipment needed for exploration [
175
]. VR offers the opportunity to develop
detailed, interactive simulations based on 3D models from underwater photogrammetry,
sonar imaging, and archeological discoveries, allowing users to virtually experience these
sites with a realism that emulates diving, minus the risks or need for certifications [
176
]. AR
enhances this further by integrating digital reconstructions with the real world, enriching
visits to museums or actual sites with historical narratives and comparisons through
smartphone apps or AR glasses [177].
However, the development of VR and AR applications for this purpose is not without
its challenges [
178
]. It necessitates collaborative efforts across disciplines, including arche-
ology, marine science, and technology development, combining accurate archeological data
with advanced 3D modeling and user-centric designs to create compelling educational
tools [
179
]. Technical hurdles such as creating high-resolution, realistic yet computation-
ally efficient 3D models for real-time rendering on VR and AR platforms pose significant
obstacles [
180
]. Additionally, there is the task of overlaying digital information accurately
onto the physical world in AR applications, which requires precise geo-location and im-
age recognition technologies to offer a seamless integration of past and present states of
archeological sites [181].
Despite these challenges, the potential of VR and AR technologies to enhance visi-
bility, accessibility, and understanding of underwater cultural heritage is immense [
182
].
For instance, the virtual reconstruction of the Antikythera shipwreck in Greece using VR
allows users worldwide to explore the site in detail, offering insights into ancient maritime
history that were previously limited to a select few divers and researchers [
183
]. Similarly,
AR applications can transform a visit to the British Museum by superimposing digital
images of the original form of artifacts over their current states, providing a more con-
textual understanding of their historical significance [
184
]. Beyond public education and
engagement, these technologies serve as vital tools for the conservation and preservation
of submerged sites, reducing physical impact on these delicate environments and enabling
precise monitoring over time to safeguard them for future generations [185].
3.7.2. Engineering-Grade Devices
Developing robust AR and VR devices capable of withstanding the complex con-
ditions prevalent at construction and heritage sites marks a significant challenge in the
field of immersive technology [
186
]. These environments often present harsh conditions,
including dust, moisture, vibration, and extreme temperatures, necessitating the creation of
engineering-grade devices that are not only technologically advanced but also durable and
reliable [
187
]. The key to engineering these devices lies in designing for ruggedness from the
ground up [
188
]. This includes the use of durable materials capable of protecting sensitive
electronic components from physical impact, water ingress, and particulate matter [
189
].

Heritage 2024,76461
For example, high-grade plastics, metals, and rubberized seals can be employed to achieve
a durable exterior, while internal components may require shock-absorbing materials to
protect against vibration and impacts commonly encountered on construction sites.
In addition to physical durability, these devices must possess high-performance com-
puting capabilities to render complex AR/VR experiences in real-time [
190
]. This requires
efficient thermal management systems to dissipate heat generated by processors, espe-
cially in outdoor environments where temperatures can significantly fluctuate. Advanced
cooling techniques, such as heat pipes and thermal conduction materials, become critical
in maintaining optimal device performance without compromising the integrity of the
hardware [
191
]. Battery life is another critical consideration, as these devices need to
operate for extended periods without access to charging facilities [
192
]. Implementing
energy-efficient processors and displays, alongside larger capacity batteries and potentially
energy harvesting technologies, can help extend the operational life of AR/VR devices in
the field [
193
]. Furthermore, the user interface and experience must be tailored to the needs
of construction and heritage site workers, who may have limited experience with AR/VR
technology and who often wear protective gear [
178
]. Devices should feature intuitive
controls that can be easily manipulated with gloves, and displays must be readable in a
wide range of lighting conditions, including direct sunlight [
194
]. Voice recognition and
gesture-based interfaces offer promising alternatives for hands-free operation, enhancing
usability in environments where manual interaction is limited [195].
Interoperability with existing digital tools and systems used in construction and her-
itage conservation is also essential [
196
]. This requires the development of open standards
and APIs that facilitate seamless integration with software for project management, 3D
modeling, and data analysis, ensuring that AR/VR devices can be effectively incorporated
into the existing technological ecosystem of these industries [
197
]. The development of
engineering-grade AR/VR devices for construction and heritage sites represents an intersec-
tion of advanced materials science, electronics, and user-centered design [
198
]. Achieving
this blend of durability, performance, and usability will not only revolutionize the way
professionals interact with complex sites but also open new possibilities for the application
of immersive technologies in various industrial settings, enhancing efficiency, safety, and
the preservation of cultural heritage [199].
