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Automation in manufacturing and assembly of industrialised construction
Li Xu
a,*
, Yang Zou
a
, Yuqian Lu
b
, Alice Chang-Richards
a
a
Department of Civil and Environmental Engineering, University of Auckland, Auckland 1010, New Zealand
b
Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland 1010, New Zealand
ARTICLE INFO
Keywords:
Automation
Industrialised construction
Manufacturing
Assembly
Systematic literature review
ABSTRACT
The integration of automation technologies has improved the efciency of industrialised construction (IC), yet a
deeper understanding of their effects on the manufacturing and assembly stages remains necessary. This paper
provides a systematic review of how various automation technologies inuence these key stages in IC, analysing
53 articles. It explores the deployment of 22 technologies, including the Internet of Things (IoT), deep learning,
digital twins, and robotics, and identies seven benets for IC: (1) interoperability, (2) scheduling optimisation,
(3) production traceability, (4) production safety, (5) manufacturability, (6) quality assurance, and (7) con-
structability. To further advance automation in IC, future research should address critical challenges, including
enhancing data quality, expanding empirical testing, exploring emerging technologies in depth, and integrating
fragmented workows. This article underscores the need of strategic technology deployment to seamlessly
integrate various processes in future construction practices, offering insights into the transformative potential of
automation.
1. Introduction
The construction sector has historically been characterised by low
productivity, environmental unfriendliness, and a lack of innovation
[1–3]. Many technologies and novel construction styles have been
introduced in response to these challenges [4]. One notable innovation
is industrialised construction (IC), which typically encompasses ve key
stages: design, factory-wide manufacturing, logistics, on-site assembly,
and operations and maintenance (O&M) [5,6]. This approach represents
a construction paradigm shift from on-site, structure-centric to off-site,
process-oriented. Such a transition facilitates enhanced quality con-
trol, diminishes waste, and expedites construction timelines [7]. IC
provides an economically viable and technically skilled alternative for
residential and commercial construction enterprises [8]. However,
studies have pointed out its developmental obstacles at different life-
cycle stages, including a scarcity of off-site practical knowledge among
designers [3,9], a lack of manufacturing standardisation, and complex-
ities in logistics and assembly planning [10,11].
To address these issues, there is a growing interest in the role of
automation in IC. Automation entails the utilisation of various tech-
nologies, machinery, or systems to efciently and consistently execute
repetitive and intricate tasks [12]. This approach holds considerable
promise for alleviating the current developmental bottlenecks of IC
[3,13]. When considering the IC lifecycle, the manufacturing and as-
sembly stages are particularly critical due to their signicant impact on
efciency, quality, and resource consumption [8,14]. The
manufacturing stage involves production personnel. They make various
building components on specialised lines, following the established
design and quality control plans. Moving to the assembly stage, workers
are required to install various building components and systems adeptly,
ultimately yielding buildings that align with specied requirements.
These two stages consume the majority of labour and material resources
and are pivotal in determining the success of IC projects [15–17]. The
importance of these stages is further underscored by the developmental
bottlenecks they face. Inconsistent component quality and low produc-
tion efciency in manufacturing can complicate assembly and increase
project risks [18]. Moreover, the lack of standardisation and high en-
gineering costs associated with IC pose signicant challenges [19].
Automation offers a promising solution to these issues by standardising
processes, improving coordination, and reducing reliance on skilled la-
bour and material wastage, thereby lowering costs and enhancing out-
comes [20,21].
Recognising the potential of automation in overcoming these chal-
lenges, substantial research has focused on incorporating automation
technologies to enhance efciency, reduce costs, and improve quality
and safety in both manufacturing and assembly stages. Automation
* Corresponding author.
E-mail address: lxu480@aucklanduni.ac.nz (L. Xu).
Contents lists available at ScienceDirect
Automation in Construction
journal homepage: www.elsevier.com/locate/autcon
https://doi.org/10.1016/j.autcon.2024.105945
Received 2 May 2024; Received in revised form 4 December 2024; Accepted 16 December 2024
Automation in Construction 170 (2025) 105945
Available online 28 December 2024
0926-5805/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (
http://creativecommons.org/licenses/by/4.0/ ).
ensures precision and consistency in manufacturing, while in assembly,
it streamlines installation and reduces errors. Technologies such as ro-
botics, IoT, and Articial Intelligence (AI) signicantly enhance quality
and efciency in both stages, making IC more viable and scalable [6]. By
alleviating the key bottlenecks in manufacturing and assembly, auto-
mation is playing a crucial role in advancing the adoption and effec-
tiveness of industrialised construction.
Despite these advancements, there is a noticeable gap in compre-
hensive reviews specically focusing on the manufacturing and assem-
bly stages within IC. Existing review papers on IC automation, as shown
in Table 1, can be categorised into three groups. Firstly, some reviews
[6,13,20–27] provide a statistical overview of the use of digital tech-
nologies in existing literature, but they often lack a detailed analysis of
how these automation technologies are integrated into the
manufacturing and assembly processes of IC. Secondly, a portion of the
literature [17,28–30] delves into the applications of AI and robotics in IC
stages, but tends to overlook other critical automation technologies,
such as the enhancement of production data collection through sensors
and IoT. Lastly, a subset of reviews focuses on specic aspects such as
knowledge management [9], integration with lean construction princi-
ples [31,32], and automation within specic materials like timber
frames or precast concrete [33–35]. To address the above limitations,
this study endeavours to provide a thorough review of the use of auto-
mation technologies in the manufacturing and assembly stages of IC,
along with an analysis of their positive impact on the benets of IC. The
focal point of this research revolves around three fundamental
questions:
Q1. What are the state-of-the-art automation technologies in the
manufacturing and assembly stages of IC?
Q2. What benets of IC are enhanced by the implementation of these
automation technologies?
Q3. What are the limitations of current automation in manufacturing
and assembly and future research directions?
This article is structured into six primary sections. Section 2 outlines
the systematic review methodology. Section 3 presents the bibliometric
analysis results. Section 4 examines the deployment of different auto-
mation technologies in both manufacturing and assembly stages and the
benets they yield, employing template analysis. Section 5 discusses the
benets and challenges of automating IC and provides recommendations
for future research. Lastly, Section 6 concludes the main ndings of this
review.
2. Methodology
To reveal the current deployment status of automation in the
manufacturing and assembly stages of IC, as well as to identify pro-
spective avenues for future research, this study employs a systematic
literature review (SLR) method. A SLR follows predened procedures to
evaluate multiple studies, aiming to provide clear and evidence-based
answers to the posed inquiries [36]. To ensure accurate collection of
literature and strict synthesis and analysis of data, the SLR method in
this study integrates the Preferred Reporting Items for Systematic Re-
views and Meta-Analyses (PRISMA) guidelines, bibliometric analysis,
and template analysis, as shown in Fig. 1.
2.1. Identication of relevant work
The PRISMA guidelines are a set of evidence-based criteria designed
to enhance the clarity and transparency of reporting in systematic re-
views and meta-analyses [37]. It mitigates potential oversights and
subjective biases inherent in manual literature collection [38]. Adhering
to the PRISMA procedure, this study extracted literature related to the
research theme from databases. As shown in Fig. 2, the main processes
include literature identication, screening, and snowball search.
Selecting appropriate search terms is critical for ensuring the quality and
efciency of the literature search process. After analysing the keyword
selection strategies employed in 11 prior reviews
[6,7,13,20,21,24,25,29,39–41], we arrived at the denitive set of key-
words associated with IC (see Table 2). The term “industrialised con-
struction” encompasses a broad range of synonymous expressions, such
as “off-site construction”, “modular construction”, and “prefabricated
construction” [42]. These terms are frequently used interchangeably in
practice, though they have distinct meanings within the broader concept
of industrialised construction [9,43]. Specically, off-site construction is
a subset of industrialised construction, which includes both modular and
prefabricated methods [5,44]. In the context of automation-related
keywords, terms such as “automat*”, “robot*”, “articial intelligence”,
“building information modelling”, “digital twins”, “virtual reality”, “AI”,
“BIM”, “IoT”, “RFID”, and “sensor” are found to be pertinent and
adequate to the study’s topic. Among these terms, certain ones are
abbreviated (e.g., “articial intelligence” to “AI”) or inclusive (e.g.,
“RFID” and “sensor”) to match more related literature. To align with the
research focus on manufacturing and assembly, terms like “manu-
factur*”, “schedul*”, “plan*”, and “assembl*”, with the wildcard char-
acter (*), have also been considered.
The Scopus and Web of Science databases were selected for literature
querying, as they provide exhaustive peer-reviewed articles in the ar-
chitecture, engineering and construction sector [45,46]. Two search
syntaxes were formulated, which consist of a combination of eld tags,
Boolean operators (i.e., “AND” and “OR”) and terms. Subsequently,
literature gathered from preliminary searches undergoes ltering based
on document attributes such as publication date, article language, and
document type. This ltering restriction is known as “bibliographical
criteria” [47]. Since 2000, there has been a growing amount of research
on automation technologies like robotics and AI in the construction in-
dustry [48,49]. To ensure the acquisition of a high-quality and
manageable sample pool [6], the scope of the review was limited to
peer-reviewed English journal articles published between 2000 and
2024, spanning 25 years. Notably, both database search engines allow
for the addition of bibliographical criteria tags, enabling simultaneous
ltering during the initial search phase. This approach eliminates the
tedious manual ltering work. For example, in this study, the search
string used on Scopus is detailed in Table 2, conducted on March 17,
2024. As shown in Fig. 2, during the literature identication stage, 1644
articles were located, including 1287 articles from the Scopus database
and 357 articles from the Web of Science (WoS) database. By imple-
menting the lter tags, 1046 articles that did not meet the biblio-
graphical criteria were ltered out, and after merging the search results
from the two databases, 200 duplicate articles were identied. Conse-
quently, a preliminary sample of 398 papers was procured for subse-
quent screening.
During the identication stage, the database search engines initially
screened papers based on established search terms and bibliographic
criteria. Although these papers aligned with the predened parameters,
they included a signicant volume of publications that were not relevant
to the research topic. To yield a manageable collection of publications
for the ultimate review, a meticulous screening process is imperative.
Every document selected for review must adhere to the following in-
clusion criteria: (1) the paper must propose, describe, or apply a method
or technology aimed at enhancing the automation of IC, which may
encompass robotics, IoT, AI, advanced sensors, software frameworks, or
combinations thereof, and (2) the method or technology must be
applied, at a minimum, to one of the following stages in the lifecycle of
prefabricated buildings: factory-wide manufacturing of prefabricated
components and on-site assembly. In contrast, literature meeting the
following exclusion criteria are immediately discarded: (1) papers have
no or little contribution to automation in IC, and (2) papers focus on the
stage of planning, design, logistics, or O&M of IC.
The screening stage consists of three sequential steps [47]. To
commence, the abstracts of these articles underwent a quick skim,
individually assessed against the predened inclusion and exclusion
criteria. During this scrutiny, 211 articles were identied as unrelated to
L. Xu et al.
Automation in Construction 170 (2025) 105945
2
Table 1
Summary of literature review on industrialised construction and automation.
No. Title Year Notes Technologies reviewed Reference
Category 1: statistical overviews of digital technology use
1 Building information modelling for off-site
construction: review and future directions
2019 Provides an in-depth analysis of the application of BIM
in off-site construction (OSC), exploring its use in the
design, logistics, and construction stages.
BIM, IoT, laser scanning [20]
2 A systematic review of digital technology
adoption in off-site construction: current
status and future direction towards Industry
4.0
2020 Provides a systematic review of digital technology
adoption in OSC, focusing on BIM, RFID, IoT, and VR.
BIM, RFID, GPS, IoT, GIS, sensors, AR, VR,
photogrammetry, laser scanning, AI, 3D
printing, robotics, big data, blockchain
[21]
3 An integrated review of automation and
robotic technologies for structural
prefabrication and construction
2020 Explores the integration of automation and robotic
technologies in structural prefabrication, including
design, robotic manufacturing, autonomous
transportation, and automatic assembly.
Robotics, BIM, CAD/CAM, CNC,
autonomous vehicles, UAVs
[22]
4 Mapping the knowledge domains of
emerging advanced technologies in the
management of prefabricated construction
2021 Maps the knowledge domains of emerging technologies
such as BIM, IoT, RFID, and 3D printing in prefabricated
construction management.
BIM, IoT, RFID, 3D printing, cloud
computing, laser scanning, blockchain, AI
[23]
5 A systematic review of emerging
technologies in industrialised construction
2021 Reviews the application of emerging technologies,
including BIM, IoT, and robotics, in industrialised
construction. It highlights research gaps on certain
structural systems (e.g., wood and steel frames) and
underexplored technologies like 3D printing.
BIM, IoT, 3D printing, robotics [6]
6 Towards a more extensive application of off-
site construction: a technological review
2022 Focuses on emerging technologies such as BIM, IoT,
AR/VR, and 3D printing in OSC, examining their
implementation in design, logistics, and maintenance.
BIM, AR/VR/MR, 3D printing, cloud
computing, GIS, RFID, smart sensing, IoT,
AI, robotics
[13]
7 Automating the modular construction
process: a review of digital technologies and
future directions with blockchain technology
2022 Examines digital tools and technologies in modular
construction, with a particular focus on blockchain
technology. It highlights gaps in the adoption of digital
tools, especially in the prefab transportation stage.
BIM, RFID, machine learning, genetic
algorithms, blockchain
[24]
8 Digital technologies in offsite and
prefabricated construction: theories and
applications
2023 Discusses the application of digital technologies, such as
BIM, IoT, and blockchain, in OSC. It also covers Design
for Manufacturing and Assembly (DfMA) and supply
chain integration.
BIM, IoT, AR/VR, Digital Twins, 3D
printing
[25]
9 Using BIM in the safety risk management of
modular construction
2022 Explores the use of BIM in enhancing safety risk
management for modular construction, with a focus on
improving safety inspections, crane management, and
worker training.
BIM [26]
10 The convergence of BIM, AI and IoT:
reshaping the future of prefabricated
construction
2024 Investigates the integration of BIM, AI, and IoT in
prefabricated construction, focusing on automated
design developments, robotic fabrication, and
information sharing.
BIM, AI, IoT [27]
Category 2: AI and robotics applications
11 Articial intelligence and robotics for
prefabricated and modular construction: a
systematic literature review
2022 Examines the application of AI and robotics in
prefabricated and modular construction, focusing on
machine learning, robotics, and evolutionary
computation techniques. It identies key gaps in
integrating AI and robotics in construction.
AI, robotics [28]
12 Computer vision applications in offsite
construction
2023 Focuses on the use of computer vision technologies in
offsite construction, including applications such as
progress monitoring, quality assurance, and ergonomic
analysis.
Computer vision techniques [29]
13 Articial intelligence for production,
operations and logistics management in
modular construction industry: a systematic
literature review
2024 Explores the application of AI in production, operations,
and logistics management within the modular
construction industry, identifying AI technologies such
as machine learning for process optimisation.
AI techniques like machine learning,
reinforcement learning
[30]
14 Human-robot collaboration for modular
construction manufacturing: review of
academic research
2024 Discusses the role of human-robot collaboration (HRC)
in modular construction manufacturing, highlighting its
benets, challenges, and potential opportunities.
AGVs, robotic arms, specialised robots,
exoskeletons, UAVs
[17]
Category 3: specic focus areas (knowledge management, lean integration, and materials)
15 Knowledge management for off-site
construction
2024 Focuses on knowledge management (KM) practices in
OSC. It identies gaps in the systematic integration of
KM with emerging AI technologies.
BIM, AI, ontology, VR [9]
16 A review on the interactions of robotic
systems and lean principles in offsite
construction
2022 Explores the interactions between robotic systems and
lean construction principles in OSC, identifying a gap in
the application of robotic systems. It focuses on the
intersection of robotics, lean principles, and
automation.
BIM, robotic systems, AGVs, UAVs [31]
17 Evaluation of lean off-site construction
literature through the lens of Industry 4.0
and 5.0
2023 Evaluates lean OSC literature using Industry 4.0 and 5.0
principles, with a focus on automation, resilience, and
sustainability.
