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Model and data management issues in the integrated assessment of existing building stocks

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The increasing population growth and urbanization rises the worldwide consumption of material resources and energy demand. The challenges of the future will be to provide sufficient resources and to minimize the continual amount of waste and energy demand. For the achievement of sustainability, increasing recycling rates and reuse of materials, next to the reduction of energy consumption has the highest priority. This article presents the results of the multidisciplinary research project SCI_BIM, which is conducted on an occupied existing building. Within SCI_BIM, a workflow for coupling digital technologies for scanning and modeling of buildings is developed. Laser scanning is used for capturing the geometry, and ground-penetrating radar is used for assessing material composition. For the semi-automated generation of an as-built BIM, algorithms are developed, wherefore the Point-Cloud serves as a basis. The BIM-model is used for energy modeling and analysis as well as for the automated compilation of Material Passports. Further, a gamification concept will be developed to motivate the buildings’ users to collect data. By applying the gamification concept, the reduction of energy consumption together with an automated update of the as-built BIM will be tested. This article aims to analyze the complex interdisciplinary interactions, data, and model exchange processes of various disciplines collaborating within SCI_BIM. Results show that the developed methodology is confronted with many challenges. Nevertheless, it has the potential to serve as a basis for the creation of secondary raw materials cadaster and for the optimization of energy consumption in existing buildings.
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Organization, Technology and Management in Construction 2020; 11: 2148–2157
Research Paper Open Access
Meliha Honic* and Iva Kovacic
Model and data management issues in the
integrated assessment of existing building stocks
DOI 10.2478/otmcj-2020-0011
Received November 14, 2019; accepted February 11, 2020
Abstract: The increasing population growth and urban-
ization rises the worldwide consumption of material
resources and energy demand. The challenges of the
future will be to provide sufficient resources and to mini-
mize the continual amount of waste and energy demand.
For the achievement of sustainability, increasing recy-
cling rates and reuse of materials, next to the reduction of
energy consumption has the highest priority.
This article presents the results of the multidisciplinary
research project SCI_BIM, which is conducted on an occu-
pied existing building. Within SCI_BIM, a workflow for
coupling digital technologies for scanning and modeling of
buildings is developed. Laser scanning is used for captur-
ing the geometry, and ground-penetrating radar is used for
assessing material composition. For the semi-automated
generation of an as-built BIM, algorithms are developed,
wherefore the Point-Cloud serves as a basis. The BIM-
model is used for energy modeling and analysis as well
as for the automated compilation of Material Passports.
Further, a gamification concept will be developed to moti-
vate the buildings’ users to collect data. By applying the
gamification concept, the reduction of energy consumption
together with an automated update of the as-built BIM will
be tested. This article aims to analyze the complex interdis-
ciplinary interactions, data, and model exchange processes
of various disciplines collaborating within SCI_BIM.
Results show that the developed methodology is con-
fronted with many challenges. Nevertheless, it has the
potential to serve as a basis for the creation of secondary
raw materials cadaster and for the optimization of energy
consumption in existing buildings.
Keywords: discipline models, data exchange, as-built BIM,
Material Passports, resources and energy optimization
1 Introduction
The demand for resources from nature is rising fast due to
the expected population growth from 7 billion to 9 billion
in 2050 (Programme des Nations Unies pour l’environ-
nement, 2011). Accordingly, the increasing demands will
lead to a significant amount of waste. Future challenges,
therefore, will be dealing with the upcoming waste as well
as the supply of sufficient land, material, and natural
resources. As the construction sector is responsible for
60% of the raw materials extracted from the lithosphere
(Bribián et al., 2011) and for 40% of energy-related CO2
emissions (Dean et al., 2016), this sector requires optimi-
zation regarding resources and energy efficiency.
The building stock represents the largest material
stock of industrial economies by being about as large as
reserves of primary resources in nature on a global scale
(Brunner and Rechberger, 2017), underlining the fact that
it is of long-term importance to maintain or frequently
recycle these urban stocks. Therefore, there is an urgent
need for the development of applicable methodologies to
build up knowledge of the existing stock. Since the exist-
ence of such knowledge would enable the assessment of
the building stocks’ performance and, moreover, enable
analysis and prediction. For both, materials assessment
and prediction and optimization of the energy demand, a
BIM (building information modeling)-model is required.