3.7.3. Digital Preservation of Cultural Elements
Applying AR and VR for digital preservation marks a transformative approach to
conserving cultural elements, enabling vivid contextual recall and comparison of old scenes
at heritage sites [
200
]. This innovative use of technology allows not only for the preservation
of physical structures but also for the capture and recreation of the intangible aspects that
give these sites their unique cultural and historical significance [
201
]. AR technology can
overlay historical images, videos, or 3D reconstructions onto the current view of a site,
allowing visitors to see how it has changed over time [
202
]. This immediate contextual
comparison helps in understanding the evolution of the site, highlighting the conservation
efforts and changes due to natural or human factors. For instance, visitors equipped with
AR devices or smartphones can point their cameras at different parts of a heritage site and
see overlays of historical photographs or artistic recreations of past events associated with
that location [
203
]. This enhances the visitor experience by making history come alive and
serves an educational purpose, providing deeper insights into the cultural and historical
context of the site.
VR takes this one step further by creating completely immersive environments that
can transport users to different time periods [
204
]. Through detailed 3D modeling and
rendering, VR can reconstruct heritage sites in their historical context, allowing users to
explore them as they once were [
205
]. This is particularly valuable for sites that have been
damaged or lost due to natural disasters, conflict, or neglect. VR enables the preservation
of these sites in digital form, ensuring that their cultural and historical value is accessible
to future generations [
206
]. Users can navigate through these virtual reconstructions,

Heritage 2024,76462
experiencing the spatial and architectural elements of the past firsthand, which is invaluable
for educational, research, and preservation purposes [
207
]. Moreover, these technologies
offer significant benefits for the preservation of intangible cultural heritage, such as rituals,
languages, and traditional crafts.
3.8. Bridging Disciplines
3.8.1. Ethical Frameworks for Digital Replication
Ethical Principles for Digital Workflows
Developing ethical principles or codes of ethics for heritage recording specialists is
critical to addressing the moral and ethical implications of digital technologies in the con-
servation of heritage buildings [
208
]. As digital workflows become increasingly integral
to the documentation, analysis, and preservation of heritage sites, the need for a guid-
ing ethical framework becomes paramount [
209
]. Such principles should address issues
like data accuracy, representation, accessibility, and the long-term preservation of digital
records [
210
]. They must ensure that digital documentation and analysis methods respect
the integrity and authenticity of heritage sites, preventing the distortion or loss of historical
and cultural significance [211].
Furthermore, ethical guidelines should consider the impact of digital technologies on
privacy and the rights of communities associated with heritage sites [
212
]. The use of digital
tools in heritage conservation often involves capturing detailed information about sites that
may have cultural sensitivities or require protection from exploitation [
87
]. Ensuring that
communities are engaged in the decision-making process and that their rights are protected
in the digital representation of heritage sites is essential [
213
]. This includes acknowledging
intangible cultural heritage and ensuring that digital reproductions do not misinterpret or
misrepresent cultural practices and values [214].
Ethics by Design
Ethics by Design is a fundamental approach for framing a code of ethics in the digital
age, ensuring that digital and leading-edge technologies align with data protection and
ethical standards [
215
]. This approach mandates the integration of ethical considerations
into the very fabric of technology development and deployment processes, focusing on
protecting individual privacy, ensuring data security, and promoting fairness and inclusiv-
ity [216].
The Ethics by Design methodology emphasizes transparency, accountability, and user
empowerment in the development of digital technologies [
217
]. It requires developers
and organizations to not only adhere to existing legal frameworks and data protection
laws but also to exceed these standards by fostering an ethical culture that prioritizes
the well-being of individuals and communities [
217
]. This involves conducting thorough
impact assessments to understand and address the societal implications of technologies,
engaging with diverse stakeholders to gain a broad perspective on potential ethical issues,
and committing to continuous ethical evaluation and improvement of technologies post-
deployment [
218
]. Implementing Ethics by Design also involves addressing the challenges
of technological innovation, such as artificial intelligence (AI) and big data analytics, where
ethical risks can be complex and unpredictable [219].
An example of Ethics by Design in heritage conservation could be the development of
an AR mobile application for visitors to engage with a historic cathedral, ensuring ethical
considerations must be integrated throughout the design and implementation process [
220
].
This includes obtaining user consent for data collection, securing user data, and providing
transparent documentation of information sources. Stakeholder engagement guarantees
accuracy and authenticity, while user empowerment must be prioritized through customiz-
able features catering to diverse interests [
221
]. Continuous evaluation and improvement
of the application post-deployment ensure ongoing alignment with ethical standards, en-
hancing visitors’ understanding and appreciation of the cathedral’s cultural heritage while
respecting privacy and promoting inclusivity [222].