Lean tools, BIM, IoT [32]
18 State of practice of automation in precast
concrete production
2021 Discusses the current state of automation in precast
concrete production, with a focus on automated
processes such as concrete mixing, reinforcement cage
Robotics, CNC, BIM, 3D printing [33]
(continued on next page)
L. Xu et al.
Automation in Construction 170 (2025) 105945
3
the study’s subject and excluded. This left a subset comprising 187 re-
ports (PRISMA terminology), which were then subjected to compre-
hensive full-text reading and nal assessment for eligibility. Adhering to
the established inclusion and exclusion criteria, a combined total of 154
articles were determined to be ineligible. Lastly, 33 articles that had
successfully traversed the entire rigorous screening process. To prevent
the omission of valuable studies that may not have been discoverable
through keyword searches, a snowballing search strategy was imple-
mented iteratively to broaden the scope of research-related literature.
Initially, 33 papers from the previous screening process served as the
basic set. Through both forward snowballing, to see where those papers
were cited, and backward snowballing, to see what citations those pa-
pers used to build their case [50], an additional 20 papers that met the
inclusion criteria were subsequently identied. Consequently, a total of
53 papers collected from these two sources were selected for the review,
as depicted in Appendix A.
2.2. Summary of evidence
Following the literature search, bibliometric analysis and template
analysis are used to summarise the evidence from the selected literature.
Bibliometric analysis is a valuable tool for researchers seeking insights
into the historical evolution of a specic scientic discipline and for
shaping future research trajectories [51]. It offers perceptions of the
publication trends within a certain subject, the signicance of various
research products, and the relationships between academics and orga-
nisations [52]. In this study, the bibliometric analysis provides a pre-
liminary overview of automation implementation in the manufacturing
and assembly stages of IC. This analysis encompasses several compo-
nents: trends in annual publications, identication of inuential co-
authors and their afliations, and patterns of keyword co-occurrence
within the selected literature. These elements collectively establish a
foundation for the more detailed examination in template analysis.
Template analysis is a structured methodology for analysing specic
research themes that can synthesise qualitative data from multiple
sources [53]. By developing a coding template and continually rening
it during the coding process, e.g., adding or removing themes/cate-
gories, the researcher is able to organise and compare the results of a
variety of studies in order to identify common themes, gaps, and chal-
lenges in existing research, as well as possible directions for future
research [54,55]. As Table 3 illustrates, a coding template was devel-
oped to extract pertinent data from the selected publications. The
extracted data underpin the examination of technologies and benets in
Section 4 and inform the discussion on challenges and future research
directions in Section 5.
3. Bibliometric analysis
3.1. Publication trends
Figure 3 presents the initial statistical ndings from analysing the 53
selected articles. The publication data spans from 2011 to 2024, and the
increasing number of documents indicates the growing interest in
automating IC. Prior to 2016, only a total of 4 articles reviewed were
published. Subsequently, there was a substantial surge in publication
volume, peaking at 16 articles in 2022. This signicant rise in scholarly
output can be ascribed to the expanding inuence of Industry 4.0 con-
cepts and the rapid development of diverse automation technologies.
Remarkably, approximately 62 % of these articles were published be-
tween 2020 and 2024, signifying a concentrated period of research ac-
tivity. Projection suggests that by the conclusion of 2024, the number of
related publications is expected to exceed 14, indicating a continued and
robust expansion in the eld. This growth underscores the advancing
momentum in the automation manufacturing and assembly stages in IC.
These articles originate from a diverse range of 12 journals. Table 4
provides the list of journals for all reviewed literature, with the top three
journals representing 77.4 % of the total publications. The journal
Automation in Construction leads the list with a substantial 34 published
Table 1 (continued )
No. Title Year Notes Technologies reviewed Reference
production, and shuttering. It emphasises the
sustainability and economic benets of automation.
19 Automated and robotized processes in the
timber-frame prefabrication construction
industry: a state of the art
2022 Discusses the level of automation and robotisation in
timber-frame prefabrication, identifying available
technologies and barriers to their adoption. It highlights
equipment such as CNC machines, framing stations, and
nailing bridges.
CNC, robotic arms, BIM [34]
20 Review on automated quality inspection of
precast concrete components
2023 Focuses on automated methods for quality inspection of
precast concrete components, utilising laser scanning
and computer vision techniques. It identies research
gaps and emphasises future directions in data collection
and analysis.
3D laser scanning, computer vision, deep
learning
[35]
Fig. 1. Overview workow of this SLR study.
L. Xu et al.
Automation in Construction 170 (2025) 105945
4
articles out of a total of 53. Following this, the Journal of Construction
Engineering and Management ranks second with four articles, while
Computers in Industry holds third place with three peer-reviewed articles.
These ndings underscore the prominent role of these journals in
disseminating research related to IC automation.
In the realm of citation records, as delineated in Table 4, the journal
Automation in Construction has amassed over 2000 citations. The articles
from this journal are widely cited and serve as foundational references
for subsequent research. Table 5 delves into the granular details of in-
dividual article citations. Leading the citation rankings is the paper titled
“Prefabricated construction enabled by the Internet-of-Things” [56]
published in 2017, garnering an impressive 285 citations. Following
closely, references [57,58] command notable citation counts of 269 and
200, respectively. These citation metrics underscore the inuential
contributions of these specic articles to the research landscape in IC
automation.
3.2. Inuential co-authors and afliation
The primary objective of conducting inuential collaborators and
Fig. 2. PRISMA literature identication, screening, and snowballing search process.
Table 2
Strings for advanced search of the Scopus database engine.
Items Strings
Topic tag TITLE-ABS-KEY((
Prefabricated
Construction
“off-site construction” OR “offsite construction” OR
“modular construction” OR “modular building” OR
“prefabricated construction” OR “prefabricated building”
OR “industrialised construction” OR “industrialised
building”
) AND (
Stages “manufactur*” OR “schedul*” OR “plan*” OR “assembl*”
) AND (
Automation
“automat*” OR “robot*” OR “articial intelligence” OR
“building information modelling” OR “digital twin” OR
“virtual reality” OR “AI” OR “BIM” OR “IoT” OR “RFID” OR
“sensor”
Bibliographical
Criteria
)) AND PUBYEAR >1999 AND PUBYEAR <2025 AND
(LIMIT-TO(LANGUAGE, “English”)) AND (LIMIT-TO
(DOCTYPE,”ar”))
Table 3
Categories of the developed coding template.
Categories Description
General
information
Basic details: author, title, journal, year, source, country/
region)
Specic to IC Focus on industrialised construction: stages, enhanced
benet, material type
Adopted
technologies
Technologies implemented for automation
Research goals Objectives and aims of the study
Automated
scenarios
Realised automated operations or processes
Data types Types of data utilised for automation
Decision-making The basis for decision-making from strategic to technical
levels
Contributions Innovations and methodologies introduced by the research
Implications Potential impact on practice or future research
Limitations Acknowledged limitations or constraints
Further research Future investigations emerged from the studies
L. Xu et al.
Automation in Construction 170 (2025) 105945
5
afliation analysis is to identify researchers and institutions who have
made signicant contributions to the eld so as to unveil inuential core
research teams and monitor the research trajectories of prominent
scholars [66,67]. As shown in Fig. 4, researchers primarily afliated
with the University of Alberta, the University of Hong Kong, and the
Hong Kong Polytechnic University authored the majority of the papers
in this review, collectively representing 47.2 % of the total. Notably,
contributions from the University of Alberta and the University of Hong
Kong were particularly signicant, accounting for 28.3 % and 13.2 %,
respectively, highlighting their prominent roles in advancing the auto-
mation of IC. In terms of co-authors, in this study, a total of 53 selected
articles were authored by a collective of 128 distinct researchers. As
shown in Fig. 5, M. Al-Hussein from the University of Alberta was
involved in the largest number of studies, amounting to 12 papers,
which is twice as many as the second ranked. The timeline of publica-
tions of the top 10 authors involved in the highest number of publica-
tions is shown in Fig. 6, which is consistent with the publication trend
shown in Fig. 3, where their research work is mainly concentrated after
2016 and continues to contribute to the automation of IC.
3.3. Co-occurrence of keywords
Keyword co-occurrence analysis helps identify common research
themes and their interrelationships. To facilitate this analysis, it is
crucial to standardise the terminology used by diverse researchers. This
Fig. 3. Annual publication trend of selected articles.
Table 4
List of journals for all reviewed literature.
Source title Documents Total
citations
Average
citations
Automation in Construction 34 (64.2 %) 2061 60
Journal of Construction Engineering
and Management 4 (7.5 %) 26 6
Computers in Industry 3 (5.7 %) 183 61
Journal of Cleaner Production 2 (3.8 %) 381 190
International Journal of Construction
Management 2 (3.8 %) 68 34
Journal of Computing in Civil
Engineering 2 (3.8 %) 66 33
Expert Systems with Applications 1 (1.9 %) 88 88
Journal of Manufacturing Systems 1 (1.9 %) 21 21
Advanced Engineering Informatics 1 (1.9 %) 12 12
Journal of Architectural Engineering 1 (1.9 %) 10 10
IEEE Transactions on Automation
Science and Engineering 1 (1.9 %) 3 3
Engineering Applications of Articial
Intelligence 1 (1.9 %) 2 2
Table 5
The top 10 articles with the highest number of citations.
Title Journal Afliation Citations Reference
Prefabricated
construction
enabled by the
Internet-of-Things
Automation
in
Construction
The University
of Auckland
285 [56]
An Internet of
Things-enabled
BIM platform for
on-site assembly
services in
prefabricated
construction
Automation
in
Construction
Shenzhen
University
269 [57]
Schedule risks in
prefabrication
housing
production in
Hong Kong: a
social network
analysis
Journal of
Cleaner
Production
The Hong
Kong
Polytechnic
University
200 [58]
Integrating RFID and
BIM technologies
for mitigating risks
and improving
schedule
performance of
prefabricated
house construction
Journal of
Cleaner
Production
Shenzhen
University
181 [59]
Automated
dimensional
quality assurance
of full-scale
precast concrete
elements using
laser scanning and
BIM
Automation
in
Construction
University of
Cambridge
168 [60]
Cloud asset-enabled
integrated IoT
platform for lean
prefabricated
construction
Automation
in
Construction
Harbin
Institute of
Technology
129 [61]
Integrated
production
planning and
control system for
a panelized home
prefabrication
facility using
simulation and
RFID
Automation
in
Construction
University of
Alberta
116 [62]
Framework for
modelling
operational
uncertainty to
optimise offsite
production
scheduling of
precast
components
Automation
in
Construction
Shanghai Jiao
Tong
University
112 [63]
Automated
dimensional
quality assessment
of precast concrete
panels using
terrestrial laser
scanning
Automation
in
Construction
Korea
Advanced
Institute of
Science and
Technology
107 [64]
Automated quality
assessment of
precast concrete
elements with
geometry
irregularities using
terrestrial laser
scanning
Automation
in
Construction
The Hong
Kong
University of
Science and
Technology
107 [65]
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standardisation process involves several situations, such as substituting
synonymous terms (e.g., replacing “modular building” with “modular
construction”), unifying variant spellings (e.g., reconciling “US” and
“UK” spellings), addressing different word forms (e.g., singular and
plural forms), and handling hyphenations (−) and abbreviations (e.g.,
abbreviating “Building Information Modelling” to “BIM”).
Figure 7 presents a network visualisation of the keyword co-
occurrence map generated using VOSviewer [68]. In this representa-
tion nodes represent keywords with their size proportional to the fre-
quency of occurrence. The connecting lines between nodes signify the
relationships between these keywords with closer nodes indicating
stronger correlations. Remarkably the entire keyword network is
organised into several clusters with the central keyword “BIM” encom-
passed by related keywords like “industrialised construction,”
“manufacturing,” and “automation.” This assertion highlights the
pivotal role of building information modelling (BIM) as a leading digital
technology within the construction industry. Additionally the clusters
each represented by distinct colours on the periphery
delineate various technology application areas. For instance clusters
include “genetic algorithms” associated with scheduling optimisation
“computer vision” and “deep learning” linked to production traceability
“laser scanning” pertaining to quality assurance and “ergonomic anal-
ysis” in the context of production safety. The literature cluster derived
from keyword analysis serves as the foundational element for catego-
rising literature in the template analysis section. Following this a careful
application of induction and expansion guided by the interconnections
among the literature will lead to the creation of a denitive knowledge
system.
4. Review of the state-of-the-art automation technologies and
their benets in manufacturing and assembly of IC
This section synthesises ndings from template analysis to depict the
current state of automation in IC. It addresses critical aspects of the
technologies employed and their associated benets. This analysis fa-
cilitates the identication of prevalent obstacles that hinder the inte-
gration of automation processes during the manufacturing and assembly
stages of IC. Additionally, it establishes a foundation for proposing po-
tential avenues for future research.
4.1. Overview
Figure 8 illustrates a Sankey chart that categorises the content of the
selected literature based on keyword analysis and template analysis,
offering an overview of the primary research motivations and technol-
ogies employed in these works. The four columns, arranged from left to
right, include author, adopted technologies, enhanced benets, and
afliation. In the author column, the chart presents the quantity and
combination of technologies employed in each study. The second and
third columns signify that a total of 22 technologies have contributed to
Fig. 4. Leading institutions by number of publications.
Fig. 5. Leading authors by number of publications.
Fig. 6. Timeline of prolic authors’ publications.
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realising 7 benets in IC through diverse choices and combinations.
These studies were mainly conducted by the institutions detailed in the
fourth column. The technologies utilised, and the benets enhanced will
undergo thorough analysis and discussion in the subsequent subsections.
4.2. Technologies adopted
The Sankey diagram (Fig. 8) visually depicts the technology adoption
strategies employed by reviewed literature, encompassing 22 distinct
technologies grouped into four main categories. This subsection delves
into the nuanced roles of these technologies in automating
manufacturing and assembly stages, directly addressing the research
question Q1.
4.2.1. Sensors and measurement devices
Sensors and measurement devices enable the detecting and moni-
toring of the status of people, materials, and equipment, serving as
crucial data sources for the automation of manufacturing and assembly
stages. Among the 53 papers reviewed, 27 incorporated the technologies
delineated in Table 6 to differing extents. This nding underscores the
signicance of data collection processes in the implementation of
automation technologies. Notably, radio frequency identication
(RFID), cameras and laser scanning were prominently employed in these
studies.
Obtaining data regarding the identity and location is a prerequisite
for enabling object state analysis. A lightweight method is to scan
barcodes or quick-response (QR) codes afxed to prefabricated com-
ponents [73]. Despite the simplicity of this optical method, its applica-
tion has been limited to only one study due to its reliance on a direct line
of sight. In contrast, RFID technology, renowned for its wireless iden-
tication and tracking capabilities for tagged assets, emerges as the most
prominently utilised hardware technology. 12 articles specically
acknowledge RFID as the predominant technology for location and
identication purposes. In addition, given the advantages of the Global
Positioning System (GPS) for wide-area localisation, it was used in 4
studies as a supplementary positioning technology IC [59].
The physical properties of the target are usually collected by me-
chanical sensors. The inertial measurement unit (IMU) demonstrates
superior accuracy in delivering objective and varied data [88]. IMU can
be incorporated into wearable devices to facilitate ergonomic analysis
[76]. Nevertheless, its application is still limited, having been used in
only one study. Stress and strain sensors can be used to monitor the
mechanical deformation or force. However, within the scope of this
review, only one article has focused on their application in the assembly
of prefabricated components.
Recently, the demand for image data for AI has turned cameras into
an integral part of automation technologies, allowing this technology to
be deployed in a large number of studies (This review encompasses 5
such studies). Similarly, due to the inherent benets of non-destructive
and contactless inspection [64], there is a notable surge in research
activity centred around laser scanning methodologies. Specically,
among the reviewed literature, 8 articles emphasised the use of laser
Fig. 7. Keyword co-occurrence mapping.
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scanning to capture the external geometry of precast concrete
components.