BIM, as an emerging tool, has the potential to serve
as a knowledge basis for follow-up material and energy
assessments since its potential for life-cycle optimization
of buildings has already been recognized (Fellows and
Liu, 2012). BIM enables modeling, analysis, and optimi-
zation regarding resources and energy efficiency of new
constructions as well as of the building stock. Through
coupling BIM with scanning methods such as laser scan
and ground-penetrating radar (GPR), a thorough assess-
ment of existing stocks can be conducted. Moreover,
inventories on the detailed material composition of build-
ings, such as Material Passports (MPs) and simulations
regarding energy consumptions, can be generated. The
BIM-based coupling of digital technologies for modeling
Open Access. © 2020 Honic and Kovacic, published by Sciendo.   This work is
licensed under the Creative Commons Attribution NonCommercial-NoDerivatives 4.0 License.
*Corresponding author: Meliha Honic, Technische Universität Wien,
Wien, Austria, E-mail: meliha.honic@tuwien.ac.at
Iva Kovacic, Technische Universität Wien, Wien, Austria
Honic and Kovacic, Model and data management issues in the assessment of building stocks  2149
and analysis has large potentials to support both reduc-
tion of the resources consumption and of the energy
demand.
In this article, the results of the ongoing funded
research project SCI_BIM “Scanning and data capturing
for Integrated Resources and Energy Assessment using
Building Information Modelling” are presented. The
research project received funding from the Austrian Min-
istry for Transport, Innovation and Technology through
the Austrian research promotion agency FFG (Österre-
ichische Forschungsförderungsgesellschaft). The project
aims to increase both the resources and energy efficiency
of buildings. Therefore, technologies and methods for
capturing and modeling (as-built BIM with geometry and
material composition) of buildings are coupled. By apply-
ing the gamification concept, users are integrated into the
process of updating the as-built BIM-model.
2 State of the art
At present, buildings consume >35% of the energy world-
wide and are responsible for about 40% of global CO2
emissions (Abergel et al., 2017). Due to worldwide rapidly
increasing consumption of resources and population
growth, dealing with resource scarcity is and will con-
tinue being a challenging task. To overcome these obsta-
cles, some strategies, such as “Urban Mining,” already
exist. Urban mining proposes to reuse or recycle the exist-
ing stocks to minimize the use of primary resources and
thus decrease the extraction of raw materials. However, to
apply the urban mining strategy, it is necessary to have
detailed knowledge about the existing stock and incor-
porated materials—a knowledge that is currently lacking.
Another strategy is introduced by the European Union’s
(EU) action plan for Circular Economy (CE), which pro-
poses to increase recycling rates to minimize the con-
sumption of raw materials, the upcoming of waste and
environmental impacts. CE aims to reach a resource-effi-
cient and low carbon economy by maintaining the value
of materials and resources in the economy as long as pos-
sible (European Commission, 2015). The achievement of
the EU goals 20-20-20 lies in the existing stocks and less
in new constructions since the rate of new constructions
is only 3% (Euroconstruct, 2018). However, the lack of
comprehensive knowledge on the exact material compo-
sition of the existing building stock is still the main obsta-
cle for a prediction of future material flows as well as the
increase of recycling rates. Even the assumptions of the
energy performance of the existing building stock mostly
build up on statistical analyses or energy passes following
time categorization.
The application of digital technologies, such as BIM,
offers extensive advantages in resource management
(Figure1). BIM, as an emerging tool in the Architecture,
Engineering and Construction (AEC) industry, has the
potential to serve as a knowledge database and moreover
Fig. 1: The life cycle of a building (https://hydronic-flow-control.com/en/page/our-services--building-life-cycle).
2150  Honic and Kovacic, Model and data management issues in the assessment of building stocks
it enables modeling of building elements including mate-
rials information and quantity determination (Eastman et
al., 2011; Bazjanac, 2006, Azhar, 2011). BIM allows applying
a life cycle perspective on facilities and construction pro-
jects. At the end of the life cycle, the demolition of an object,
stands the waste management, which benefits from a data
model with a wealth of information. The challenge for using
BIM technologies lies especially in digitizing the current
building stock and thereby making it accessible to the life
cycle orientated management in accordance with the BIM
philosophy. However, such methods of capturing are very
elaborate, and thus, the comprehensive data collected by
the laser scan and GPR should be most widely used. The
BIM models, which are enriched with the exact geometry
and material properties, should, therefore, serve not only
as a basis for the material cadaster but also for the life-cycle
analysis and optimization.