Heritage 2024,76463
3.8.2. The Convergence of Multidisciplinary Expertise
Multidisciplinary Decision-Making Methods
Multidisciplinary decision-making methods, particularly multi-criteria decision-making
(MCDM), play a crucial role in navigating the complexities of heritage building conser-
vation [
223
]. These methods enable the integration of various perspectives, including
architectural, historical, environmental, and socio-economic factors [
224
], into a cohesive
decision-making framework [
225
]. MCDM offers a structured approach to evaluate multi-
ple criteria that are often conflicting, making it possible to arrive at decisions that balance
preservation needs with modernization demands [
226
]. However, the application of these
methods in the context of heritage buildings is fraught with challenges [
227
]. These include
the subjective nature of valuing historical significance, the difficulty in quantifying esthetic
and cultural factors, and the need for stakeholder engagement in the decision-making
process [227].
Despite the potential of MCDM to enhance decision-making in heritage building
conservation, there remains a significant gap in research concerning its practical application
and effectiveness [
223
]. Current studies often focus on theoretical frameworks and method-
ological developments without sufficiently addressing the real-world complexities and
the dynamic nature of heritage conservation [
228
]. There is a particular need for empirical
research that explores how these methods are applied in practice, including the integration
of stakeholder preferences, the handling of uncertain and incomplete information, and the
assessment of long-term sustainability impacts [
229
]. Addressing this gap is essential for
developing more robust and adaptable decision-making tools that can meet the unique
challenges of conserving heritage buildings [230].
Non-Destructive Techniques for Conservation
The development of non-destructive and non-contact techniques for conservation
assessment marks a significant advancement in preserving heritage buildings [
231
]. These
techniques, which include methods like ground-penetrating radar, ultrasonic testing, and
digital photogrammetry, allow for the detailed analysis of a structure’s condition without
causing harm to the building’s fabric [
232
]. This approach preserves the integrity of histori-
cal sites and provides a wealth of information about hidden or inaccessible features [
231
].
Interdisciplinary expertise is crucial in this area, combining knowledge from civil engineer-
ing, materials science, computer science, and conservation science to innovate and refine
these technologies [232].
However, there is a gap in the interdisciplinary application and integration of these
techniques into standard conservation practices [
233
]. Many conservation professionals
may not be fully aware of the capabilities and limitations of these technologies, leading to
underutilization in the field. Furthermore, there is a need for comprehensive frameworks
that guide the selection and application of appropriate non-destructive techniques based
on the specific requirements of each heritage site [
234
]. Bridging this gap requires focused
research on the practical implementation of these technologies, including case studies
demonstrating their effectiveness in various conservation scenarios and guidelines for their
integration into conservation strategies [235].
3.9. Case Study
In this detailed exploration through this review paper, the research investigates the
potential of emerging technologies in the conservation and restoration of heritage build-
ings [236–239], through a compelling case study from Saudi Arabia.
The research explored four distinct historical sites, each presenting unique challenges
and opportunities for the application of digital twin (DT) technologies, including building
information modeling (BIM), laser scanning, machine learning (ML), and the Internet of
Things (IoT). The aim is to demonstrate novel contributions to the field by specifying equip-
ment and methods and providing examples of how these technologies can be implemented
to preserve the cultural legacy of these sites.

Heritage 2024,76464
Saudi Arabia is chosen as a focal point for this exploration due to its rich cultural
tapestry and the government’s progressive stance on heritage preservation [
240
–
242
], as
exemplified by Vision 2030. The country’s rapid modernization, juxtaposed with its deep
historical roots [
240
,
243
,
244
], provides a unique backdrop for the implementation of DT
technologies. The three historical sites selected—At-Turaif District, Bujairi Quarter, Buwaib
Village, and Rughabah Village, as can be seen in Table 8—represent a cross-section of the
kingdom’s diverse heritage, from urban landscapes (242–245) to agricultural terrains and
abandoned rural settlements. Table 8succinctly summarizes the interventions undertaken
at each site, illustrating the varied methodologies employed and their corresponding im-
pacts. This table not only serves as a reference for specific interventions but also emphasizes
the overarching strategies of conservation, such as the importance of maintaining the tradi-
tional atmosphere while adapting to modern needs. The diversity of use cases, from tourist
attractions to museums, underscores the multifaceted approach needed for sustainable
heritage preservation.
Table 8. Summary of sustainable heritage reuse interventions in Saudi Arabia. Case studies of
Ushaiger Village, At-Turaif District, Rawdat Sudair Village, and Rughabah Village based on [245].
Heritage Site Location Conservation Date and
Institutions
Methodologies and
Interventions Use
Ushaiger Village Najd region, near Shaqra.
History of 1500 years as a
pilgrim rest spot.
Rehabilitated in 2017 by the
Saudi Commission for
Tourism and National
Heritage. Received the Prince
Sultan bin Salman Award.
One hundred houses
restored with modern
amenities. Strategy to
preserve traditional
atmosphere.
Evolved to a tourist
attraction with a
restaurant, market, and
private museums.
At-Turaif District
The first capital of the Al
Saud dynasty, northwest
of Riyadh. UNESCO
World Heritage site.