4.2.2. Data interconnectivity
Data interconnectivity serves as the foundational element for the
analysis, decision-making, and execution of production and assembly
activities. The IoT, facilitating the connection of diverse sensors and
devices, ensures response and operation based on real-world data. This
review encompasses 9 studies dedicated to the deployment of IoT. In the
context of studies pertaining to blockchain and digital twins, 3 and 4
studies are identied, respectively. Fig. 9 illustrates the association be-
tween these three technologies and the potential advantages of their
collective application, and some of the studies also integrate two of
them. However, research on integrating these technologies to achieve
automation in IC remains limited. The singular documented instance
involves the concurrent use of the IoT and blockchain to enhance data
security and ensure transparent data transmission and sharing, as
detailed in reference [70]. Notably, there remains a signicant research
gap in combining blockchain with digital twins, IoT with digital twins,
and the simultaneous application of all three technologies.
4.2.3. Modelling and virtual environment
BIM is one of the most widely cited technologies in the construction
eld. As delineated in Table 7, nearly half of the studies (26 out of 53) in
the selected review literature used BIM as a basis for automation. By
establishing a BIM digital environment, the manufacturing and assembly
stages can be represented through VR, thereby affording enhanced av-
enues for experiential understanding, decision-making, and execution
Fig. 8. Overview of the adoption of different automation technologies.
Table 6
Application of sensors and measurement devices.
Technology Data type Application Benets Reference(s)
RFID
Identity and
location data
Monitor the status of products, materials, or machines Interoperability; Production traceability;
Quality assurance; Constructability [56,57,59,61,62,69–75]
QR code or
barcode Track product location and process information Production traceability; Constructability [73]
GPS Locate prefabricated components Interoperability; Constructability [57,59,69,71]
IMU Physical
properties
Collect acceleration, tilt, and magnetic eld around
human bodies, machinery, or hoisted components Constructability; Production safety [76]
Stress or strain
sensors
Monitor the stress-strain data of structural members to
identify damage Quality assurance [77]
Camera Geometry and
graphic
Monitor and track the behaviour of workers during the
manufacturing and assembly stages
Production safety; Production traceability;
Manufacturability; Constructability [78–82]
Laser scanning Scan the geometric shape of prefabricated components Quality assurance [60,64,65,83–87]
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[89]. However, the utilisation of VR in IC is presently constrained, with a
mere 3 pertinent studies identied.
Acknowledging the paucity of available case studies and the inherent
challenges in capturing analytically rich data, researchers have turned to
virtual prototyping capable of integrating diverse modelling technolo-
gies [88]. This approach facilitates the simulation of manufacturing and
assembly environments, enabling the cost-effective exploration of
various workows. Within the selected articles, 7 papers delved into the
deployment of virtual prototyping to enhance teamwork, ensure safe
production practices, and bolster constructability. Furthermore, 3
studies concentrated on ergonomic analyses of manufacturing stages,
leveraging the immersive environments and motion modelling benets
afforded by VR and virtual prototyping.
4.2.4. Robotics and articial intelligence
In the realm of robotics, this paper concentrates on the utilisation of
specialised robots and robotic arms, excluding the broader category of
robots in IC, such as exoskeletons, automated guided vehicles, and un-
manned aerial vehicles. Specialised robots, characterised by semi-
automatic attributes [93], typically derive from equipment designed
for specic production processes, aiming to alleviate the workload on
human operators [81,104]. As illustrated in Fig. 10, in the collected
literature, 6 studies focused on specialised robots used to assemble
frame panels. Meanwhile, robotic arms with replaceable end-effectors
emerge as a particularly promising application in IC, with 5 studies
focusing on this aspect.
AI technology, owing to its robust learning capabilities derived from
existing data, assumes a pivotal role in facilitating real-time intelligent
monitoring and decision-making in IC. Of the 53 literature articles
reviewed, 14 have extensively delved into research within this domain.
These technologies are harnessed for monitoring, analysing, and opti-
mising production and assembly stages, utilising collected data to offer
decision support to various stakeholders.
4.3. Benets of automation technology to manufacturing and assembly
This subsection aims to elucidate how existing studies have utilised
various technologies to enhance the benets of the manufacturing and
Fig. 9. Internet of things, blockchain, and digital twins: interconnections and applications in industrialised construction.
Table 7
Application of modelling and virtual environment.
Technology Application Benets Reference(s)
BIM
Provide platforms for project coordination,
production optimisation, construction planning, and
service management
Interoperability, Production traceability, Production
scheduling, Manufacturability, Quality assurance,
Constructability,
[56–61,65,69,70,74,75,77,81,83,86,87,90–99]
VR Provide virtual environments for manufacturing and
assembly stages Interoperability, Production safety, Constructability [57,76,100]
Ergonomic
analysis
Assess the risks of manual operations and analyse
the rationality of workplace layout Production safety [88,101,102]
Virtual
prototyping
Create digital environments for workow
simulation and analysis Production safety, Constructability [88,90,97,100–103]
Fig. 10. Application of articial intelligence and robotics in industrialised
construction.
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assembly stages of IC in order to address research question Q2. Drawing
from bibliometric analysis and template analysis, as depicted in Fig. 11,
this subsection identies seven key areas where automation innovations
are currently applied in the manufacturing and assembly stages of IC,
thus serving as a crucial groundwork for recognising prevailing devel-
opment challenges and suggesting future research directions.
4.3.1. Interoperability
Interoperability demonstrates the ability to seamlessly process in-
formation and data across disparate departments or technology systems
[105]. However, IC involves various stakeholders who typically manage
information, resources, and production activities independently. When
data or information lacks universality across different technology sys-
tems or faces prolonged unavailability for sharing within the same sys-
tem, it may lead to decient interoperability [106]. Substantial research
persists in endeavours to augment the interoperability of IC. These en-
deavours are approached from both horizontal and vertical perspectives.
Horizontal initiatives aim to foster collaboration among diverse tasks
within the same stage. Conversely, vertical initiatives concentrate on
enhancing seamless processing across stages. With regard to horizontal
interoperability, enhancing real-time information sharing within the
same stage can effectively reduce the fragmentation and discontinuity of
activities [90]. Incorporating sensors or measurement devices into
various elements of production, including building components, tools,
machinery, and materials, can elevate them into intelligent objects.
These enhanced elements are then capable of providing instantaneous
feedback on crucial production parameters, such as position and timing
[56]. These interconnected objects, facilitated by IoT, furnish contextual
information to personnel or devices engaged in different tasks within the
same pipeline. This facilitates the systematic scheduling of production
activities. Examples include real-time monitoring and dissemination of
product location data throughout the manufacturing stage [73], and
tracking the movement trajectory and installation status of adjacent
modules during the assembly stage [56,57].
Improving vertical interoperability entails achieving seamless inte-
gration of technologies across diverse systems. This can be exemplied
by the fusion of BIM-driven computational design with robot-based
manufacturing [92,107]. Such integration minimises the complexities
associated with manual data conversion at a technical level, thereby
streamlining processes and preventing manufacturing and assembly
errors. Moreover, enhancing vertical interoperability facilitates effective
regulation by all stakeholders. In pursuit of this objective, various digital
platforms that integrate BIM and IoT have been developed. These plat-
forms are designed to enhance collaboration [90], identify project risks
[58], facilitate information exchange [59], manage cloud asset data
[61], and support collaborative decision-making [69,108]. Notably,
since BIM and IoT data are susceptible to forgery, tampering and
leakage, blockchain technology, known for its decentralisation and
transparency, has been used to provide stakeholders with digital trans-
action protection and real-time information sharing [109]. Several
studies have applied blockchain to IC, addressing issues such as partic-
ipant data privacy protection [110], verication and tracking of BIM
modication records [70], and automatic execution of smart contracts
[71]. These efforts underscore blockchain’s potential to offer a secure
digital environment across various applications and platforms, enabling
wider data exchange and collaboration.
4.3.2. Scheduling optimisation
Before assembling building components on-site, most of them need
to be manufactured in a factory environment according to an initially
predetermined production schedule. While the standardised nature of IC
facilitates the mass production of prefabricated components [111], the
oversight and scheduling of numerous manufacturing processes still
heavily rely on manpower intervention [112]. Insufcient managerial
experience or understanding of production conditions can lead to
reduced production efciency and delivery delays. To address these
challenges and enhance the rational utilisation of material and labour
resources, researchers have devised various automated models and al-
gorithms aimed at optimising production scheduling.
The validity of a production scheduling plan can be assessed by
analysing the design model before commencing production operations.
Mohsen et al. [72] proposed a machine-learning approach to forecast the
production cycle of panel walls, leveraging the physical attributes of the
product and workshop production conditions as input variables. In
contrast, genetic algorithms are widely preferred in production pro-
cesses due to their capability to swiftly generate scheduling optimisation
Fig. 11. Benets of automation technology to manufacturing and assembly.
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solutions based on analysis of changes in production conditions [113].
This method is particularly effective for addressing multi-objective
optimisation challenges. Notable applications include balancing
resource utilisation and production time [63,113,114], adapting mul-
tiple production lines to frequent changes in precast component types
[115], and rening multi-shift scheduling to respond to sudden surges in
orders [116]. Moreover, several innovative algorithms have demon-
strated their effectiveness in optimising scheduling for the challenging
task of maintaining continuous production in a xed-station production
model. For instance, inspired by real-world ticket queuing systems, Ding
et al. [108] developed a mechanism to optimise mold table scheduling.
This approach involves analysing the uncertainty of the production
environment using data gathered from the IoT, thereby mitigating issues
such as increased time differentials and prolonged workstation occu-
pancy. Conversely, Barkokebas et al. [117] introduced a digital twin-
based model that focuses on dynamically assigning tasks to multi-
skilled workers. This method helps reduce the high costs and capacity
limitations associated with the xed-station production model.
4.3.3. Production traceability
Gathering production-related data within the workshop is a funda-
mental approach to comprehending the current production status,
serving as a vital reference for real-time production scheduling optimi-
sation. However, in the context of prefabricated components with
diverse design features, the production line often takes the form of a
hybrid model assembly line. Relying on human observation to monitor
production activities in such a setup proves to be inefcient and prone to
errors, rendering it inadequate as a basis for real-time dynamic decision-
making in production planning [79]. Through the analysis of diverse
production data gathered by sensors, real-time information regarding
material consumption status and workshop production efciency can be
acquired. For instance, the implementation of sensor-IoT technologies
integrating barcodes or RFID enables the monitoring of manufacturing
conditions within the workshop [73], facilitating the optimised alloca-
tion of raw materials and enhancement of productivity. As demonstrated
by Altaf et al. [62], the analysis of production data gathered from
workstations using RFID aids in generating optimal production plans
and conguring workstations for future operations.
In addition to the prevalent sensor-IoT approaches, computer vision
and computer audition methods offer alternative means to identify and
extract production-related information within the workshop. In terms of
the computer vision method, Martinez et al. [78] utilised a Faster
Region-based Convolutional Neural Network (R-CNN)-based method to
achieve automatic recognition and tracking of resource usage status
during the manufacturing stage. This approach provides personnel with
visual representations of production information, enhancing the man-
agement of resources. Conversely, computer audition offers a comple-
mentary approach to identifying production activities by analysing
sounds emitted during the manufacturing stage, which can overcome
the inherent limitations of computer vision methods, particularly in
scenarios involving occlusions. A related study was conducted by Rashid
and Louis [79], who devised a method for identifying production ac-
tivities in a workshop utilising support vector machines. This study
accurately recognised manual activities such as hammering, nailing, and
sawing, but there is still a need to improve the ability to recognise
overlapping sound situations.
4.3.4. Production safety
The manufacturing processes conducted within a factory environ-
ment are characterised by their controlled and standardised nature.
While many advanced mechanical and manufacturing technologies are
integrated into production lines for prefabricated components, certain
aspects still necessitate manual operations [80]. It is of paramount
importance to implement appropriate measures aimed at mitigating the
risk of worker injuries. Such precautions not only safeguard the lives and
well-being of employees but also contribute to cost reduction [88].
Workers engaged in highly repetitive and physically demanding
tasks within dynamic work environments, which may involve frequent
kneeling and bending postures, face an elevated risk of occupational
injuries, such as work-related musculoskeletal disorders (WMSDs)
[118]. Ergonomics assessment serves as a practical approach for eval-
uating and addressing issues related to work efciency and worker
health, focusing on the interaction between humans, machinery, and the
work environment. Barkokebas et al. [76] have introduced an ergo-
nomic assessment model grounded in VR, which proactively anticipates
the physical movements and potential risks faced by workers. Li et al.
[88] utilised 3D modelling of workstations and worker tasks through
virtual prototyping, and then conducted ergonomic analyses of workers’
body movements and joint angles. Extending Li’s research, Wang et al.
[101] employed rule-based fuzzy inference algorithms to quantify the
risks associated with continuous human motion. These ergonomic
methods can not only decrease the incidence of work-related injuries but
also offer guidance for modifying the layout of workplaces to enhance
work efciency. Additionally, occupational safety risks arise from in-
teractions between workers and machinery. Monitoring these in-
teractions is therefore crucial for predicting potential hazards,
enhancing safety measures, and reducing accident risks [119]. Xiao et al.
[80] proposed a vision-based method utilising a Mask R-CNN algorithm
for instance segmentation. This approach tracks multiple workers and
adapts to challenges such as worker occlusion, thus enhancing produc-
tivity and workplace safety.
4.3.5. Manufacturability
The manufacturability of IC pertains to the feasibility of producing
their components in a factory setting. Given the bespoke nature of IC, the
design and manufacturing of prefabricated components frequently un-
dergo multiple iterations to ensure they can be manufactured success-
fully and meet requirements. The manufacturability of prefabricated
components is contingent upon several key factors, chiey categorised
as design rationality, implementation feasibility, and equipment
availability.
Design rationality: Assessing design rationality during the design
stage can reduce iterative rework between designers and manufacturers.
Garg and Kamat [100] have created a virtual prototype of a novel ro-
botic mechanism for the automated assembly of rebar cages. This pro-
totype offers a cost-effective means of evaluating the manufacturability
of precast concrete components prior to manufacturing. For pre-
fabricated wood components, Cao et al. [91] have developed an auto-
mated analysis model incorporating semantic reasoning. This model
enables the verication of the design rationality of wood frame panels
through manufacturing rules and provides assessments of their neces-
sary production time and resource requirements. Additionally, Anane
et al. [92] showcased the manufacturing of wooden components as a
demonstration of a system that enables industrial robots to fabricate
modular components, facilitated by a BIM-driven computational design
integration. This integrated approach signicantly enhances design ra-
tionality by effectively reducing the loss of information exchange be-
tween the design and manufacturing stages.
Implementation feasibility: This refers to the practicality of pro-
ducing a designated component within a particular factory setting.
Despite the utilisation of 3D models and BIM to facilitate production,
these tools often lack an integrated manufacturing strategy, leaving
engineers to manually create time-consuming and subjective
manufacturing processes. This inefciency underscores the necessity for
automating process planning and evaluation. For instance, wall panels
are integral components in prefabricated buildings, fundamental for
ensuring optimal thermal performance [120] and indoor comfort [121].
The primary structure of a frame wall is composed of various inter-
connected parts, and the implementation feasibility of the connection
plan and process planning inuences its manufacturability. Researchers
at the University of Alberta have undertaken groundbreaking research to
enhance the manufacturability of framed wall panels. They have
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designed a prototype machine and conducted extensive studies in this
domain. Based on this prototype, Martinez et al. [81] developed a
vision-based pre-inspection system capable of automatically identifying
potential drilling point positions and corresponding stud metrics before
assembling light-gauge steel panels. Furthermore, An et al. [95,96]
advanced the automation of assessing implementation feasibility by
integrating BIM with a manufacturing decision support system. Their
focus was on detecting the feasibility of hard joints in framing assem-
blies, including nailing in timber framing and screw fastening in steel
framing. More recently, new frameworks have been developed that
provide automated collision-free trajectory planning for various opera-
tions, such as timber panel cutting [94] and screw fastening [93].