The geometry of existing building stocks is increas-
ingly being assessed by laser scanners, depending on the
purpose of the resulting point cloud in color or mono-
chrome. The point cloud obtained from the registered scans
can either act as the basis for an accurate to the millimeter
line evaluation or can be processed into a photorealistic
3D model, such that it is not only available for planners
and architects but also to the populace. A single scan can
generate up to millions of 3D points. The laser scanning
technology enables the follow-up generation of as-built
BIMs. For the structure of a model, the examined building
has to be scanned from different positions to finally merge
the generated point cloud into a model, which is currently
possible only with a semi-automated process. The gener-
ated point cloud can be converted into triangular surfaces,
which, however, cannot be transformed directly into BIM
objects. When modeling the BIM, the following tasks have
to be solved: (1) the geometry of the components must be
defined (“Which shape does the wall have?”); (2) catego-
ries and materials have to be assigned to the components
(“This is a brick wall.”); and (3) relationships and connec-
tions between the objects must be established (“Wall 1 is
connected to Wall 2 and is located on the second floor”).
Thereby, the current state-of-the-art generation of
as-built BIMs from point cloud or voxels is still a mainly
manual, time-consuming, and error-prone process.
Although there are already numerous methods and tech-
nologies for capturing data of as-built BIMs, these focus
mostly on the gathering of geometry—the gathering of the
material composition is currently little explored (Figure 2).
In this research project, the gamification approach
within the BIM environment will be tested through
user-participation. The gamification approach arises from
the game industry and is useful for buildings, where it
is not possible or too expensive to install sensors. Mer-
schbrock et al. (2014) tested the implementation of gam-
ification through user participation in the design stage of
a building. Rüppel and Schatz (2011) applied gamifica-
tion to simulate the user behavior during a case of fire.
Fig. 2: Comparison of the state-of-the-art as-built BIM data processing and SCI_BIM integrated data assessment and modeling method
(based on Huber et al., 2011 and Volk et al., 2014).
Honic and Kovacic, Model and data management issues in the assessment of building stocks  2151
In SCI_BIM, gamification is used to document the “as-is
state” of the building as well as to implement the user
behavior.
In our previous research, we developed a BIM-based
MP, which documents the material composition of a build-
ing (Honic et al., 2019). The MP serves as a planning and
optimization tool even in early planning phases with
regard to the efficient use of materials and subsequent
demolition, as documentation for the recycling of build-
ings and as the basis for an urban material cadaster at
the city level. The methodology developed for BIM-based
MPs is very well applicable and serves as a basis for “SCI-
BIM.” The acquired know-how can be further deepened in
“SCI-BIM” and extended through the integration of energy
efficiency aspects as well as user-participation within the
gamification approach, which is tested on a real use case.
3 Problem statement
The construction sector needs optimizations regarding
energy and resource efficiency since it is the sector with
the highest consumption of raw materials and moreover
consumes >35% of the worldwide energy (Dean et al.,
2016). To analyze and optimize the existing stock, on the
one hand, it is necessary to have material information for
enabling recycling of the materials in the stock, on the
other hand, it is necessary to optimize the energy con-
sumption of existing buildings. Currently, there is a lack
of knowledge on the material composition and construc-
tion of building stocks, which represents a major obstacle
for increasing recycling rates on the city-level (Brunner,
2011), as well as for the optimization of the energy con-
sumption of buildings. Research about the existing stock
shows that, for many materials, the secondary stock is
even larger than the primary resources, for example, Aus-
tria—a country that strongly depends on imports. In many
European countries, the situation is similar to Austria: due
to a low amount of primary resources, an import of raw
materials is necessary (Brunner and Rechberger, 2017).
In this article, a research gap, which is the compre-
hensive modeling of material composition and geometry
of existing buildings, based on capturing data by scan-
ning is addressed. Since new construction rate across
Europe is only about 3%, including residential, nonresi-
dential, and civil engineering sectors, this article focuses
on the existing stocks (Euroconstruct, 2018). The captur-
ing and modeling of buildings’ geometry is already well
explored; however, methods for capturing and modeling
of materials embedded in buildings in combination with
the geometry of buildings are largely lacking. In particu-
lar, this article focuses on the analysis of complex inter-
disciplinary interactions and data exchange processes
of various disciplines collaborating within the proposed
SCI_BIM process-design: planners, surveyors, computer
graphics, and software developers, and the handling
of their respective discipline-specific models and data.