Conservation was segmented
into periods before 2010,
2010–2017, and after 2017.
Managed by Diriyah Gate
Development Authority.
Focus on
non-intrusiveness,
reversibility, and original
materials. Some
anastylosis.
Open-air museum with
buildings open to the
public, showcasing the
area’s history.
Rawdat Sudair In Sudair region, Najd
province. Historically
significant for agriculture.
Restored between 2005 and
2015 by the Saudi Commission
for Tourism and Antiquities.
Conservation aimed at
reusing Al-Dakhlah
Mosque. Used local
materials, minimal
anastylosis.
Commercial activities, like
a museum, promote
tourism with a focus on
traditional values.
Rughabah Village Northwest of Riyadh in
Najd, urban development
from 1669.
Tower restored in 1974, 1996,
and 2018 by various patrons
and the Saudi Commission for
Tourism and National
Heritage.
Traditional materials and
techniques for restoration.
Minimal legibility and
reversibility.
Abandoned village known
for the restored tower and
nearby castle remains
functions as an open-air
museum.
In addition, Figure 19 provides a quantified evaluation of various conservation inter-
ventions across the four heritage sites mentioned above. This figure visually encapsulates
the comparative effectiveness of different conservation methods, revealing insightful pat-
terns in how each site’s interventions align with key criteria such as legibility, reversibility,
and alteration. For instance, the high score for reversibility at Ushaiqer Village suggests
a successful strategy in maintaining the integrity of the site while allowing for necessary
adaptations. Conversely, the lower scores for Rughabah Village in legibility indicate areas
where further attention may be needed to enhance visitor understanding and engage-
ment. This visual representation not only aids in assessing the success of interventions
but also highlights areas for future research and practice, aligning conservation efforts
with standardized criteria for intervention success. The assessment considers criteria such
as legibility, reversibility, overstanding, alteration, replacement, and analysis and ruins.
This visual representation aids in understanding the effectiveness of different conservation
methods applied to each site. It becomes clear that while some sites like Ushaiqer Village
score high in terms of reversibility, others like Rughabah Village are better evaluated for
legibility. These data are invaluable for directing future conservation efforts, ensuring that
strategies employed not only respect the inherent value of the cultural heritage but are also

Heritage 2024,76465
measured against standardized criteria for intervention success. Integrating these assess-
ment values into the DT framework enhances decision-making processes by providing a
metric-based approach to evaluate and refine conservation techniques.
Heritage 2024, 7, FOR PEER REVIEW 35
Figure 19. Assessment values for the specified urban heritage intervention parameters based on
[245].
3.9.1. At-Turaif District
At-Turaif District, with its historical significance, offers fertile ground for DT technol-
ogy. Here, BIM acts as the cornerstone of digital preservation. Utilizing the advanced ca-
pabilities of Autodesk Revit, it is possible to construct a precise virtual model of the district.
Integrating this with HBIM, specialized in heritage buildings, allows for the layering of
historical data, enabling the tracing of architectural evolution over time. For example, the
implementation of Trimble’s TX8 laser scanner would produce detailed 3D models of the
district’s unique Najdi architectural features, aiding in the accurate restoration of dam-
aged structures. These models are instrumental for physical restoration and serve educa-
tional and research purposes, as they enable historians and architects to analyze the site’s
historical construction techniques within a virtual environment. This digital approach en-
hances preservation accuracy and paves the way for innovative virtual tourism experi-
ences, extending the site’s reach without endangering its physical state.
3.9.2. Bujairi Quarter
At the Bujairi Quarter, the DT model becomes a nexus between agricultural heritage
and architectural conservation. IoT technology can be extensively employed to monitor
the microclimate that affects both the crops and earthen structures. A network of sensors,
such as the Decagon 5TM Water Content and Temperature sensors, could be strategically
placed to relay real-time data on soil moisture levels and ambient temperatures. These
data, fed into an AI platform like IBM’s Watson IoT, enables predictive analytics to safe-
guard heritage structures from adverse weather effects by forecasting potential risks and
automating preventive measures. Furthermore, ML, through platforms like TensorFlow,
can process this environmental data alongside historical conservation records to predict
structural vulnerabilities, guiding timely maintenance while preserving the site’s agricul-
tural narrative.
3.9.3. Buwaib Village
For Buwaib Village, the combination of laser scanning and BIM facilitates the resur-
rection of a forgoen past. High-definition scanners, such as the FARO Focus3D, can dig-
itally capture the village’s current state, allowing for the creation of an exact virtual replica.
This digital reconstruction serves as a blueprint for restoration and as a foundation for the
Figure 19. Assessment values for the specified urban heritage intervention parameters based on [
245
].