Equipment availability: With the maturation of technology and
reduction in usage costs, automated machines and robots have become
vital in enhancing construction capabilities. These devices reduce reli-
ance on skilled labour and improve product quality and diversity. For
example, the simulation conducted by Garg and Kamat [100] on the
kinematic design of a robotic mechanism within a virtual prototype il-
lustrates the capacity of improved equipment availability to enhance
productivity and quality. Kasperzyk et al. [122] developed a robot sys-
tem capable of automatically disassembling existing prefabricated
structures and reassembling new structures based on new designs while
reusing old subassemblies. This research highlights the potential of ro-
botics to improve design rationality and equipment availability. How-
ever, the limitations of manufacturing workshop space and the
intricacies of modular components pose a signicant challenge for
implementation feasibility, particularly in nding collision-free paths
for industrial robot arms. To address this issue, Yang and Kang [123]
devised a collision avoidance method tailored for industrial robotic arms
and created a visualisation of the path-nding process and results.
4.3.6. Quality assurance
While prefabricated components are produced in a controlled factory
environment, variations in production conditions and the complexity of
the product can lead to quality deviations from the design specications.
Furthermore, transportation and assembly operations may introduce
damage to prefabricated components, potentially altering their geo-
metric shape or structural performance, thereby affecting building
safety. Detecting and monitoring the mechanical and geometric prop-
erties of prefabricated components are crucial for ensuring building
safety. However, manual monitoring methods are plagued by subjec-
tivity, time-intensive processes, high costs, and a shortage of well-
trained inspectors [60,64]. Hence, there is an urgent need to integrate
technologies capable of swiftly detecting and monitoring the quality of
prefabricated components. In terms of structural performance, while
stress-strain sensors yield precise measurements concerning structural
stress and deformation, these outputs typically manifest as raw numer-
ical data or graphs, which may not be readily comprehensible. Inte-
grating stress-strain sensors with BIM technology not only augments the
interpretability of these data points but also facilitates a macroscopic
understanding of structural safety and performance. Valinejadshoubi
et al. [77] introduced a structural health monitoring system leveraging
BIM. This system automatically analyses data collected by strain sensors
afxed to steel frame structural components, providing engineers with
real-time visualisation of component health status and offering struc-
tural maintenance suggestions.
The conformity of a component’s geometric quality with re-
quirements dictates its suitability for correct on-site assembly. Geo-
metric quality testing for IC primarily encompasses dimensional and
surface quality inspections [124]. Laser scanning, renowned for its non-
contact and high-precision characteristics, is widely employed to detect
the dimensional quality (e.g., length, width, and squareness) of precast
concrete slabs with either simple [64] or irregular shapes [60,65]. Xu
et al. [83] have demonstrated that laser scanning can also perform
surface quality inspections of prefabricated components with an accu-
racy of up to 0.1 mm. To ensure complete geometric capture of a
component, it is necessary to scan from multiple positions to accom-
modate potential occlusions or blind spots. However, developing a
suitable scanning plan poses challenging. To solve this problem, Son and
Han [84] proposed an algorithm for automatically planning scanner
parameters and scanning positions to mitigate the impact of improper
selection on scanning quality. Another available method is to utilise the
principle of mirror reection to simultaneously scan multiple surfaces
[65]. Li and Kim [85] further introduced a registration-free scanning
method based on this principle. While this enhances scanning quality
and range, the challenge lies in arranging multiple fragile mirrors for
different components. Additionally, comparing laser scanning data from
on-site installation structures with factory-prefabricated modules can
enhance the dimensional compatibility between components [86]. This
improvement helps to reduce unnecessary costs and time losses associ-
ated with component mismatches [87].
4.3.7. Constructability
Prefabricated building components, produced in factory settings, are
transported to construction sites for assembly. These components are
hoisted by cranes and manually assembled according to specic as-
sembly plans [125]. However, assembly planning methods often depend
on manual data extraction and empirical evaluations, which are both
time-consuming and complex. These practices signicantly impair
constructability during the assembly stage. Therefore, it is essential to
optimise the assembly plan through automated monitoring and analysis
of the assembly process to enhance constructability [126]. In advancing
the automation of assembly process monitoring, Li et al. [57] utilised the
IoT-BIM platform to achieve real-time recording of the position and
installation status of RFID pasted objects. In contrast, Zheng et al. [103]
developed a system based on Mask R-CNN for the automatic detection of
component positions and movements. For automated analysis of the
assembly process, Zhu et al. [97] integrated particle swarm optimisation
with simulated annealing to develop a virtual prototype. This prototype
is capable of simulating lifting operations prior to the actual assembly,
thereby enhancing the planning process. Jiang et al. [74] implemented
bidirectional interoperation between physical and virtual digital repre-
sentations of assembly sites based on digital twins. This digital twin
system helps to efciently design resource utilisation and assembly
processes. Further extending this functionality, Jiang et al. [75] enable
the utilisation of real-time data to synchronise logistics, assembly, and
multiple operational processes at construction sites.
Integrating robots into the assembly stage to replace manual labour
offers another avenue for improving constructability on site. However,
their application remains somewhat limited, as suggested by recent
studies [127–129]. A prominent area of ongoing research is the
deployment of robots for specic assembly tasks. For instance, Feng
et al. [82] demonstrated a robot capable of constructing modular
structures by establishing a local reference frame using a single camera.
Another area of research focusses on achieving automatic planning of
assembly tasks for robots. Zhu et al. [98] developed a simulator capable
of conguring various virtual construction environments derived from
BIM and open game engines. This simulator aims to optimise robotic
assembly planning through the utilisation of deep reinforcement
learning. Additionally, Zhu et al. [99] proposed a framework to coor-
dinate the collaborative work of multiple robots, thereby assigning tasks
to robots with corresponding capabilities based on the characteristics of
prefabricated components.
5. Discussion
Inspired by the previous bibliometric and template analyses of the
application of automation technologies in the manufacturing and as-
sembly stages, this section initially delves into the benets and chal-
lenges of automating IC. Subsequently, potential future research
directions are delineated, primarily focusing on automating
manufacturing and assembly processes. However, recognising that
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Automation in Construction 170 (2025) 105945
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many automation scenarios are inherently contextual and cannot be
conned to a single stage, the section also explores the applications of
these technologies across other stages.
5.1. Benets of automating IC
The primary goals of automating IC encompass enhancing con-
struction efciency, elevating product quality, diminishing costs, miti-
gating environmental impacts, and augmenting job safety [80,88,117].
The various automation scenarios, supported by current research,
enhance the benets of IC across multiple dimensions. These enhance-
ments, as depicted in Fig. 12, are evident throughout the life stages of IC
and can be summarised as follows:
•Cross-stages benets: The integration of BIM-IoT platforms, coupled
with diverse data collection technologies, facilitates enhanced
interoperability and collaboration throughout the ve IC stages.
Real-time monitoring technology spanning manufacturing, logistics,
and assembly stages enables stakeholders to promptly grasp stage
statuses, fostering effective communication and informed decision-
making. Regarding the integration between two stages, synergising
design with manufacturing or assembly stages enables proactive
identication of limitations and constraints, thereby pre-emptively
optimising the manufacturability and constructability of IC. Simul-
taneously, judiciously analysing connections between the logistics
stage and manufacturing or assembly stages enhances workow
continuity, augments site utilisation, and elevates work efciency.
•Intra-stage benets: Leveraging automated design methodologies
empowers designers to deliver exible solutions aligning with
customer requisites and professional standards. Incorporating sen-
sors and IoT technology for automatic production monitoring opti-
mises production plans while safeguarding worker well-being.
Integration of automatic positioning technology during component
transportation augments product traceability, facilitating rened
planning for manufacturing and assembly. Application of robotic
technology and scheduling optimisation for lifting activities during
the assembly stage minimises on-site installation duration and en-
sures building quality.
5.2. Challenges of automating IC
5.2.1. Data renement
Since data is the basis of information technology, it impacts how
different automation solutions are implemented. Intelligent decision-
making, process optimisation, and quality control in IC rely on
meeting the data requirements of emerging technologies. These re-
quirements include data quality, completeness, timeliness, accessibility,
consistency, and precision. For instance, for computer vision-based
techniques, delayed and blurred video clips can severely impact the
accuracy of the algorithm [103]. Therefore, enhancing the timeliness
and quality of the data is a necessary precondition for obtaining credible
results. Moreover, as technologies like digital twins and laser scanning
gain popularity, ensuring data accessibility and consistency is crucial for
accurately modelling and representing personnel, materials, and
equipment. This becomes especially important when dealing with
complex components, irregular shapes, and dynamic site conditions
[60,75,87]. Additionally, for plan optimisation technologies such as
genetic algorithms, ensuring data completeness and precision is crucial
to obtaining the best solution [114].
However, template analysis of existing studies shows that there are
still some challenges in the eld of IC in terms of collection, processing
and storage of data. Firstly, the quality and precision of the obtained
data are limited due to issues such as inaccurate sensors, imperfect
simulation methods, transmission errors and environmental distur-
bances, which may undermine the accuracy of the results of automated
decision-making [72,79,100]. Second, the lack of integration and
interoperability between various data sources, such as BIM models,
sensor data, and project management information, hinders the seamless
exchange and sharing of data and information [92]. Third, limited
computational ability, data storage capacity, and algorithmic efciency
make it challenging to realise real-time processing of large amounts of
data, thus affecting the responsiveness of automated systems [79,115].
In addition, due to the limited application of blockchain technology in
IC, data security and reliability are also one of the challenges in digi-
tising IC [70].
5.2.2. Potential exploration
The template analysis shows that the potential of certain advanced
technologies, especially AI, digital twins and blockchain, has not yet
been fully realised in IC. Regarding AI, their applications are often
limited to specic tasks such as production cycle time estimation, pro-
duction activity identication, or assembly sequence optimisation [72].
Broader applications such as advanced predictive maintenance,
comprehensive project optimisation, and intelligent automation are yet
to be developed. A major obstacle in fully exploiting AI lies in inte-
grating diverse data sources and ensuring data quality [78,79]. How-
ever, high-quality datasets are scarce in the construction eld.
Regarding digital twins, while some projects use them for monitoring
and simulation tasks [74], they are not yet fully utilised for lifecycle
(from design to O&M) management. This is primarily due to the complex
Fig. 12. Benets of automation technology to industrialised construction.
L. Xu et al.
Automation in Construction 170 (2025) 105945
14
challenge of creating and maintaining accurate digital twins containing
real-time data from multiple sources [75]. Additionally, the lack of
standardisation and interoperability between the various software
platforms used in construction projects can hinder the seamless inte-
gration of digital twins. Blockchain technology has the potential to
provide transparent and immutable records and enable smart contracts,
but it remains underutilised outside of specic use cases such as trace-
ability systems and contract transactions [71].
5.2.3. Empirical testing
A recurring pattern observed in various research endeavours in
automating IC is that proposed solutions and innovative technologies
usually remain primarily in the conceptual, laboratory, or limited-scale
testing phase [63,100]. For instance, research in areas like virtual pro-
totyping for ergonomic analysis, IoT for component tracking, and laser
scanning for quality assessment often face challenges when moving from
controlled environments to real-world construction sites. [84,90,102].
These challenges include network connectivity issues, potential damage
to sensors/RFID tags, and the inability to fully handle geometric irreg-
ularities in components [65,70].
The gap between theoretical developments and practical applica-
tions is further evidenced by the adoption of advanced technologies like
AI, digital twins, and blockchain. While offering a plethora of potential
benets, their application is often hampered by practical issues such as
dataset availability, environmental variability, and technology robust-
ness under dynamic and unpredictable construction site conditions
[80,103]. For example, AI applications enabling worker activity tra-
jectory tracking and prediction have been validated mainly in controlled
or simulated environments, and their effectiveness in complex con-
struction environments remains to be thoroughly tested [80]. Similarly,
the development of digital twins faces challenges related to the stable
communication of objects in outdoor environments [74], and
blockchain-based quality traceability frameworks require sufcient
empirical testing to conrm their utility in large-scale projects [70].
5.2.4. Workow streamlining
In the reviewed studies, automation technologies were used to solve
different specic tasks. However, these solutions rarely consider seam-
less communication or integration with other automation systems
involved in different stages or processes, highlighting the lack of an
integrated end-to-end automation workow in the IC industry. Con-
cerning data exchange and utilisation, during the design and planning
stage, while BIM-based systems enable co-design and virtual prototyp-
ing, integrating these with downstream manufacturing and assembly
stages remains a challenge. BIM models created by design teams may not
seamlessly transfer data to robotic manufacturing systems used to pro-
duce prefabricated components due to incompatible formats of data or
lack of interoperability standards [92]. Conversely, automated quality
assurance systems like laser scanners may struggle with appropriate data
formats to provide feedback to the design team about detected defects,
making it challenging to improve designs and prevent future errors
[60,87].
Regarding the integration of various automation systems, numerous
examples exist in the manufacturing and assembly stages. Although
robotic systems automate tasks such as cage assembly or component
mounting, these systems often operate independently without integra-
tion [100]. Robots may not be able to communicate with systems
responsible for material transportation or component lifting, leading to
potential errors or delays [99]. In addition, although technology facili-
tates tracking and recording of parts production and delivery, without
integrating this data into a digital twin that monitors the assembly
process, real-time visibility and coordination across all manufacturing
stages may be compromised [74]. These examples underscore the
fragmentation of automation efforts in the construction industry, hin-
dering transformative optimisation efforts.
5.3. Future research directions
The manufacturing and assembly stages within IC serve as interme-
diary stages that connect design, logistics, and subsequent O&M stages.
They stand as pivotal elements in realising seamless automation across
the entire lifecycle of IC. This article outlines ve key automation
technologies pertinent to the manufacturing and assembly stages, as
depicted in Fig. 13. Furthermore, potential avenues for extending the
application of these technologies to other stages of IC are identied.
These ve developmental trajectories prioritise the enhancement of
automation in data collection, representation, simulation, analysis,
decision-making, and activity execution. Pursuing these avenues aims to
establish a digitally driven and intelligent workow, augmenting the
advantages offered by IC in terms of efciency, quality, sustainability,
reliability, intelligence, and digitisation.
5.3.1. IoT and sensor networks
The amalgamation of IoT and sensor networks enables the remote
monitoring of diverse objects, serving as a pivotal data source for pro-
duction analysis. The manufacturing and assembly stages generate an
extensive array of data types, encompassing status information con-
cerning personnel, materials, equipment and products. Existing research
predominantly concentrates on the implementation and utilisation of
IoT systems within specic scenarios. These scenarios encompass the
gathering of location data for prefabricated components in workshops,
which aids in optimising production scheduling [62] and the tracking of
personnel activity to evaluate safety measures [101]. Recent research
trends underscore a growing emphasis on systematically deploying
sensor networks and developing platforms tailored for data integration
and analysis [74,130]. By deploying smart gateways and sensor net-
works, data ecosystems can be established. When coupled with tech-
nologies such as BIM and AI, these ecosystems can effectively support
tasks like inventory management, production analysis, and scheduling
optimisation [56,130]. Therefore, future research should prioritise
enhancing the comprehensiveness and versatility of data ecosystems.
This involves ensuring that deployed sensor networks cover a wide
range of different object types and enabling the system to monitor and
analyse various scenarios, thereby facilitating the sharing and utilisation
of information. Furthermore, the IoT, empowered by technologies such
as cloud computing or 5G wireless communication [131], is anticipated
to further enhance the digitisation of IC across various stages. For
instance, by bridging information sharing between manufacturing, lo-
gistics, and assembly, planning can be facilitated among different
stakeholders [132,133]. Additionally, through upgrading and utilising
the IoT infrastructure established during assembly, effective manage-
ment and predictive maintenance of building facilities could be achieved
in subsequent stages [77]. In conclusion, by improving the IoT and
sensor networks within the context of IC, a data base can be provided for
the realisation of digital control in all stages of the building lifecycle,
thus enhancing efciency and reliability.