Further on, the user-participation issues within this digital
ecosystem and the employed incentive mechanisms will
be addressed. Required interactions between the stake-
holders, data exchange challenges, and the workflows
will be analyzed.
The objective of this article is to build up a methodol-
ogy for the integrated assessment of the material compo-
sition and for the optimization of the energy consumption
of buildings. Therefore, a framework will be generated,
which shows the data flows, interactions between the
involved stakeholders and the processes in between.
Moreover, the applicability of the developed methodol-
ogy on a larger scale will be discussed. The main research
question is, if by coupling of laser scan- and GPR-technol-
ogy for follow-up BIM-model generation and creation of
discipline models for assessing the material and energy
efficiency, as well as the application of gamification, an
integrated assessment of the existing stock is possible.
4 Methodology
This research builds up on the interactions of various dis-
ciplines, such as planners, surveyors, computer graphics,
and software developers, and the handling of their respec-
tive discipline-specific models and data to generate an
“as-built” model, which serves as material inventory and
as an energy optimization model. The basis for this article
is the research project SCI_BIM, which is conducted at
Vienna University of Technology in collaboration with
Faculties for Civil Engineering (TU-IBAU, TU-FAR), Archi-
tecture (TU-BPI, TU-DAP), and Computer Science (TU-VC)
and the central institution for meteorology and geody-
namics—ZAMG Archeo Prospections (ZAMG), as well as
industrial partners—two engineering survey companies
C1 and C2. Throughout the research, an integrated data
assessment and modeling method are developed and
tested in a real case.
The methodology, as shown in Figure 3, is tested on a
building of an institute of Vienna University of Technol-
ogy. The use case is a single-story industrial building with
a typical construction out of reinforced concrete, whereby
the exterior walls are cladded with a corrugated sheet. The
2152  Honic and Kovacic, Model and data management issues in the assessment of building stocks
building consists of three main parts, which also vary in
their height: the storage has an area of 560m2 at a height
of 6.85m, the office including communal facilities has an
area of 275m2 at a height of 3.8m, and the lab part extends
over an area of 430m2 at a height of 5.9m. Throughout all
surveys, the building was occupied by the users.
The first step in this research was scanning the use
case to obtain the geometry and material composition of
the building. The geometry was determined through laser
scanning (by C1 and C2) and the material composition
through GPR scans (by ZAMG). Thereby C1 was using a
very precise point cloud with large data size (high-tech),
whereas C2 is using a low-tech variant with lower data size
and cost.
The second step, after the creation of the point cloud,
is the generation of a BIM-model. The generation of a
BIM out of a point cloud is confronted with many manual
steps. In this research, the development of a semi-auto-
mated process will be tested. Thereby, the first task, as
mentioned in the Section 3, is the automated recognition
of surfaces and objects through algorithms. After the rec-
ognition of surfaces, an automated generation of BIM
objects and consequently of the entire BIM-model should
be enabled.
In the third step, discipline models are created for
further energy assessment on the one hand and for mate-
rial assessments on the other hand, after the generation
of the BIM-model. For further energy assessments, a
simplified model is required, whereby for material assess-
ments and follow-up generation of a MP, enrichment of
the model through data obtained from ZAMG (GPR scan)
is necessary.
In the next step, two disciplines (energy and material
assessments) are working on their specific models to gen-
erate results for energy demand and resource consump-
tion of the building. Thereby, the aim is to optimize the
energy demand and to generate a MP for the building,
which shows the detailed material composition and the
recycling potential of the building.
The final step is to gather all data in one model—“as-
built” BIM—which is the digital twin of the existing build-
ing. Changes in the building, as well as user behavior, are
tracked by user participation through applying a gamifi-
cation concept. Thereby, the users of the building are con-
nected to the model through a smartphone app, through
which they track changes in the building and collect data
of the building.
The actual energy demand will be verified through
a comparison of the predicted energy demand with the
actual consumption. The real material composition will be
verified through invasive methods such as drilling as well
as through surveying by demolition companies, which
are commissioned to conduct a contaminant investiga-
tion in any case for demolition objects. Invasive methods
are possible since the building will be demolished in the
near future. The entire process, from scanning to the final
Fig. 3: Process design.