3.9.1. At-Turaif District
At-Turaif District, with its historical significance, offers fertile ground for DT tech-
nology. Here, BIM acts as the cornerstone of digital preservation. Utilizing the advanced
capabilities of Autodesk Revit, it is possible to construct a precise virtual model of the dis-
trict. Integrating this with HBIM, specialized in heritage buildings, allows for the layering
of historical data, enabling the tracing of architectural evolution over time. For example,
the implementation of Trimble’s TX8 laser scanner would produce detailed 3D models
of the district’s unique Najdi architectural features, aiding in the accurate restoration of
damaged structures. These models are instrumental for physical restoration and serve
educational and research purposes, as they enable historians and architects to analyze
the site’s historical construction techniques within a virtual environment. This digital
approach enhances preservation accuracy and paves the way for innovative virtual tourism
experiences, extending the site’s reach without endangering its physical state.
3.9.2. Bujairi Quarter
At the Bujairi Quarter, the DT model becomes a nexus between agricultural heritage
and architectural conservation. IoT technology can be extensively employed to monitor the
microclimate that affects both the crops and earthen structures. A network of sensors, such
as the Decagon 5TM Water Content and Temperature sensors, could be strategically placed
to relay real-time data on soil moisture levels and ambient temperatures. These data, fed
into an AI platform like IBM’s Watson IoT, enables predictive analytics to safeguard her-
itage structures from adverse weather effects by forecasting potential risks and automating
preventive measures. Furthermore, ML, through platforms like TensorFlow, can process
this environmental data alongside historical conservation records to predict structural vul-
nerabilities, guiding timely maintenance while preserving the site’s
agricultural narrative.
3.9.3. Buwaib Village
For Buwaib Village, the combination of laser scanning and BIM facilitates the res-
urrection of a forgotten past. High-definition scanners, such as the FARO Focus3D, can
digitally capture the village’s current state, allowing for the creation of an exact virtual

Heritage 2024,76466
replica. This digital reconstruction serves as a blueprint for restoration and as a foundation
for the virtual museum experience. In this DT environment, IoT sensors can be installed to
manage the site’s microclimate and visitor interactions. For instance, integrating Bosch’s
Connected Building Solutions can help in preserving the structures and enhance the visitor
experience through interactive guides and augmented reality tours, bringing the vibrant
history of the village to life.
3.9.4. Rughabah Village
Rughabah Village, positioned northwest of Riyadh, stands as a testament to the
endurance of traditional construction techniques amidst urban development since 1669.
The village’s restoration efforts, particularly of its historic tower, have been periodic and
respectful of the original building methods. Here, DT can provide a cohesive platform for
managing future restorations and enhancing the site’s role as an open-air museum. Laser
scanning, using high-precision equipment like the Artec Leo 3D scanner, could capture the
texture and form of the tower and nearby castle remains, creating a detailed digital archive.
BIM, through platforms such as Bentley Systems’ MicroStation, could integrate these scans
with historical data, allowing conservators to plan restoration with an emphasis on material
authenticity and traditional techniques. Furthermore, the integration of ML could analyze
patterns from past restorations to ensure that any future interventions maintain minimal
legibility and reversibility—a key aspect of sustainable heritage conservation. For example,
employing Google’s AutoML Vision could help in identifying the most durable materials
and construction techniques that align with traditional methods. This predictive analysis
would also support the planning of maintenance schedules, optimizing resource allocation.
The implementation of IoT can contribute significantly to the village’s upkeep and
visitor experience. Environmental monitoring systems, such as the Onset HOBO data
loggers, can continuously assess conditions that may affect the structural integrity of the
site. Simultaneously, IoT-enabled wearables could offer visitors an interactive exploration
of the village, providing historical context and stories behind each structure, narrated
through synchronized mobile applications.
The interplay of BIM, laser scanning, ML, and IoT in these case studies from Saudi
Arabia illustrates a transformative approach to heritage conservation. Through incorpo-
rating specific devices and platforms, such as Autodesk Revit for BIM, Trimble TX8 and
FARO Focus3D for laser scanning, IBM Watson and TensorFlow for ML, and an array of
IoT sensors, the research paved the way for novel solutions that respect the authentic-
ity of heritage sites while embracing modernity. These technologies collectively form a
comprehensive DT framework, as can be seen in Figure 20, ensuring the longevity and
accessibility of cultural heritage for future generations. This flowchart encapsulates the
integrative approach necessary for effective heritage conservation, illustrating how various
technologies intersect and complement each other. By depicting the relationships between
BIM, laser scanning, ML, and IoT, Figure 20 clarifies the process of creating a digital twin,
which acts as a dynamic repository of knowledge for ongoing conservation efforts. Such
a framework not only supports the preservation of physical structures but also enhances
public engagement through immersive experiences. The visual clarity of this figure aids
stakeholders in understanding the potential of these technologies to transform traditional
practices, promoting a collaborative framework that respects both innovation and her-
itage. Through such innovative implementations, DT conserves history and redefines the
narrative of heritage preservation.