5.3.2. Digital twin
A digital twin integrates physical entities, their virtual replicas, and
the data exchange between them [134]. This technology enables a
comprehensive understanding, optimisation, and management of real
physical systems through digital means [135]. When applying digital
twins to IC, the physical entities typically involve prefabricated com-
ponents and associated elements such as materials, personnel, equip-
ment, tools and processes involved in manufacturing, logistics,
assembly, and O&M stages. Correspondingly, virtual replicas are digital
copies created and maintained by digital modelling software containing
data describing the physical entities [136]. Through real-time analysis
and bidirectional exchange of data, analytical optimisation and intelli-
gent decision support can be provided for the design, manufacturing,
logistics, assembly, and O&M [137]. For instance, by applying digital
twins in the manufacturing and assembly stages, both the design could
L. Xu et al.
Automation in Construction 170 (2025) 105945
15
be optimised by sending feedback and the manufacturing and assembly
quality could be improved by comparing the actual production and
design data. For the downstream logistics and O&M stages, digital twin
technology could be used to optimise logistics planning and provide
preventive maintenance recommendations [137]. However, current
applications of digital twin technology in IC primarily focus on the
digital representation of building entities [87]. Future research en-
deavours could explore synergising digital twin technology with
emerging technologies such as IoT, cloud computing, AI, and block-
chain. Specically, integrating IoT and cloud computing into digital
twins could streamline the collection and updating of data from pro-
duction oors and assembly sites [74]. Leveraging AI within the digital
twin framework could proactively identify potential issues and optimi-
sation prospects within each production activity, offering valuable
decision-making insights [138]. Moreover, the decentralised architec-
ture of blockchain holds promise for enhancing the trustworthiness and
security of digital twin data, thereby fortifying its reliability [130]. It is
foreseeable that the all-round intelligent support and optimisation pro-
vided by digital twin technology will help IC move towards a digital and
intelligent future [75,139].
5.3.3. Virtual technology
Virtual technology, epitomised by VR/AR, stands as a pivotal
component in the digital revolution of the industrial sector, offering
highly immersive digital environments and innovative solutions for
production activities. Its extensive application within the traditional
construction industry, notably in aiding design decisions [140] and
providing safety training [141], underscores its efcacy. At present, the
utilisation of VR/AR in IC primarily centres on simulating
manufacturing, such as analysing manual manufacturing activities [76].
Looking ahead, the integration of VR/AR with cloud computing, AI, and
virtual prototyping holds promise for extending its utility in IC. This
fusion will enable the creation of product prototypes through computer
simulation, allowing for the testing and optimisation of manufacturing
and assembly stages during the design stage or pre-manufacturing phase
[142]. Additionally, in the O&M stage, it will facilitate remote visual-
isation and swift diagnosis of building health. Notably, advancements in
virtual technology devices, such as the recently unveiled mixed reality
(MR) headset Apple Vision Pro [143], which merges VR/AR with spatial
computing, signify a signicant leap forward. These devices empower
users to interact with virtual environments seamlessly, blending virtual
and real elements through visual, manual, and vocal interactions. Their
emergence holds the potential to revolutionise IC by enabling workers to
visualise full-scale 3D models of buildings overlaid on assembly sites
before the arrival of physical prefabricated components. Additionally,
experts could remotely guide assembly programs using spatial video
conferencing capabilities, optimising site layout and assembly se-
quences. The ongoing development of virtual technology is expected to
usher in a safer and more efcient production model for IC, further
propelling digital transformation within the sector.
Fig. 13. Potential future research directions of industrialised construction automation from the perspective of manufacturing and assembly.
L. Xu et al.
Automation in Construction 170 (2025) 105945
16
5.3.4. Articial intelligence
AI, with its extensive advantages in data analytics and adaptive
decision-making, is transforming the construction industry and will
continue to advance the intelligence and sustainability of IC. Although
AI has found extensive applications in sectors such as healthcare,
nance, and education [144], its deployment in IC remains relatively
limited. Current uses primarily involve the evaluation of manufacturing
operations [80] and the optimisation of scheduling plans [115]. In the
future, AI techniques could be developed for design optimisation, proj-
ect management and predictive maintenance. For example, in the design
stage, AI algorithms could be used to analyse large amounts of data in
order to generate building solutions that satisfy design constraints and
goals. In the manufacturing and assembly stages, AI-driven predictive
analytics could provide real-time control of production processes and
product quality. In the O&M stage, AI could facilitate the management
and maintenance of assets by analysing data collected by sensor net-
works. Notably, limited datasets are another important reason for the
limitation of AI applications. Since IC involves a wide range of materials,
processes, and industry standards, collecting large-scale, high-quality
data could help AI models better understand and predict the various
characteristics and variations of IC. In addition to the utilisation of
traditional AI algorithms, there is a growing interest in leveraging rev-
olutionary pre-trained AI products released in recent years [145]. Ex-
amples include OpenAI’s sophisticated large language model ChatGPT
[146], its complementary tool DALL⋅E [147], and the video generation
model Sora [148]. These cutting-edge AI models possess remarkable
capabilities in automatically generating professional and detailed text
documents and vivid media presentations. For instance, they could be
expected to create project implementation plans and renderings of
houses based on simple textual prompts provided by users. The devel-
opment of these large AI models holds the promise of signicantly
enhancing the IC industry, making it not only more efcient but also
more creative than ever before.
5.3.5. Robotics
Robots offer the capability to execute repetitive, hazardous, and
precision-demanding tasks, thereby allowing human workers to focus on
more creative endeavours. In IC, where the manipulation of pre-
fabricated components primarily occurs within factories and assembly
sites, the integration of robots brings forth advantages such as enhanced
efciency, exibility, and product quality alongside reduced costs and
manual intervention [28]. Future research endeavours could commence
with the integration of intelligent control systems, sensor technology
and additive manufacturing. For instance, during the manufacturing
stage, specialised robots could be programmed with control systems to
execute precise cutting, welding, and assembling tasks, ensuring the
accuracy and consistency of components. Additionally, equipping robot
arms with replaceable end-effectors or functional accessories would
make complex manufacturing tasks more accessible, such as screw
tightening or laser scanning. Moreover, the design of 3D concrete
printing robots could facilitate the fabrication of efcient and accurate
concrete structures [40]. During the assembly stage, the deployment of
mobile assembly robots equipped with visual recognition and local-
isation technologies could enable correct docking and connection of
various components [82]. Furthermore, the integration of AI into robots
holds promise for enhancing both manufacturing and assembly stages
[149]. Through extensive training, robots can acquire adaptive control
and learning capabilities, enabling real-time analysis of production data
to optimise operations and achieve exible handling of various products
[150]. In addition to the application of robots, improving human-robot
collaboration emerges as an effective strategy to realise safe, efcient,
and intelligent production in IC. Future research could explore task
allocation, common workspace design, and collaborative scheduling to
maximise production efciency by leveraging the respective advantages
of humans and robots.
6. Conclusions
This paper analysed 53 literature sources using a systematic review
methodology that combines PRISMA, bibliometric analysis, and tem-
plate analysis. It identied 22 different technologies and their combi-
nations, which primarily contribute to seven key benets in the
manufacturing and assembly stages of IC: interoperability, scheduling
optimisation, production traceability, production safety, manufactur-
ability, quality assurance, and constructability. The deployment of
automation technologies such as sensors, IoT, digital twins, and AI is
digitising and enhancing intelligence in production factories and as-
sembly sites. This transformation enables production monitoring,
collaborative cooperation, scheduling optimisation, and human-
machine collaboration, ultimately improving the manufacturability
and constructability of IC. Drawing from bibliometric analysis and
template analysis, this article discusses the benets of various automa-
tion scenarios implemented by these technologies in IC, detailing the
advantages across different stages and within each stage. Subsequently,
the review delineates challenges such as immature data collection and
analysis systems, inadequate deployment of emerging technologies, and
limited empirical testing for automating IC. Additionally, this paper
suggests ve future research avenues to increase the level of automation
at different stages of IC: (1) IoT and sensor networks, (2) digital twin, (3)
virtual technology, (4) AI, and (5) robotics.
The study demonstrates that understanding the unique characteris-
tics and required benets of different stages is vital for the strategic
application of technology in IC. It offers valuable insights for practi-
tioners, policymakers, and researchers aimed at enhancing IC automa-
tion and strategically integrating new technologies to tackle challenges
such as data quality and workow integration, facilitating future ad-
vancements. Despite its contributions, this study has limitations. Pri-
marily, it focuses on the manufacturing and assembly stages, though it
includes some extended discussions of automation scenarios in the
design, logistics, and O&M stages. Secondly, the search terms used may
have overlooked emerging and niche technologies.
CRediT authorship contribution statement
Li Xu: Writing – review & editing, Writing – original draft, Visuali-
zation, Validation, Software, Methodology, Investigation, Formal anal-
ysis, Data curation, Conceptualization. Yang Zou: Writing – review &
editing, Validation, Supervision, Project administration, Methodology,
Funding acquisition, Conceptualization. Yuqian Lu: Writing – review &
editing, Validation, Supervision, Project administration, Methodology,
Funding acquisition, Conceptualization. Alice Chang-Richards:
Writing – review & editing, Validation, Supervision, Project adminis-
tration, Methodology, Funding acquisition, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgment
This article’s rangahau (research) was enabled by funding from Te
H¯
ıkina Whakatutuki - The Ministry of Business, Innovation and
Employment (MBIE) of Aotearoa New Zealand within the framework of
an Endeavour Research Programme managed by HERA (Heavy Engi-
neering Research Association).
M¯
aori references use the Waikato-Tainui meta (dialect) to
L. Xu et al.
Automation in Construction 170 (2025) 105945
17
acknowledge HERA being located in the rohe of Manukau, T¯
amaki
Makaurau (Auckland). The use of te reo M¯
aori (the M¯
aori language) is
intentional as part of the programme’s commitment to Vision
M¯
atauranga.
Appendix A. Summary of data extracted from reviewed articles
No. Title Enhanced benet Adopted technologies Contributions Reference
1 Connecting teams in modular construction
projects using game engine technology
Interoperability BIM; Virtual prototyping A digital platform enhancing collaboration and decision-
making across modular construction teams
[90]
2 Prefabricated construction enabled by the
Internet-of-Things
Interoperability BIM; RFID; IoT A unied platform for tracking and managing
prefabricated components, enabling better coordination
among stakeholders
[56]
3 An Internet of Things-enabled BIM platform for
on-site assembly services in prefabricated
construction
Interoperability BIM; RFID; GPS; IoT; VR An IoT-enabled platform that leverages BIM and RFID
technologies to provide real-time visibility, traceability,
and decision support
[57]
4 Schedule risks in prefabrication housing
production in Hong Kong: a social network
analysis
Interoperability BIM; IoT BIM-centred strategies for mitigating risks and
improving stakeholder communication, thereby
addressing schedule delays
[58]
5 Integrating RFID and BIM technologies for
mitigating risks and improving schedule
performance of prefabricated house
construction
Interoperability BIM; RFID; IoT; GPS A RFID-enabled BIM platform that enhances schedule
performance in prefabricated house construction by
improving real-time data visibility, stakeholder
communication, and process coordination
[59]
6 Cloud asset-enabled integrated IoT platform
for lean prefabricated construction
Interoperability BIM; RFID; IoT An IoT platform that integrates various technologies to
support lean prefabricated construction, improving
process efciency, reducing waste, and enhancing
coordination among stakeholders
[61]
7 Customization of on-site assembly services by
integrating the internet of things and BIM
technologies in modular integrated
construction
Interoperability BIM; RFID; IoT; GPS An IoT-enabled Smart BIM platform to improve
coordination among stakeholders and streamline the on-
site assembly process
[69]
8 Two-layer adaptive blockchain-based
supervision model for off-site modular housing
production
Interoperability Blockchain A novel approach to manufacturing supervision that
ensures data privacy, reduces storage costs, and offers a
tamper-proof record-keeping mechanism
[110]
9 Linking permissioned blockchain to Internet of
Things (IoT)-BIM platform for off-site
production management in modular
construction
Interoperability BIM; RFID; IoT;
Blockchain
An integrated platform that avoids single points of failure
in IoT networks and ensures BIM modications,
enhancing the security, integrity, and efciency
[70]
10 Sensor-integrated hybrid blockchain system
for supply chain coordination in volumetric
modular construction
Interoperability Blockchain; RFID; GPS A hybrid on- and off-chain blockchain system that
enables the storage and sharing of large sensor data les
in modular construction, supports traceable information
sharing among stakeholders, and facilitates data-driven
automated decision-making
[71]
11 A machine learning approach to predict
production time using real-time RFID data in
industrialised building construction
Scheduling
optimisation
AI (Machine learning);
RFID
A data-driven predictive model for estimating
production cycle times
[72]
12 Synchronizing production scheduling with
resources allocation for precast components in
a multi-agent system environment
Scheduling
optimisation
AI (Genetic algorithm);
AI (Multi-agent system)
An integrated approach to precast production planning
that synchronises scheduling and resource allocation,
potentially improving on-time delivery and reducing
waiting and extension times
[113]
13 Precast production scheduling using multi-
objective genetic algorithms
Scheduling
optimisation
AI (Genetic algorithm) A novel multi-objective scheduling model that integrates
considerations of buffer sizes and resource limitations,
providing more realistic and feasible production
schedules
[114]
14 Optimised owshop scheduling of multiple
production lines for precast production
Scheduling
optimisation
AI (Genetic algorithm) A novel model and optimisation approach that considers
multiple production lines and specic constraints like
mold and pallet quantities
[115]
15 Flowshop scheduling optimisation for multi-
shift precast production with on-time delivery
Scheduling
optimisation
AI (Genetic algorithm) A novel owshop scheduling optimisation model for
multi-shift precast production, providing a theoretical
basis to adjust production schedules for time-pressured
orders efciently
[116]
16 Framework for modelling operational
uncertainty to optimise offsite production
scheduling of precast components
Scheduling
optimisation
AI (Genetic algorithm) A novel hybrid model that combines genetic algorithms
with discrete event simulation to more accurately
represent real-world precast production environments
[63]
17 Multistage self-adaptive decision-making
mechanism for prefabricated building modules
with IoT-enabled graduation manufacturing
system
Scheduling
optimisation
IoT A novel framework and a decision-making mechanism
that improves prefabricated production planning,
scheduling
[108]
18 Assessment of digital twins to reassign
multiskilled workers in offsite construction
based on lean thinking
Scheduling
optimisation
Digital twin A novel approach to increasing exibility on off-site
construction shop oors by leveraging a digital twin to
manage multiskilled workers dynamically
[117]
19 Framework for an IoT-based shop oor
material management system for panelized
homebuilding
Production
traceability
IoT; RFID; Barcode A framework for an IoT-based material management
system that addresses the challenges of dynamic changes
in panelised construction
[73]
20 Integrated production planning and control
system for a panelized home prefabrication
facility using simulation and RFID
Production
traceability
RFID An innovative framework that combines data collection,
data cleaning, and simulation-based optimisation for
production planning and control in panelised building
[62]
(continued on next page)