Honic and Kovacic, Model and data management issues in the assessment of building stocks  2153
“as-built” model, is accompanied by a cost–benefit anal-
ysis to evaluate the tested methodology and determine
the more feasible variant (high-tech or low-tech, costs) for
application on a larger scale (e.g., city level).
5 Data and process management
framework
The overall aim of the project is to increase the energy
and resources efficiency through the coupling of technol-
ogies and methods for capturing and modeling (as-built
BIM with geometry and material composition) an exist-
ing building. Finally, using the gamification concept,
the as-built BIM-model is being updated. However, the
generation of the as-built BIM-model and the mainte-
nance of the model by continuous update are challenging
tasks. The process requires a step-by-step data exchange
between the various disciplines and the participation
of the users. Figure 4 displays the workflow steps:
(1)data gathering, (2) pre-processing, (3) model creation,
(4)post-processing, and (5) data maintenance.
• Data are gathered by the surveyors (C1, C2, and
ZAMG) through scanning the use case from different
positions inside and outside the building. Thereby,
the geometry is determined through laser scanning
(by C1 and C2) and the material composition is deter-
mined through GPR-scans (by ZAMG). C1 is conduct-
ing a very detailed data collection of the geometry
using an expensive handheld scanner (device A). C2
tested three different devices for scanning: a high-cost
Fig. 4: Data and process management framework.
2154  Honic and Kovacic, Model and data management issues in the assessment of building stocks
handheld scanner (device A), a low-cost terrestrial
laser scanner (TLS) scanner that runs on a tablet
(device B), and a depth camera (device C). Device A
is the most expensive one, followed by device B and
finally C. After testing the devices, device C was not
considered in the project further, since scanning
took too much time even for a small room and more-
over the obtained results were not valuable. For that
reason, for further research and scanning of the use
case, only devices A and B were used. As C1 also uses
device A, C2 focused only on device B for the further
steps. Both devices generate point clouds, whereby
device A creates more detailed point clouds with
higher amount of data than device B, which means
that C1 tests the high-tech variant and C2 tests the
low-tech variant. With device A it was possible to scan
the whole building with only 4 scans, whereby with
device B 90 scans were conducted to obtain the whole
geometry of the buildings’ interior. The difference in
the amount of scans is reflected in the required scan-
ning-time: the scanning time of the high-tech variant
required only one-fourth of the time which the low-
tech scanner needed for scanning the whole building.
The materials composition of the elements is deter-
mined by using a GPR. The scanning with GPR was
conducted in three stages, since it was not possible
to scan all building elements due to furnishing. The
GPR sends and receives electromagnetic waves and as
a result it creates an image of the received waves and
their energy.
• After scanning, pre-processing of the point cloud
starts. The created point clouds are registered and
joined to one representative point cloud of the whole
building. In the next step, the point reduction is con-
ducted in PointCab (PointCab, 2019), whereby unnec-
essary data, such as furniture inside the building and
trees in the surrounding, are removed. ZAMG uses
GPR to scan the building components and defines
their material composition. The identification of the
materials occurs through mapping the determined
densities (through GPR) with those from a materials
list out of a material inventory. First, the results of the
measurements showed that a mapping of densities
will not work since the obtained result from the scan is
an image of the received electromagnetic waves, from
the walls and slabs. The GPR can distinguish between
two different materials with a varying density, but
cannot define the material composition immediately.
Therefore, the interpretation of the data is required to
define the possible material composition of a building
element. However, some rough material compositions
of elements were defined, which enable data handling
in GIS and the export of an Excel sheet for further use
for the MP.
• The model creation is conducted manually and
semi-automated based on the obtained point cloud.
The surveyors create the manual model by using Archi-
cad (Graphisoft, Archicad 2019). Therefore, the point
cloud is imported into Archicad and serves as base for
the manual modeling of the building components. The
manually created BIM-model by C1 is the reference
model and represents the basis for all other models.
C1 and C2 are both generating a manual BIM-model for
the use case, though building upon two different point
clouds. C1 is using a very precise point cloud with large
data size, whereby C2 is using the low-tech variant
with lower data-size. The investigation of the differ-
ences between the created models regarding quality
and time-effort, is part of the cost–benefit analysis,
as mentioned in the Section 4. The semi-automated
generation of the BIM is conducted by TU-VC. Based
on Huber et al. (2011) and Volk et al. (2014), as illus-
trated in Figure 2, a semi-automated generation of the
BIM-model in Archicad was tested. However, as the
research project is still ongoing, there are no results of
this part of the process yet.