Heritage 2024,76467
Heritage 2024, 7, FOR PEER REVIEW 37
Figure 20. Flowchart depicting the integrative framework of digital twin technology for the preser-
vation of heritage buildings in the case study.
4. Discussion
The integration of emerging technologies such as digital twins (DTs), building infor-
mation modeling (BIM), 3D laser scanning, machine learning (ML), and the Internet of
Things (IoT) in the conservation of heritage buildings marks a significant shift towards
more informed and precise preservation practices. Expert interviews conducted as part of
this study underscore the value of these technologies in enhancing structural analysis and
restoration planning. The consensus among professionals in the architecture, engineering,
and construction (AEC) sector is clear; detailed digital representations and predictive an-
alytics can significantly improve the identification of vulnerabilities and the formulation
of conservation strategies. Nonetheless, the interviews revealed a gap in the widespread
adoption and understanding of these technologies, highlighting the necessity for focused
training and knowledge sharing within the conservation community.
To deepen the discussion on the integration of emerging technologies in heritage con-
servation, it is essential to explore the complex potentials and challenges associated with
these advancements. The insights gleaned from expert interviews and bibliometric analy-
sis indicate a transformative impact on preservation practices, yet a more nuanced exam-
ination is necessary. For instance, digital twins (DTs) have demonstrated significant po-
tential in real-time monitoring and predictive maintenance, allowing for proactive conser-
vation strategies. However, the implementation of DTs also faces challenges, such as the
need for extensive initial data collection and the integration of various data sources, which
can be resource-intensive. Case studies, such as the application of BIM in the restoration
of historic buildings, reveal both successes—like enhanced collaboration among stake-
holders and improved decision-making—and challenges, including difficulties in stand-
ardizing data formats and ensuring interoperability. By incorporating specific examples
of these technologies in action, the discussion can provide clearer insights into how they
can revolutionize heritage conservation while addressing the practical hurdles that must
be overcome. This approach will enrich the manuscript’s academic and practical value,
offering a comprehensive understanding of the landscape of digital innovation in heritage
preservation.
The bibliometric analysis of literature from 1996 to 2024 reveals an increasing aca-
demic and professional interest in the application of technology in heritage conservation.
This growing aention underscores the recognition of digital innovations as vital tools in
the preservation of cultural heritage. However, the analysis also points to a notable imbal-
ance, with a considerable focus on technological advancements at the expense of practical
applications and interdisciplinary methodologies. This imbalance suggests that while
Figure 20. Flowchart depicting the integrative framework of digital twin technology for the preserva-
tion of heritage buildings in the case study.
4. Discussion
The integration of emerging technologies such as digital twins (DTs), building infor-
mation modeling (BIM), 3D laser scanning, machine learning (ML), and the Internet of
Things (IoT) in the conservation of heritage buildings marks a significant shift towards
more informed and precise preservation practices. Expert interviews conducted as part of
this study underscore the value of these technologies in enhancing structural analysis and
restoration planning. The consensus among professionals in the architecture, engineering,
and construction (AEC) sector is clear; detailed digital representations and predictive an-
alytics can significantly improve the identification of vulnerabilities and the formulation
of conservation strategies. Nonetheless, the interviews revealed a gap in the widespread
adoption and understanding of these technologies, highlighting the necessity for focused
training and knowledge sharing within the conservation community.
To deepen the discussion on the integration of emerging technologies in heritage
conservation, it is essential to explore the complex potentials and challenges associated
with these advancements. The insights gleaned from expert interviews and bibliometric
analysis indicate a transformative impact on preservation practices, yet a more nuanced
examination is necessary. For instance, digital twins (DTs) have demonstrated significant
potential in real-time monitoring and predictive maintenance, allowing for proactive con-
servation strategies. However, the implementation of DTs also faces challenges, such as
the need for extensive initial data collection and the integration of various data sources,
which can be resource-intensive. Case studies, such as the application of BIM in the
restoration of historic buildings, reveal both successes—like enhanced collaboration among
stakeholders and improved decision-making—and challenges, including difficulties in
standardizing data formats and ensuring interoperability. By incorporating specific exam-
ples of these technologies in action, the discussion can provide clearer insights into how
they can revolutionize heritage conservation while addressing the practical hurdles that
must be overcome. This approach will enrich the manuscript’s academic and practical
value, offering a comprehensive understanding of the landscape of digital innovation in
heritage preservation.