L. Xu et al.
Automation in Construction 170 (2025) 105945
18
(continued)
No. Title Enhanced benet Adopted technologies Contributions Reference
21 A vision-based approach for automatic
progress tracking of oor paneling in offsite
construction facilities
Production
traceability
AI (Deep learning);
Camera
A method to capture productivity-related metrics
automatically and address the limitations of manual
monitoring methods
[78]
22 Activity identication in modular construction
using audio signals and machine learning
Production
traceability
AI (Machine learning);
Camera
An innovative approach for automated activity
identication in modular construction using audio
signals, providing foundational knowledge for feature
selection and optimisation
[79]
23 VR-MOCAP-enabled ergonomic risk
assessment of workstation prototypes in offsite
construction
Production Safety VR; IMU A VR-motion capture enabled ergonomic risk assessment
method for manufacturing workstations
[76]
24 Automated post-3D visualisation ergonomic
analysis system for rapid workplace design in
modular construction
Production Safety Virtual prototyping;
Ergonomic analysis
An innovative system that integrates 3D visualisation
with ergonomic risk assessment tools
[88]
25 3D fuzzy ergonomic analysis for rapid
workplace design and modication in
construction
Production Safety Virtual prototyping;
Ergonomic analysis; AI
(Fuzzy logic)
A novel approach to ergonomic risk assessment using 3D
fuzzy logic, offering improved accuracy and reliability
over traditional methods
[101]
26 3D standard motion time-based ergonomic risk
analysis for workplace design in modular
construction
Production Safety Virtual prototyping;
Ergonomic analysis
A novel method that combines 3D visualisation with
PMTS to accurately assess ergonomic risks in modular
construction, offering a systematic and objective
approach to proactive workplace design
[102]
27 Vision-based method for tracking workers by
integrating deep learning instance
segmentation in off-site construction
Production Safety AI (Deep learning);
Camera
A novel vision-based tracking method integrating deep
learning instance segmentation, improving tracking
accuracy and robustness in off-site construction
environments
[80]
28 Virtual prototyping for robotic fabrication of
rebar cages in manufactured concrete
construction
Manufacturability VR; Virtual prototyping;
Robotics (Specialised
robot)
A novel approach to automating the assembly of rebar
cages for precast concrete elements using a robotic
system
[100]
29 Ontology-based manufacturability analysis
automation for industrialised construction
Manufacturability BIM An innovative approach to automating
manufacturability analysis in industrialised
construction, bridging the gap between design and
manufacturing knowledge
[91]
30 BIM-driven computational design for robotic
manufacturing in off-site construction: an
integrated Design-to-Manufacturing (DtM)
approach
Manufacturability BIM; Robotics (Robot
arm)
A novel DtM approach that combines BIM and CD for
effective robotic manufacturing in off-site construction,
addressing the gap between design intricacy and
manufacturing capabilities, and promoting greater
efciency and customisation
[92]
31 A vision-based system for pre-inspection of
steel frame manufacturing
Manufacturability AI (Computer vision);
BIM; Camera; Robotics
(Specialised robot)
A vision-based framework for automating supervision in
the pre-manufacturing stage of steel framing
[81]
32 Generation of safe tool-paths for automatic
manufacturing of light gauge steel panels in
residential construction
Manufacturability BIM; Robotics
(Specialised robot)
A novel framework for automating the transfer of
manufacturing information from BIM to a prototype
machine
[93]
33 Target-path planning and manufacturability
check for robotic CLT machining operations
from BIM information
Manufacturability BIM; Robotics
(Specialised robot)
A novel approach to integrating design and
manufacturing information for panels
[94]
34 BIM-based decision support system for
automated manufacturability check of wood
frame assemblies
Manufacturability BIM; Robotics
(Specialised robot)
A novel BIM-based framework for automating the
manufacturability analysis of wood frame assemblies
[95]
35 Automated verication of 3D
manufacturability for steel frame assemblies
Manufacturability BIM; Robotics
(Specialised robot)
A novel approach for automating manufacturability
checks of steel frame assemblies in 3D
[96]
36 Automated re-prefabrication system for
buildings using robotics
Manufacturability Robotics (Robot arm) A concept for automated refabrication in IC to improve
the exibility of prefabricated structures to
accommodate design changes. The feasibility of
automated disassembly and reconstruction is
demonstrated, with the potential to increase
manufacturability and sustainability in the construction
industry
[122]
37 Collision avoidance method for robotic
modular home prefabrication
Manufacturability Robotics (Robot arm) A collision avoidance method that can dynamically adapt
to various component sizes in manufacturing processes
[123]
38 Automated dimensional quality assurance of
full-scale precast concrete elements using laser
scanning and BIM
Quality assurance BIM; Laser scanning An automated technique capable of accurately assessing
the dimensional quality of full-scale precast concrete
elements and proposes a BIM-assisted approach for
efcient storage and management of data
[60]
39 Automated dimensional quality assessment of
precast concrete panels using terrestrial laser
scanning
Quality assurance Laser scanning An automated technique for the dimensional quality
assessment of precast concrete panels using Terrestrial
Laser Scanning (TLS)
[64]
40 Automated quality assessment of precast
concrete elements with geometry irregularities
using terrestrial laser scanning
Quality assurance BIM; Laser scanning An automated technique for the quality assessment of
precast concrete elements with complex geometries. The
mirror-aided scanning approach aid in accurate data
collection in challenging scanning environments
[65]
41 Geometric modelling and surface-quality
inspection of prefabricated concrete
components using sliced point clouds
Quality assurance BIM; Laser scanning A method for point cloud data segmentation, geometric
modelling and surface quality inspection
[83]
42 Automated model-based 3D scan planning for
prefabricated building components
Quality assurance Laser scanning A model-based 3D scan planning method, generating
potential scan locations and performing visibility and
quality analysis
[84]
(continued on next page)
L. Xu et al.
Automation in Construction 170 (2025) 105945
19
(continued)
No. Title Enhanced benet Adopted technologies Contributions Reference
43 Mirror-aided registration-free geometric
quality inspection of planar-type prefabricated
elements using terrestrial laser scanning
Quality assurance Laser scanning A registration-free approach for the geometric quality
inspection of prefabricated elements using TLS and
mirrors
[85]
44 Automated compatibility checking of
prefabricated components using 3D as-built
models and BIM
Quality assurance BIM; Laser scanning A generalised method for automated compatibility
checking of prefabricated components, addressing the
challenge of module mismatch in IC
[86]
45 Deploying 3D scanning based geometric digital
twins during fabrication and assembly in
offsite manufacturing
Quality assurance BIM; Laser scanning;
Digital twin
A framework for integrating geometric digital twins and
laser scanning for remote quality control in
manufacturing of IC
[87]
46 Development of a BIM-based data
management system for structural health
monitoring with application to modular
buildings: case study
Quality assurance BIM; Stress or strain
sensors
An integrated BIM and Structural Health Monitoring
framework for modular buildings helps detect hidden
damage and make timely repair and maintenance
decisions.
[77]
47 Crane-lift path planning for high-rise modular
integrated construction through metaheuristic
optimisation and virtual prototyping
Constructability BIM; Virtual prototyping A system for crane-lift path planning in high-rise IC,
integrating advanced optimisation algorithms with
virtual prototyping to enhance safety and efciency
[97]
48 Virtual prototyping- and transfer learning-
enabled module detection for modular
integrated construction
Constructability AI (Deep learning);
Virtual prototyping
An innovative approach combining virtual prototyping
and transfer learning for developing a deep learning
model capable of automatic module detection in IC
[103]
49 Digital twin-enabled smart modular integrated
construction system for on-site assembly
Constructability BIM; RFID; Digital twin A digital twin-enabled framework for reengineering on-
site assembly in IC, enhancing real-time visibility,
traceability, and interoperability of on-site resources
[74]
50 Digital twin-enabled real-time synchronization
for planning, scheduling, and execution in
precast on-site assembly
Constructability BIM; RFID; Digital twin A model that signicantly improves the Planning,
Scheduling, and Execution process in IC by utilising
digital twin technology for real-time data integration
[75]
51 Vision guided autonomous robotic assembly
and as-built scanning on unstructured
construction sites
Constructability Robotics (Robot arm);
Camera
Algorithms and a robotic system capable of autonomous
assembly in the unstructured environment of
construction sites, addressing the mobility and cognitive
challenges associated with such tasks
[82]
52 Deep reinforcement learning for real-time
assembly planning in robot-based
prefabricated construction
Constructability AI (Deep learning); BIM A DRL-based method for automated assembly planning
in prefabricated construction, providing a computational
environment for applying DRL in on-site operations and
offering benchmarks for future research in automated
construction
[98]
53 Smart component-oriented method of
construction robot coordination for
prefabricated housing
Constructability BIM; Robotics (Robot
arm)
A component-oriented robot construction approach [99]
References
[1] F.H. Abanda, J.H.M. Tah, F.K.T. Cheung, BIM in off-site manufacturing for
buildings, J. Build. Eng. 14 (2017) 89–102, https://doi.org/10.1016/j.
jobe.2017.10.002.
[2] F. Barbosa, J. Woetzel, J. Mischke, Reinventing Construction: A Route of Higher
Productivity, McKinsey Global Institute, 2017. https://www.mckinsey.com/
~/media/mckinsey/business%20functions/operations/our%20insights/reinvent
ing%20construction%20through%20a%20productivity%20revolution/mgi-rein
venting-construction-a-route-to-higher-productivity-full-report.pdf.
[3] M. Razkenari, A. Fenner, A. Shojaei, H. Hakim, C. Kibert, Perceptions of offsite
construction in the United States: an investigation of current practices, J. Build.
Eng. 29 (2020) 101138, https://doi.org/10.1016/j.jobe.2019.101138.
[4] T.D. Oesterreich, F. Teuteberg, Understanding the implications of digitisation and
automation in the context of industry 4.0: a triangulation approach and elements
of a research agenda for the construction industry, Comput. Ind. 83 (2016)
121–139, https://doi.org/10.1016/j.compind.2016.09.006.
[5] A.M. Kamar, Z. Abd Hamid, N.A. Azman, Industrialized building system (IBS):
revisiting issues of denition and classication, Int. J. Emerg. Sci. 1 (2) (2011)
120. https://journals.indexcopernicus.com/search/article?articleId=955321.
[6] B. Qi, M. Razkenari, A. Costin, C. Kibert, M. Fu, A systematic review of emerging
technologies in industrialized construction, J. Build. Eng. 39 (2021) 102265,
https://doi.org/10.1016/j.jobe.2021.102265.
[7] M.R. Hosseini, I. Martek, E.K. Zavadskas, A.A. Aibinu, M. Arashpour, N. Chileshe,
Critical evaluation of off-site construction research: a Scientometric analysis,
Autom. Constr. 87 (2018) 235–247, https://doi.org/10.1016/j.
autcon.2017.12.002.
[8] C. Mao, F. Xie, L. Hou, P. Wu, J. Wang, X. Wang, Cost analysis for sustainable off-
site construction based on a multiple-case study in China, Habitat Int. 57 (2016)
215–222, https://doi.org/10.1016/j.habitatint.2016.08.002.
[9] Z. Zhang, Y. Zou, B.H.W. Guo, J. Dimyadi, R. Davies, L. Jiang, Knowledge
management for off-site construction, Autom. Constr. 166 (2024) 105632,
https://doi.org/10.1016/j.autcon.2024.105632.
[10] S. Navaratnam, A. Satheeskumar, G. Zhang, K. Nguyen, S. Venkatesan,
K. Poologanathan, The challenges confronting the growth of sustainable
prefabricated building construction in Australia: construction industry views,
J. Build. Eng. 48 (2022) 103935, https://doi.org/10.1016/j.jobe.2021.103935.
[11] O. Wong Chong, J. Zhang, Logic representation and reasoning for automated BIM
analysis to support automation in offsite construction, Autom. Constr. 129 (2021)
103756, https://doi.org/10.1016/j.autcon.2021.103756.
[12] M.P. Groover, Automation, Production Systems, and Computer-Integrated
Manufacturing, Pearson Education India, 2016 (ISBN: 9332586063).
[13] L. Hou, Y. Tan, W. Luo, S. Xu, C. Mao, S. Moon, Towards a more extensive
application of off-site construction: a technological review, Int. J. Constr. Manag.
22 (11) (2022) 2154–2165, https://doi.org/10.1080/15623599.2020.1768463.
[14] Y. Zhang, Z. Lei, S. Han, A. Bouferguene, M. Al-Hussein, Process-oriented
framework to improve modular and offsite construction manufacturing
performance, J. Constr. Eng. Manag. 146 (9) (2020) 04020116, https://doi.org/
10.1061/(ASCE)CO.1943-7862.0001909.
[15] V.W.Y. Tam, J.J.L. Hao, Prefabrication as a mean of minimizing construction
waste on site, Int. J. Constr. Manag. 14 (2) (2014) 113–121, https://doi.org/
10.1080/15623599.2014.899129.
[16] T. Bock, The future of construction automation: technological disruption and the
upcoming ubiquity of robotics, Autom. Constr. 59 (2015) 113–121, https://doi.
org/10.1016/j.autcon.2015.07.022.
[17] Y. Fu, J. Chen, W. Lu, Human-robot collaboration for modular construction
manufacturing: review of academic research, Autom. Constr. 158 (2024) 105196,
https://doi.org/10.1016/j.autcon.2023.105196.
[18] X. Gan, R. Chang, J. Zuo, T. Wen, G. Zillante, Barriers to the transition towards
off-site construction in China: an interpretive structural modeling approach,
J. Clean. Prod. 197 (2018) 8–18, https://doi.org/10.1016/j.jclepro.2018.06.184.
[19] J. Ortega, H.A. Mesa, L.F. Alarc´
on, The interrelationship between barriers
impeding the adoption of off-site construction in developing countries: the case of
Chile, J. Build. Eng. 73 (2023) 106824, https://doi.org/10.1016/j.
jobe.2023.106824.
[20] X. Yin, H. Liu, Y. Chen, M. Al-Hussein, Building information modelling for off-site
construction: review and future directions, Autom. Constr. 101 (2019) 72–91,
https://doi.org/10.1016/j.autcon.2019.01.010.
[21] M. Wang, C.C. Wang, S. Sepasgozar, S. Zlatanova, A systematic review of digital
technology adoption in off-site construction: current status and future direction
towards industry 4.0, Buildings 10 (11) (2020) 204, https://doi.org/10.3390/
buildings10110204.
[22] C.P. Chea, Y. Bai, X. Pan, M. Arashpour, Y. Xie, An integrated review of
automation and robotic technologies for structural prefabrication and
L. Xu et al.
Automation in Construction 170 (2025) 105945
20
construction, Transp. Saf. Environ. 2 (2) (2020) 81–96, https://doi.org/10.1093/
tse/tdaa007.
[23] C.Z. Li, M. Hu, B. Xiao, Z. Chen, V.W.Y. Tam, Y. Zhao, Mapping the knowledge
domains of emerging advanced technologies in the management of prefabricated
construction, Sustainability 13 (16) (2021) 8800, https://doi.org/10.3390/
su13168800.
[24] T.O. Olawumi, D.W.M. Chan, S. Ojo, M.C.H. Yam, Automating the modular
construction process: a review of digital technologies and future directions with
blockchain technology, J. Build. Eng. 46 (2022) 103720, https://doi.org/
10.1016/j.jobe.2021.103720.
[25] Z. Cheng, S. Tang, H. Liu, Z. Lei, Digital technologies in offsite and prefabricated
construction: theories and applications, Buildings 13 (1) (2023) 163, https://doi.
org/10.3390/buildings13010163.
[26] M. Chatzimichailidou, Y. Ma, Using BIM in the safety risk management of
modular construction, Saf. Sci. 154 (2022) 105852, https://doi.org/10.1016/j.
ssci.2022.105852.
[27] V. Rangasamy, J.-B. Yang, The convergence of BIM, AI and IoT: reshaping the
future of prefabricated construction, J. Build. Eng. 84 (2024) 108606, https://doi.
org/10.1016/j.jobe.2024.108606.
[28] M. Pan, Y. Yang, Z. Zheng, W. Pan, Articial intelligence and robotics for
prefabricated and modular construction: a systematic literature review, J. Constr.
Eng. Manag. 148 (9) (2022) 03122004, https://doi.org/10.1061/(ASCE)
CO.1943-7862.0002324.
[29] F. Alsakka, S. Assaf, I. El-Chami, M. Al-Hussein, Computer vision applications in
offsite construction, Autom. Constr. 154 (2023) 104980, https://doi.org/
10.1016/j.autcon.2023.104980.
[30] Q. Liu, Y. Ma, L. Chen, W. Pedrycz, M.J. Skibniewski, Z.-S. Chen, Articial
intelligence for production, operations and logistics management in modular
construction industry: a systematic literature review, Inf. Fusion 109 (2024)
102423, https://doi.org/10.1016/j.inffus.2024.102423.
[31] S. Gusmao Brissi, O. Wong Chong, L. Debs, J. Zhang, A review on the interactions
of robotic systems and lean principles in offsite construction, Eng. Constr. Archit.
Manag. 29 (1) (2022) 383–406, https://doi.org/10.1108/ECAM-10-2020-0809.
[32] A. Hadi, F. Cheung, S. Adjei, A. Dulaimi, Evaluation of lean off-site construction
literature through the lens of industry 4.0 and 5.0, J. Constr. Eng. Manag. 149
(12) (2023) 03123007, https://doi.org/10.1061/JCEMD4.COENG-13622.
[33] S. Reichenbach, B. Kromoser, State of practice of automation in precast concrete
production, J. Build. Eng. 43 (2021) 102527, https://doi.org/10.1016/j.
jobe.2021.102527.