• After the BIM-model has been created, the model is
post-processed by two different disciplines to create
on the one hand a building energy model (BEM)
(TU-BPI and TU-IBAU) and on the other a BIM for MP
(TU-IBAU and TU-FAR). For further energy simulation
and prediction, a simplification and post-processing
(e.g., adding of room stamps) is required to prepare
the model for the EnergyPlus Software (EnergyPlus,
2019). To create a MP for the building, the material
information from ZAMG has to be assigned to the
components of the BIM-model in Archicad, which is a
manual process, conducted by TU-IBAU. For the inte-
gration of materials’ information to the BIM-model, an
automated process will also be tested by TU-VC. For
the compilation of the MP, the BuildingOne (OneTools,
2019) tool is used, where also the final MP-document
is created. For the generation of the MP, MP-relevant
parameters (e.g., recycling potential, environmental
impacts, etc.) are assigned to the materials and quan-
tities obtained from the BIM-model. As BuildingOne
is an Add-On in Archicad and has a bi-directional
interface to Archicad, there are no challenges regard-
ing data exchange. The final as-built BIM merges the
energy model and material information together to
one model, which represents the as-built model and is
maintained through the gamification app.
Honic and Kovacic, Model and data management issues in the assessment of building stocks  2155
• Data are maintained by tracking the changes in the
building (e.g., static data: removing an inside wall)
and by constantly updating the BIM-model. Besides
the static data, also dynamic data are being tracked:
for example, the state of a window (opened/closed/
tilted) or the lightning (switched on/off), such that the
tracked data are also used as a basis for energy opti-
mization. As for the optimization of the energy effi-
ciency of a building, the user behavior plays a crucial
role, the user behavior is tracked through user partic-
ipation. Therefore, TU-DAP is creating a smartphone
app, through which users are connected to the as-built
model and supply the app with the required informa-
tion (e.g., window open or closed; the wall has been
removed, etc.). The automated update of the as-built
BIM-model is enabled through the connection of user
participation with the BIM-model, which is one of the
big challenges of this research. Therefore, the required
data formats have to be defined, and the smartphone
app has to be developed.
6 Applicability of the methodology
on a larger scale
The high- and low-tech variants are assessed and com-
pared regarding their applicability on a larger scale.
Therefore, a cost–benefit analysis is carried out, in which
the two variants are assessed and compared. Since the
project is still ongoing, the cost–benefit analysis is not
complete yet. The high-tech variant requires higher costs
for the acquisition of the device (A), thereby producing a
high-quality point cloud with large data amounts. The low-
tech variant involves the usage of a less expensive device
(B), thereby producing a point cloud with lower data size.
The generation of the BIM-model is based on the created
point cloud, whereby it will be analyzed, if the high- or
low-tech variant is more efficient for an automated BIM-
model generation. The high-tech variant might be more
efficient since a high-data amount would lead to a faster
BIM-model generation and accordingly to a more accu-
rate model. There is also the possibility that the high-tech
variant leads to an overload of data due to the required
time for pre-processing. The disadvantage of the low-tech
variant might be the inaccuracy of the point cloud and
time effort for pre-processing. The advantages of the low-
tech variant are the low costs for the device and fewer data
processing if the obtained data are sufficient. On a large
scale, the costs for pre-processing of data are significant,
since the costs will accrue each time a building is being
scanned and a BIM-model generated, whereby on the con-
trary, if an expensive device is bought once, it can be used
for many buildings and the costs will not add up.
7 Results
The results show that the created methodology based on
the coupling of technologies and methods for capturing
and modeling (as-built BIM with geometry and material
composition) enables the integrated assessment of the
existing stock. A fully automated methodology is not pos-
sible due to different obstacles such as insufficient data
collection and occupancy of the building, as described in
the next section. The cost–benefit analysis, which will be
completed at the end of the research project, will show
if the low- or high-tech variant is better applicable on a
broader scale such as city-level.