The bibliometric analysis of literature from 1996 to 2024 reveals an increasing academic
and professional interest in the application of technology in heritage conservation. This
growing attention underscores the recognition of digital innovations as vital tools in the
preservation of cultural heritage. However, the analysis also points to a notable imbal-
ance, with a considerable focus on technological advancements at the expense of practical
applications and interdisciplinary methodologies. This imbalance suggests that while
technological innovation is progressing rapidly, its integration into heritage conservation
practices remains an area ripe for further exploration and development.

Heritage 2024,76468
Identified research gaps through this paper illuminate several critical areas needing at-
tention. Among these, the absence of standardized protocols for integrating laser scanning
data with DTs presents a significant barrier to creating unified digital representations of
heritage sites. Furthermore, the demand for detailed finite element models that accurately
reflect the structural characteristics of heritage buildings emphasizes the need for enhanced
data processing and modeling techniques. Challenges in automating Heritage Building
Information Modeling (HBIM) processes and integrating 360
◦
photography further under-
score the technological and methodological advancements required to advance the field of
heritage conservation.
In combining the insights from expert interviews, bibliometric analysis, and identi-
fied research gaps, this discussion underscores the transformative potential of emerging
technologies in heritage building conservation. While challenges remain, particularly in
the realms of technology adoption, interdisciplinary collaboration, and ethical considera-
tions, the path forward is clear. The ongoing restoration efforts at Notre Dame and similar
high-profile projects illustrate the practical application of digital technologies in heritage
conservation [
246
]. For instance, the use of 3D laser scanning and building information
modeling (BIM) [
247
] has been instrumental in creating detailed digital models that guide
restoration strategies, ensuring the preservation of both the physical and intangible aspects
of the structure. Additionally, recent studies have focused on the integration of digital
twins into heritage conservation practices, highlighting efforts to organize heritage data for
effective conservation planning [
102
]. These initiatives emphasize the necessity of adapting
current standards for heritage documentation to incorporate advanced digital tools, facil-
itating a more comprehensive approach to conservation that respects historical integrity
while leveraging technological advancements. By acknowledging such case studies, the
manuscript underscores the importance of practical applications of technology in enhanc-
ing heritage conservation efforts. Bridging the identified gaps necessitates a concerted
effort from technologists, conservationists, and policymakers to foster the development
and application of digital innovations in a manner that respects both the material and intan-
gible aspects of cultural heritage. Addressing these challenges through multidisciplinary
research and practice will enhance the effectiveness of conservation efforts and ensure the
sustainable preservation of heritage buildings for future generations.
5. Future Directions
The exploration of emerging technologies in the conservation of heritage buildings
has unveiled promising avenues for future research. As highlighted in the discussion, the
integration of DT, BIM, 3D laser scanning, ML, and the IoT offers transformative potential
for heritage conservation. However, to fully harness these technologies, several key areas
require further exploration and development.
Additionally, future research will involve selecting a specific case study in Saudi
Arabia to focus on the detailed potential of applying emerging technologies in heritage
conservation. This targeted exploration aims to illustrate how these technologies, including
digital twin (DT), building information modeling (BIM), and 3D laser scanning, can be
effectively utilized to preserve and restore cultural heritage. By concentrating on a specific
site, this research will provide comprehensive insights into the impact and applications
of these technologies, thereby enriching the overall discourse on digital innovations in
heritage preservation.
Firstly, future research should focus on developing standardized information pro-
tocols for integrating diverse data sources, such as laser scanning and IoT sensor data,
with DT and BIM frameworks. To address the noted gap regarding standardization in
the integration of digital tools in heritage conservation, it is essential to provide specific,
actionable recommendations for practitioners. A dedicated section will be included that
outlines detailed standardization protocols, including data protocols for the integration
of diverse digital tools, interoperability standards to ensure seamless data exchange, and
guidelines for effectively utilizing various technologies in conservation practices. These

Heritage 2024,76469
recommendations will enhance the practical utility of the manuscript and position it as
a pivotal resource for professionals in the field, facilitating the successful adoption and
implementation of digital innovations in heritage preservation. Standardization would
facilitate seamless data exchange and interoperability among different technological plat-
forms, enhancing the accuracy and efficiency of digital conservation efforts. Investigating
methodologies for automating the conversion of raw data into actionable insights within
Heritage Building Information Modeling (HBIM) processes could significantly streamline
conservation workflows.
Additionally, there is a crucial need for research into the ethical implications of using
digital technologies in heritage conservation. Developing ethical guidelines that address
issues of authenticity, representation, and accessibility in digital replication and restoration
is paramount. This includes considering the impact of these technologies on the perception
of historical integrity and exploring how they can be used responsibly to support rather
than supplant traditional conservation methods.
Another promising area for future research lies in the application of machine learn-
ing algorithms to predict deterioration and guide conservation strategies. Studies could
explore the development of predictive models that utilize environmental, structural, and
historical data to forecast potential risks and recommend preventative measures. This
approach would mark a significant shift towards proactive conservation, leveraging the
vast amounts of data generated by IoT sensors and other digital tools to anticipate and
mitigate deterioration before it occurs.