[34] E. Lachance, N. Lehoux, P. Blanchet, Automated and robotized processes in the
timber-frame prefabrication construction industry: a state of the art, in: 2022
IEEE 6th International Conference on Logistics Operations Management (GOL),
2022, pp. 1–10, https://doi.org/10.1109/GOL53975.2022.9820541.
[35] Z. Ma, Y. Liu, J. Li, Review on automated quality inspection of precast concrete
components, Autom. Constr. 150 (2023) 104828, https://doi.org/10.1016/j.
autcon.2023.104828.
[36] A.P. Siddaway, A.M. Wood, L.V. Hedges, How to do a systematic review: a best
practice guide for conducting and reporting narrative reviews, meta-analyses, and
meta-syntheses, Annu. Rev. Psychol. 70 (July) (2019) 747–770, https://doi.org/
10.1146/annurev-psych-010418-102803.
[37] D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman, Preferred reporting items for
systematic reviews and meta-analyses: the PRISMA statement, Int. J. Surg. 8 (5)
(2010) 336–341, https://doi.org/10.1016/j.ijsu.2010.02.007.
[38] A. Boaye Belle, Y. Zhao, Evidence-based decision-making: on the use of
systematicity cases to check the compliance of reviews with reporting guidelines
such as PRISMA 2020, Expert Syst. Appl. 217 (2023) 119569, https://doi.org/
10.1016/j.eswa.2023.119569.
[39] F. Kedir, D.M. Hall, Resource efciency in industrialized housing construction – a
systematic review of current performance and future opportunities, J. Clean.
Prod. 286 (2021) 125443, https://doi.org/10.1016/j.jclepro.2020.125443.
[40] B. Ter Haar, J. Kruger, G. van Zijl, Off-site construction with 3D concrete printing,
Autom. Constr. 152 (2023) 104906, https://doi.org/10.1016/j.
autcon.2023.104906.
[41] R. Jin, S. Gao, A. Cheshmehzangi, E. Aboagye-Nimo, A holistic review of off-site
construction literature published between 2008 and 2018, J. Clean. Prod. 202
(2018) 1202–1219, https://doi.org/10.1016/j.jclepro.2018.08.195.
[42] X. Cao, X. Li, Y. Zhu, Z. Zhang, A comparative study of environmental
performance between prefabricated and traditional residential buildings in China,
J. Clean. Prod. 109 (2015) 131–143, https://doi.org/10.1016/j.
jclepro.2015.04.120.
[43] S. Costa, M.S. Carvalho, C. Pimentel, C. Duarte, A systematic literature review and
conceptual framework of construction industrialization, J. Constr. Eng. Manag.
149 (2) (2023) 03122013, https://doi.org/10.1061/(ASCE)CO.1943-
7862.0002410.
[44] A.G. Gibb, Off-Site Fabrication: Prefabrication, Pre-Assembly and Modularisation,
John Wiley & Sons, 1999. ISBN: 9780470378366.
[45] M.E. Falagas, E.I. Pitsouni, G.A. Malietzis, G. Pappas, Comparison of PubMed,
Scopus, web of science, and Google scholar: strengths and weaknesses, FASEB J.
22 (2) (2008) 338–342, https://doi.org/10.1096/fj.07-9492LSF.
[46] Q. He, G. Wang, L. Luo, Q. Shi, J. Xie, X. Meng, Mapping the managerial areas of
building information modeling (BIM) using scientometric analysis, Int. J. Proj.
Manag. 35 (4) (2017) 670–685, https://doi.org/10.1016/j.
ijproman.2016.08.001.
[47] P. Sch¨
onfelder, A. Aziz, B. Faltin, M. K¨
onig, Automating the retrospective
generation of as-is BIM models using machine learning, Autom. Constr. 152
(2023) 104937, https://doi.org/10.1016/j.autcon.2023.104937.
[48] B. Xiao, C. Chen, X. Yin, Recent advancements of robotics in construction, Autom.
Constr. 144 (2022) 104591, https://doi.org/10.1016/j.autcon.2022.104591.
[49] Y. Pan, L. Zhang, Integrating BIM and AI for smart construction management:
current status and future directions, Arch. Comput. Methods Eng. 30 (2) (2023)
1081–1110, https://doi.org/10.1007/s11831-022-09830-8.
[50] T. Greenhalgh, R. Peacock, Effectiveness and efciency of search methods in
systematic reviews of complex evidence: audit of primary sources, BMJ 331
(7524) (2005) 1064–1065, https://doi.org/10.1136/bmj.38636.593461.68.
[51] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, W.M. Lim, How to conduct a
bibliometric analysis: an overview and guidelines, J. Bus. Res. 133 (2021)
285–296, https://doi.org/10.1016/j.jbusres.2021.04.070.
[52] C. Forliano, P. De Bernardi, D. Yahiaoui, Entrepreneurial universities: a
bibliometric analysis within the business and management domains, Technol.
Forecast. Soc. Chang. 165 (2021) 120522, https://doi.org/10.1016/j.
techfore.2020.120522.
[53] N. King, Doing template analysis, in: G. Symon, C. Cassell (Eds.), Qualitative
Organizational Research: Core Methods and Current Challenges, Sage
Publications, Thousand Oaks, CA, 2012, pp. 426–449 (ISBN: 9781446258279).
[54] J. Brooks, S. McCluskey, E. Turley, N. King, The utility of template analysis in
qualitative psychology research, Qual. Res. Psychol. 12 (2) (2015) 202–222,
https://doi.org/10.1080/14780887.2014.955224.
[55] B.A. Kadir, O. Broberg, C.S.d., Conceiç˜
ao, current research and future
perspectives on human factors and ergonomics in industry 4.0, Comput. Ind. Eng.
137 (2019) 106004, https://doi.org/10.1016/j.cie.2019.106004.
[56] R.Y. Zhong, Y. Peng, F. Xue, J. Fang, W. Zou, H. Luo, S. Thomas Ng, W. Lu, G.Q.
P. Shen, G.Q. Huang, Prefabricated construction enabled by the internet-of-
things, Autom. Constr. 76 (2017) 59–70, https://doi.org/10.1016/j.
autcon.2017.01.006.
[57] C.Z. Li, F. Xue, X. Li, J.K. Hong, G.Q. Shen, An internet of things-enabled BIM
platform for on-site assembly services in prefabricated construction, Autom.
Constr. 89 (2018) 146–161, https://doi.org/10.1016/j.autcon.2018.01.001.
[58] C.Z. Li, J. Hong, F. Xue, G.Q. Shen, X. Xu, M.K. Mok, Schedule risks in
prefabrication housing production in Hong Kong: a social network analysis,
J. Clean. Prod. 134 (2016) 482–494, https://doi.org/10.1016/j.
jclepro.2016.02.123.
[59] C.Z. Li, R.Y. Zhong, F. Xue, G. Xu, K. Chen, G.G. Huang, G.Q. Shen, Integrating
RFID and BIM technologies for mitigating risks and improving schedule
performance of prefabricated house construction, J. Clean. Prod. 165 (2017)
1048–1062, https://doi.org/10.1016/j.jclepro.2017.07.156.
[60] M.K. Kim, Q. Wang, J.W. Park, J.C.P. Cheng, H. Sohn, C.C. Chang, Automated
dimensional quality assurance of full-scale precast concrete elements using laser
scanning and BIM, Autom. Constr. 72 (2016) 102–114, https://doi.org/10.1016/
j.autcon.2016.08.035.
[61] G. Xu, M. Li, C.-H. Chen, Y. Wei, Cloud asset-enabled integrated IoT platform for
lean prefabricated construction, Autom. Constr. 93 (2018) 123–134, https://doi.
org/10.1016/j.autcon.2018.05.012.
[62] M.S. Altaf, A. Bouferguene, H.X. Liu, M. Al-Hussein, H.T. Yu, Integrated
production planning and control system for a panelized home prefabrication
facility using simulation and RFID, Autom. Constr. 85 (2018) 369–383, https://
doi.org/10.1016/j.autcon.2017.09.009.
[63] Z. Wang, H. Hu, J. Gong, Framework for modeling operational uncertainty to
optimize offsite production scheduling of precast components, Autom. Constr. 86
(2018) 69–80, https://doi.org/10.1016/j.autcon.2017.10.026.
[64] M.-K. Kim, H. Sohn, C.-C. Chang, Automated dimensional quality assessment of
precast concrete panels using terrestrial laser scanning, Autom. Constr. 45 (2014)
163–177, https://doi.org/10.1016/j.autcon.2014.05.015.
[65] Q. Wang, M.-K. Kim, J.C.P. Cheng, H. Sohn, Automated quality assessment of
precast concrete elements with geometry irregularities using terrestrial laser
scanning, Autom. Constr. 68 (2016) 170–182, https://doi.org/10.1016/j.
autcon.2016.03.014.
[66] W. Gl¨
anzel, A. Schubert, Analysing scientic networks through co-authorship, in:
H.F. Moed, W. Gl¨
anzel, U. Schmoch (Eds.), Handbook of Quantitative Science and
Technology Research: The Use of Publication and Patent Statistics in Studies of
S&T Systems, Springer, Netherlands, Dordrecht, 2005, pp. 257–276. ISBN:
9781402027550.
[67] Y. Ding, Scientic collaboration and endorsement: network analysis of
coauthorship and citation networks, J. Inf. Secur. 5 (1) (2011) 187–203, https://
doi.org/10.1016/j.joi.2010.10.008.
[68] N.J. van Eck, L. Waltman, Software survey: VOSviewer, a computer program for
bibliometric mapping, Scientometrics 84 (2) (2010) 523–538, https://doi.org/
10.1007/s11192-009-0146-3.
[69] J.X. Zhou, G.Q. Shen, S.H. Yoon, X. Jin, Customization of on-site assembly
services by integrating the internet of things and BIM technologies in modular
integrated construction, Autom. Constr. 126 (2021) 103663, https://doi.org/
10.1016/j.autcon.2021.103663.
[70] L.P.F. Wu, W.S. Lu, F. Xue, X. Li, R. Zhao, M.H. Tang, Linking permissioned
blockchain to internet of things (IoT)-BIM platform for off-site production
management in modular construction, Comput. Ind. 135 (2022), https://doi.org/
10.1016/j.compind.2021.103573.
[71] D.M. Lee, L.Y. Wen, J.O. Choi, S.H. Lee, Sensor-integrated hybrid blockchain
system for supply chain coordination in volumetric modular construction,
J. Constr. Eng. Manag. 149 (1) (2023), https://doi.org/10.1061/(ASCE)CO.1943-
7862.0002427.
[72] O. Mohsen, Y. Mohamed, M. Al-Hussein, A machine learning approach to predict
production time using real-time RFID data in industrialized building construction,
Adv. Eng. Inform. 52 (2022), https://doi.org/10.1016/j.aei.2022.101631.
L. Xu et al.
Automation in Construction 170 (2025) 105945
21
[73] M. Wang, M.S. Altaf, M. Al-Hussein, Y.S. Ma, Framework for an IoT-based shop
oor material management system for panelized homebuilding, Int. J. Constr.
Manag. 20 (2) (2020) 130–145, https://doi.org/10.1080/
15623599.2018.1484554.
[74] Y. Jiang, M. Li, D. Guo, W. Wu, R.Y. Zhong, G.Q. Huang, Digital twin-enabled
smart modular integrated construction system for on-site assembly, Comput. Ind.
136 (2022) 103594, https://doi.org/10.1016/j.compind.2021.103594.
[75] Y.S. Jiang, M. Li, M.X. Li, X.L. Liu, R.Y. Zhong, W. Pan, G.Q. Huang, Digital twin-
enabled real-time synchronization for planning, scheduling, and execution in
precast on-site assembly, Autom. Constr. 141 (2022), https://doi.org/10.1016/j.
autcon.2022.104397.
[76] R.D. Barkokebas, M. Al-Hussein, X. Li, VR-MOCAP-enabled ergonomic risk
assessment of workstation prototypes in offsite construction, J. Constr. Eng.
Manag. 148 (8) (2022) 04022064, https://doi.org/10.1061/(ASCE)CO.1943-
7862.0002319.
[77] M. Valinejadshoubi, A. Bagchi, O. Moselhi, Development of a BIM-based data
management system for structural health monitoring with application to modular
buildings: case study, J. Comput. Civ. Eng. 33 (3) (2019), https://doi.org/
10.1061/(ASCE)CP.1943-5487.0000826.
[78] P. Martinez, B. Barkokebas, F. Hamzeh, M. Al-Hussein, R. Ahmad, A vision-based
approach for automatic progress tracking of oor paneling in offsite construction
facilities, Autom. Constr. 125 (2021), https://doi.org/10.1016/j.
autcon.2021.103620.
[79] K.M. Rashid, J. Louis, Activity identication in modular construction using audio
signals and machine learning, Autom. Constr. 119 (2020) 103361, https://doi.
org/10.1016/j.autcon.2020.103361.
[80] B. Xiao, H. Xiao, J. Wang, Y. Chen, Vision-based method for tracking workers by
integrating deep learning instance segmentation in off-site construction, Autom.
Constr. 136 (2022) 104148, https://doi.org/10.1016/j.autcon.2022.104148.
[81] P. Martinez, R. Ahmad, M. Al-Hussein, A vision-based system for pre-inspection of
steel frame manufacturing, Autom. Constr. 97 (2019) 151–163, https://doi.org/
10.1016/j.autcon.2018.10.021.
[82] C. Feng, Y. Xiao, A. Willette, W. McGee, V.R. Kamat, Vision guided autonomous
robotic assembly and as-built scanning on unstructured construction sites, Autom.
Constr. 59 (2015) 128–138, https://doi.org/10.1016/j.autcon.2015.06.002.
[83] Z. Xu, Y.Z. Liang, Y.S. Xu, Z.Z. Fang, U. Stilla, Geometric modeling and surface-
quality inspection of prefabricated concrete components using sliced point
clouds, J. Constr. Eng. Manag. 148 (9) (2022), https://doi.org/10.1061/(ASCE)
CO.1943-7862.0002345.
[84] R.H. Son, K. Han, Automated model-based 3D scan planning for prefabricated
building components, J. Comput. Civ. Eng. 37 (2) (2023), https://doi.org/
10.1061/(ASCE)CP.1943-5487.0001055.
[85] F.X. Li, M.K. Kim, Mirror-aided registration-free geometric quality inspection of
planar-type prefabricated elements using terrestrial laser scanning, Autom.
Constr. 121 (2021), https://doi.org/10.1016/j.autcon.2020.103442.
[86] M. Noghabaei, Y.J. Liu, K.V. Han, Automated compatibility checking of
prefabricated components using 3D as-built models and BIM, Autom. Constr. 143
(2022), https://doi.org/10.1016/j.autcon.2022.104566.
[87] C. Rausch, R.D. Lu, S. Talebi, C. Haas, Deploying 3D scanning based geometric
digital twins during fabrication and assembly in offsite manufacturing, Int. J.
Constr. Manag. 23 (3) (2023) 565–578, https://doi.org/10.1080/
15623599.2021.1896942.
[88] X. Li, S. Han, M. Gül, M. Al-Hussein, Automated post-3D visualization ergonomic
analysis system for rapid workplace design in modular construction, Autom.
Constr. 98 (2019) 160–174, https://doi.org/10.1016/j.autcon.2018.11.012.
[89] J. Goulding, W. Nadim, P. Petridis, M. Alshawi, Construction industry offsite
production: a virtual reality interactive training environment prototype, Adv.
Eng. Inform. 26 (1) (2012) 103–116, https://doi.org/10.1016/j.aei.2011.09.004.
[90] A. Ezzeddine, B. García de Soto, Connecting teams in modular construction
projects using game engine technology, Autom. Constr. 132 (2021) 103887,
https://doi.org/10.1016/j.autcon.2021.103887.
[91] J. Cao, E. Vakaj, R.K. Soman, D.M. Hall, Ontology-based manufacturability
analysis automation for industrialized construction, Autom. Constr. 139 (2022),
https://doi.org/10.1016/j.autcon.2022.104277.
[92] W. Anane, I. Iordanova, C. Ouellet-Plamondon, BIM-driven computational design
for robotic manufacturing in off-site construction: an integrated design-to-
manufacturing (DtM) approach, Autom. Constr. 150 (2023), https://doi.org/
10.1016/j.autcon.2023.104782.