The main challenge within the whole process was
the occupancy of the use case throughout the data gath-
ering stage. The occupancy leads to difficulties in access-
ing all elements, and moreover, the users of the building
were disturbed in their daily praxis by scanning. For laser
scanning, the occupancy was not such a big challenge as
for the materials detection by ZAMG, since ZAMG could
only scan building components that were free from fur-
niture. The comprehensive analysis of all walls by ZAMG
requires many scanning stages. Another limitation of the
data-gathering stage was the accessibility of the build-
ing. In our research, the building was easily accessible
from three sides, since there is no building closely next
to it. However, the southwest facade of the building is
surrounded by a slope and trees, which made the access
difficult. Apart from the accessibility, an overload of data
was identified as another challenge (Figure 5). Since the
building was surrounded by trees and other buildings
(far away) as well as occupied and furnished during the
laser scanning phase, an effortful reduction process was
required (Figure 6). Due to the mentioned overload of
data, uncertainties occurred, as it was difficult to deter-
mine, for example, if a scanned object is just furniture or
part of the building in the point cloud. Therefore, it is dif-
ficult to verify if the modeled BIM is 100% correct, such
that a comparison of the various BIM models (C1 and C2),
existing plans, and on-site observations are necessary.
As user participation is necessary to keep the BIM-model
updated, the user participation is linked to the gamifica-
tion app, which motivates the users to participate in the
process through the distribution of little prices for certain
achievements (e.g., first person that recognized a change
2156  Honic and Kovacic, Model and data management issues in the assessment of building stocks
in the building and entered it in the app). However, there
still exists the risk that the intended workflow does not
work in every use case if the users are not willing to partic-
ipate. In the pre-processing stage, the obtained data from
ZAMG require further interpretation, since the output data
are an image of the electromagnetic waves received from
the radar antenna which does not give any information
about the exact material composition.
8 Conclusion
This article presents the results of the research project SCI_
BIM. The innovation of this project is the coupling of laser
scanning and GPR technologies as well as methods for
modeling to obtain an as-built BIM of an existing building
for follow-up assessment and optimization of the energy
consumption and recycling potential. The research was
conducted on a real use case, which is an occupied build-
ing of Vienna University of Technology. For capturing the
geometry, laser scanning-technology (hand-held scanner
and terrestrial laser scanner) was used to obtain the Point-
Cloud and BIM-model in the next step. To determine the
material composition of the use case, GPR technology was
applied, which delivered building components after the
interpretation of the image of the electromagnetic waves.
The main result is the developed methodology for the
integrated assessment of the material composition as well
as for the optimization of the energy consumption of build-
ings. Therefore, a data and process management frame-
work, displaying the core tasks of each discipline as well as
the software and data exchange interfaces for all stages by
starting with data gathering, followed by pre-processing,
model creation, post-processing, and finally concluding
with data maintenance, has been created. The main chal-
lenge within the workflow was identified as handling of
the discipline-specific models and data. Other challenges,
such as the fact that the building was occupied through-
out the scanning period which required further scanning
tasks, the motivation of the users to participate in the
gamification process, as well as the data overload through
scanning, and lack of interdisciplinary knowledge were
faced. To enable smooth data and information exchange,
it is crucial to determine the data exchange formats and
their interfaces by conducting team meetings before each
step. Another result is the conducted cost–benefit anal-
ysis, which evaluates the developed methodology from
Fig. 5: Point cloud of the use case and the surrounding (© C1).
Fig. 6: Panorama of the interior of the use case (© C1).
Honic and Kovacic, Model and data management issues in the assessment of building stocks  2157
scanning to generation and maintenance of the as-built
BIM regarding time-effort and costs, by comparison, a
low- and a high-tech variants. Apart from that, the cost–
benefit analysis tests the applicability of the developed
methodology for generating secondary raw materials
cadaster on the city-level and assesses its feasibility. As
the research project is still ongoing, the cost–benefit anal-
ysis is still not complete.
By application of the developed methodology on other
use cases, extensive information on the existing stock can
be obtained. The existence of comprehensive informa-
tion could serve as a basis for a secondary raw materials
cadaster, which displays the embedded materials on,
for example, city-level. On a macro-economic scale, the
existence of a secondary raw materials cadaster could
increase the recycling of valuable materials in the stock,
thus decrease the dependency on imports of primary
materials. Moreover, the accordingly generated secondary
raw materials cadaster would enable the generation of a
digital platform, where the obtained information could be
embedded in order to make it available for the public.
Acknowledgments
The authors would like to acknowledge the support by
the Austrian Ministry for Transport, Innovation and Tech-
nology through the Austrian research promotion agency
FFG (Österreichische Forschungsförderungsgesellschaft),
Grant No. 867314.
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