Lastly, the potential of augmented reality (AR) and virtual reality (VR) technologies
to enrich the heritage experience invites further investigation. Research should explore
innovative ways to use AR and VR for educational and engagement purposes, creating
immersive experiences that allow the public to connect with heritage sites in new and
meaningful ways. Additionally, studies could examine how these technologies can be
employed for remote conservation assessments, enabling experts to evaluate and plan
conservation interventions without the need for physical presence on site.
6. Limitations
This research, while comprehensive in its scope and analysis, encounters several limi-
tations that are inherent to studies of this nature. Firstly, the reliance on expert interviews,
though invaluable for gaining in-depth insights, is subject to the availability and the per-
spectives of the respondents. Despite efforts to include a diverse range of professionals
across the architecture, engineering, and construction (AEC) sector, the views represented
may not fully encompass the breadth of opinions and experiences within the field. This
limitation suggests a potential bias towards the technologies and methodologies that are
currently more visible or accessible to those within certain segments of the conservation
community.
Secondly, bibliometric analysis, while offering a robust overview of the academic
landscape surrounding the integration of emerging technologies in heritage conservation,
may not capture the entirety of relevant research and developments. Given the rapid pace
of technological advancement and the interdisciplinary nature of heritage conservation,
significant work may be published outside the traditional academic channels or in rapidly
evolving fields not thoroughly indexed by the databases utilized for this review.
Furthermore, the identification of research gaps, although comprehensive, is con-
strained by the scope of the literature reviewed. Innovations and challenges emerging from
practice, unpublished works, or those within closely related but distinct fields may not be
fully represented. This limitation underscores the necessity for ongoing review and synthe-
sis of literature across a broader spectrum of sources to ensure a holistic understanding of
the field.
Additionally, the research primarily focuses on the technological aspects of heritage
conservation, potentially underrepresenting the socio-cultural, economic, and ethical di-
mensions that play crucial roles in the preservation of heritage buildings. The complex

Heritage 2024,76470
interplay between technology and these broader considerations is essential for the devel-
opment of sustainable, ethical, and effective conservation strategies but may not be fully
explored within the confines of this study.
7. Conclusions
The exploration into the integration of emerging technologies such as DTs, BIM, 3D
laser scanning, ML, and the IoT within the field of heritage building conservation presents
an important shift towards safeguarding cultural heritage with unprecedented precision
and foresight. This review paper has integrated expert insights, bibliometric analyses, and
identified research gaps to illuminate the vast potential and the challenges inherent in
applying these technologies to the conservation of heritage buildings. The consensus among
experts and the trends identified through bibliometric analysis underscore a burgeoning
interest and optimism in the role of technology in preservation efforts. However, they
also highlight a critical need for standardization, ethical considerations, interdisciplinary
collaboration, and further research to overcome current limitations and fully realize the
potential of these digital tools in conservation practices.
Future research directions, as outlined, aim to bridge the identified gaps through
the development of standardized protocols, ethical frameworks, predictive models for
conservation, and innovative applications of AR and VR technologies. These efforts
promise to enhance the efficiency and effectiveness of conservation strategies as well
as to foster a deeper connection between the public and heritage sites, ensuring that these
treasures are preserved for future generations to appreciate and learn from.
While the path forward is fraught with challenges, the integration of digital innova-
tions into heritage conservation offers a new paradigm that marries the rich insights of the
past with the boundless possibilities of the future. By continuing to explore, refine, and
apply these technologies, it will be possible to stand on the cusp of revolutionizing the way
to preserve and interact with cultural heritage, ensuring its endurance and relevance in an
ever-evolving world.
Funding: This research was funded by Prince Sultan University, RIC Research and Initiative Center,
grant number: seed project n. 112, 2022–2023. The APC was funded by the RIC Research and Initiative
Center, Prince Sultan University, Riyadh, Saudi Arabia.
Institutional Review Board Statement: Informed consent was obtained from all subjects involved
in the study. The study was conducted following the Declaration of Helsinki and approved by the
Institutional Review Board of Prince Sultan University RIC Seed Project n. 112, 2022–2023.
Data Availability Statement: Data are available at Prince Sultan University, P.I. Responsible Dr. Silvia
Mazzetto.
Acknowledgments: The author gratefully acknowledges Prince Sultan University for covering the
article processing charges (APC) and providing financial incentives. Special thanks are also extended
to the Prince Sultan College of Architecture and Design, the Department of Architecture, and the
Sustainable Architecture Laboratory (SA Lab) for fostering a supportive research environment that
contributed to these outcomes.
Conflicts of Interest: The authors declare no conflicts of interest.
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