[93] N. Malik, R. Ahmad, M. Al-Hussein, Generation of safe tool-paths for automatic
manufacturing of light gauge steel panels in residential construction, Autom.
Constr. 98 (2019) 46–60, https://doi.org/10.1016/j.autcon.2018.11.023.
[94] E.M. Villanueva, P. Martinez, R. Ahmad, Target-path planning and
manufacturability check for robotic CLT machining operations from BIM
information, Autom. Constr. 158 (2024) 105191, https://doi.org/10.1016/j.
autcon.2023.105191.
[95] S. An, P. Martinez, M. Al-Hussein, R. Ahmad, BIM-based decision support system
for automated manufacturability check of wood frame assemblies, Autom. Constr.
111 (2020) 103065, https://doi.org/10.1016/j.autcon.2019.103065.
[96] S. An, P. Martinez, M. Al-Hussein, R. Ahmad, Automated verication of 3D
manufacturability for steel frame assemblies, Autom. Constr. 118 (2020), https://
doi.org/10.1016/j.autcon.2020.103287.
[97] A. Zhu, Z. Zhang, W. Pan, Crane-lift path planning for high-rise modular
integrated construction through metaheuristic optimization and virtual
prototyping, Autom. Constr. 141 (2022), https://doi.org/10.1016/j.
autcon.2022.104434.
[98] A.Y. Zhu, T.H. Dai, G.Y. Xu, P. Pauwels, B. de Vries, M. Fang, Deep reinforcement
learning for real-time assembly planning in robot-based prefabricated
construction, IEEE Trans. Autom. Sci. Eng. 20 (3) (2023) 1515–1526, https://doi.
org/10.1109/TASE.2023.3236805.
[99] A.Y. Zhu, P. Pauwels, B. De Vries, Smart component-oriented method of
construction robot coordination for prefabricated housing, Autom. Constr. 129
(2021), https://doi.org/10.1016/j.autcon.2021.103778.
[100] A. Garg, V.R. Kamat, Virtual prototyping for robotic fabrication of rebar cages in
manufactured concrete construction, J. Archit. Eng. 20 (1) (2014), https://doi.
org/10.1061/(ASCE)AE.1943-5568.0000134.
[101] J. Wang, S. Han, X. Li, 3D fuzzy ergonomic analysis for rapid workplace design
and modication in construction, Autom. Constr. 123 (2021) 103521, https://
doi.org/10.1016/j.autcon.2020.103521.
[102] J.W. Wang, X.M. Li, S. Han, M. Al-Hussein, 3D standard motion time-based
ergonomic risk analysis for workplace design in modular construction, Autom.
Constr. 147 (2023), https://doi.org/10.1016/j.autcon.2022.104738.
[103] Z. Zheng, Z. Zhang, W. Pan, Virtual prototyping- and transfer learning-enabled
module detection for modular integrated construction, Autom. Constr. 120 (2020)
103387, https://doi.org/10.1016/j.autcon.2020.103387.
[104] G. Darlow, J.O.B. Rotimi, W.M. Shahzad, Automation in New Zealand’s offsite
construction (OSC): a status update, Built Environ. Project Asset Manage. 12 (1)
(2022) 38–52, https://doi.org/10.1108/BEPAM-11-2020-0174.
[105] A. Grilo, R. Jardim-Goncalves, Value proposition on interoperability of BIM and
collaborative working environments, Autom. Constr. 19 (5) (2010) 522–530,
https://doi.org/10.1016/j.autcon.2009.11.003.
[106] Z. Lei, M.S. Altaf, Z. Cheng, H. Liu, S. Tang, Measurement of information loss and
transfer impacts of technology systems in offsite construction processes, J. Constr.
Eng. Manag. 149 (9) (2023) 05023011, https://doi.org/10.1061/JCEMD4.
COENG-13637.
[107] W. Anane, I. Iordanova, C. Ouellet-Plamondon, Modular robotic prefabrication of
discrete aggregations driven by BIM and computational design, in: Procedia
Computer Science, 2022, pp. 1103–1112, https://doi.org/10.1016/j.
procs.2022.01.310.
[108] H.R. Ding, M.X. Li, R.Y. Zhong, G.Q. Huang, Multistage self-adaptive decision-
making mechanism for prefabricated building modules with IoT-enabled
graduation manufacturing system, Autom. Constr. 148 (2023), https://doi.org/
10.1016/j.autcon.2023.104755.
[109] Z. Wang, T. Wang, H. Hu, J. Gong, X. Ren, Q. Xiao, Blockchain-based framework
for improving supply chain traceability and information sharing in precast
construction, Autom. Constr. 111 (2020) 103063, https://doi.org/10.1016/j.
autcon.2019.103063.
[110] X. Li, L.P.F. Wu, R. Zhao, W.S. Lu, F. Xue, Two-layer adaptive blockchain-based
supervision model for off-site modular housing production, Comput. Ind. 128
(2021), https://doi.org/10.1016/j.compind.2021.103437.
[111] R. Jin, J. Hong, J. Zuo, Environmental performance of off-site constructed
facilities: a critical review, Energ. Build. 207 (2020) 109567, https://doi.org/
10.1016/j.enbuild.2019.109567.
[112] A. Peiris, F.K.P. Hui, C. Dufeld, J. Wang, M. Gil Garcia, Y. Chen, T. Ngo,
Digitalising modular construction: enhancement of off-site manufacturing
productivity via a manufacturing execution & control (MEC) system, Comput.
Ind. Eng. 178 (2023) 109117, https://doi.org/10.1016/j.cie.2023.109117.
[113] Z. Wang, H. Hu, J. Gong, X. Ma, Synchronizing production scheduling with
resources allocation for precast components in a multi-agent system environment,
J. Manuf. Syst. 49 (2018) 131–142, https://doi.org/10.1016/j.jmsy.2018.09.004.
[114] C.-H. Ko, S.-F. Wang, Precast production scheduling using multi-objective genetic
algorithms, Expert Syst. Appl. 38 (7) (2011) 8293–8302, https://doi.org/
10.1016/j.eswa.2011.01.013.
[115] Z. Yang, Z. Ma, S. Wu, Optimized owshop scheduling of multiple production
lines for precast production, Autom. Constr. 72 (2016) 321–329, https://doi.org/
10.1016/j.autcon.2016.08.021.
[116] Y. Dan, G. Liu, C. Mao, K. Li, P. Xu, Flowshop scheduling optimization for multi-
shift precast production with on-time delivery, Eng. Appl. Artif. Intell. 127 (2024)
107163, https://doi.org/10.1016/j.engappai.2023.107163.
[117] B. Barkokebas, M. Al-Hussein, F. Hamzeh, Assessment of digital twins to reassign
multiskilled workers in offsite construction based on lean thinking, J. Constr. Eng.
Manag. 149 (1) (2023), https://doi.org/10.1061/(ASCE)CO.1943-7862.0002420.
[118] W.J. Meerding, W. Ijzelenberg, M.A. Koopmanschap, J.L. Severens, A. Burdorf,
Health problems lead to considerable productivity loss at work among workers
with high physical load jobs, J. Clin. Epidemiol. 58 (5) (2005) 517–523, https://
doi.org/10.1016/j.jclinepi.2004.06.016.
[119] D. Wang, F. Dai, X. Ning, Risk assessment of work-related musculoskeletal
disorders in construction: state-of-the-art review, J. Constr. Eng. Manag. 141 (6)
(2015) 04015008, https://doi.org/10.1061/(ASCE)CO.1943-7862.0000979.
[120] S. Yu, Y. Liu, D. Wang, A.S. Bahaj, Y. Wu, J. Liu, Review of thermal and
environmental performance of prefabricated buildings: implications to emission
reductions in China, Renew. Sust. Energ. Rev. 137 (2021) 110472, https://doi.
org/10.1016/j.rser.2020.110472.
[121] G. Correia Lopes, R. Vicente, M. Azenha, T.M. Ferreira, A systematic review of
prefabricated enclosure wall panel systems: focus on technology driven for
performance requirements, Sustain. Cities Soc. 40 (2018) 688–703, https://doi.
org/10.1016/j.scs.2017.12.027.
[122] C. Kasperzyk, M.-K. Kim, I. Brilakis, Automated re-prefabrication system for
buildings using robotics, Autom. Constr. 83 (2017) 184–195, https://doi.org/
10.1016/j.autcon.2017.08.002.
L. Xu et al.
Automation in Construction 170 (2025) 105945
22
[123] C.H. Yang, S.C. Kang, Collision avoidance method for robotic modular home
prefabrication, Autom. Constr. 130 (2021), https://doi.org/10.1016/j.
autcon.2021.103853.
[124] H. Li, C. Zhang, S. Song, S. Demirkesen, R. Chang, Improving tolerance control on
modular construction project with 3d laser scanning and BIM: a case study of
removable oodwall project, Appl. Sci. 10 (23) (2020) 1–21, https://doi.org/
10.3390/app10238680.
[125] P.-Y. Hsu, P. Angeloudis, M. Aurisicchio, Optimal logistics planning for modular
construction using two-stage stochastic programming, Autom. Constr. 94 (2018)
47–61, https://doi.org/10.1016/j.autcon.2018.05.029.
[126] Z.Q. Zhang, W. Pan, Multi-criteria decision analysis for tower crane layout
planning in high-rise modular integrated construction, Autom. Constr. 127 (2021)
103709, https://doi.org/10.1016/j.autcon.2021.103709.
[127] J.M. Davila Delgado, L. Oyedele, A. Ajayi, L. Akanbi, O. Akinade, M. Bilal,
H. Owolabi, Robotics and automated systems in construction: understanding
industry-specic challenges for adoption, J. Build. Eng. 26 (2019) 100868,
https://doi.org/10.1016/j.jobe.2019.100868.
[128] M. Gharbia, A. Chang-Richards, Y. Lu, R.Y. Zhong, H. Li, Robotic technologies for
on-site building construction: a systematic review, J. Build. Eng. 32 (2020)
101584, https://doi.org/10.1016/j.jobe.2020.101584.
[129] B.M. Tehrani, S. BuHamdan, A. Alwisy, Robotics in assembly-based industrialized
construction: a narrative review and a look forward, Int. J. Intell. Robot. Appl. 7
(3) (2023) 556–574, https://doi.org/10.1007/s41315-022-00257-9.
[130] R. Brandín, S. Abrishami, IoT-BIM and blockchain integration for enhanced data
traceability in offsite manufacturing, Autom. Constr. 159 (2024) 105266, https://
doi.org/10.1016/j.autcon.2024.105266.
[131] H. Rahimi, A. Zibaeenejad, A.A. Safavi, A novel IoT architecture based on 5G-IoT
and next generation technologies, in: 2018 IEEE 9th Annual Information
Technology, Electronics and Mobile Communication Conference (IEMCON),
2018, pp. 81–88, https://doi.org/10.1109/IEMCON.2018.8614777.
[132] Y. Yang, W. Pan, Automated guided vehicles in modular integrated construction:
potentials and future directions, Constr. Innov. 21 (1) (2021) 85–104, https://doi.
org/10.1108/CI-07-2019-0073.
[133] J.Y. Jang, S. Ahn, T.W. Kim, Cloud-based information system for automated
precast concrete transportation planning, Autom. Constr. 152 (2023), https://doi.
org/10.1016/j.autcon.2023.104942.
[134] S. Aheleroff, X. Xu, R.Y. Zhong, Y. Lu, Digital twin as a service (DTaaS) in industry
4.0: an architecture reference model, Adv. Eng. Inform. 47 (2021) 101225,
https://doi.org/10.1016/j.aei.2020.101225.
[135] D.-G.J. Opoku, S. Perera, R. Osei-Kyei, M. Rashidi, Digital twin application in the
construction industry: a literature review, J. Build. Eng. 40 (2021) 102726,
https://doi.org/10.1016/j.jobe.2021.102726.
[136] A.M. Madni, C.C. Madni, S.D. Lucero, Leveraging digital twin technology in
model-based systems engineering, Systems 7 (1) (2019) 7, https://doi.org/
10.3390/systems7010007.
[137] I. Osadcha, A. Jurelionis, P. Fokaides, Geometric parameter updating in digital
twin of built assets: a systematic literature review, J. Build. Eng. 73 (2023)
106704, https://doi.org/10.1016/j.jobe.2023.106704.
[138] S. Jaryani, I. Yitmen, H. Sadri, S. Alizadehsalehi, Exploring the fusion of
knowledge graphs into cognitive modular production, Buildings 13 (9) (2023)
2306, https://doi.org/10.3390/buildings13092306.
[139] Y. Jiang, X. Liu, Z. Wang, M. Li, R.Y. Zhong, G.Q. Huang, Blockchain-enabled
digital twin collaboration platform for t-out operations in modular integrated
construction, Autom. Constr. 148 (2023) 104747, https://doi.org/10.1016/j.
autcon.2023.104747.
[140] J.G. Lee, J. Seo, A. Abbas, M. Choi, End-Users’ augmented reality utilization for
architectural design review, Appl. Sci. 10 (15) (2020) 5363, https://doi.org/
10.3390/app10155363.
[141] X. Li, W. Yi, H.-L. Chi, X. Wang, A.P.C. Chan, A critical review of virtual and
augmented reality (VR/AR) applications in construction safety, Autom. Constr. 86
(2018) 150–162, https://doi.org/10.1016/j.autcon.2017.11.003.
[142] S.U. Rehman, I. Kim, J. Choi, Data-driven integration framework for four-
dimensional building information modeling simulation in modular construction: a
case study approach, J. Comput. Des. Eng. 10 (6) (2023) 2288–2311, https://doi.
org/10.1093/jcde/qwad100.
[143] Apple Vision Pro. Apple. https://www.apple.com/apple-vision-pro/;, 2024
[accessed 13 March, 2024 2024].
[144] Y.K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick, Y. Duan,
R. Dwivedi, J. Edwards, A. Eirug, V. Galanos, P.V. Ilavarasan, M. Janssen,
P. Jones, A.K. Kar, H. Kizgin, B. Kronemann, B. Lal, B. Lucini, R. Medaglia, K. Le
Meunier-FitzHugh, L.C. Le Meunier-FitzHugh, S. Misra, E. Mogaji, S.K. Sharma, J.
B. Singh, V. Raghavan, R. Raman, N.P. Rana, S. Samothrakis, J. Spencer,
K. Tamilmani, A. Tubadji, P. Walton, M.D. Williams, Articial intelligence (AI):
multidisciplinary perspectives on emerging challenges, opportunities, and agenda
for research, practice and policy, Int. J. Inf. Manag. 57 (2021) 101994, https://
doi.org/10.1016/j.ijinfomgt.2019.08.002.
[145] S.A. Prieto, E.T. Mengiste, B. García de Soto, Investigating the use of ChatGPT for
the scheduling of construction projects, Buildings 13 (4) (2023) 857, https://doi.
org/10.3390/buildings13040857.
[146] T. Brown, B. Mann, N. Ryder, M. Subbiah, J.D. Kaplan, P. Dhariwal,
A. Neelakantan, P. Shyam, G. Sastry, A. Askell, Language models are few-shot
learners, Adv. Neural Inf. Proces. Syst. 33 (2020) 1877–1901, https://doi.org/
10.48550/arXiv.2005.14165.
[147] DALL⋅E, OpenAI. https://openai.com/dall-e-3/;, 2024 [accessed 13 March 2024].
[148] Sora, OpenAI. https://openai.com/sora/;, 2024 [accessed 13 March 2024].
[149] H. You, Y. Ye, T. Zhou, Q. Zhu, J. Du, Robot-enabled construction assembly with
automated sequence planning based on ChatGPT: RoboGPT, Buildings 13 (7)
(2023) 1772, https://doi.org/10.3390/buildings13071772.
[150] Y. Pan, L. Zhang, Roles of articial intelligence in construction engineering and
management: a critical review and future trends, Autom. Constr. 122 (2021)
103517, https://doi.org/10.1016/j.autcon.2020.103517.
L. Xu et al.
Automation in Construction 170 (2025) 105945
23
