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A study on 3D/BIM-based on-site performance measurement system for building construction


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Performance measurement has become conventional in construction management. However, it is non-trivial to establish an effective system to capture, evaluate, and feedback the on-site data. Whilst 3D object–based technology prevails in the industry, project managers remain accustomed to text-based system. Unstructured data format requires a tremendous amount of time and effort when analyzing a project in detail. To overcome this issue, an extensive literature review in conjunction with industry feedback has been conducted. Based on this preliminary investigation, the authors have proposed an on-site performance measurement system that interlinks 3D object with a spreadsheet. The system enables project managers to recognize the performance and/or productivity of a project in real time and helps find timely remedies in updating the original plan. One of the key findings includes that the identified eight on-site productivity factors should be embedded into a 3D model in effectively measuring the performance for a particular project. The new system also provides a useful tool in managing the productivity of a project. The main contribution of this study is the proposition and verification of a novel methodology that effectively manages on-site performance information by relating 3D objects with productivity factors in building construction.
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Journal of Asian Architecture and Building Engineering
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A study on 3D/BIM-based on-site performance
measurement system for building construction
HeeSung Cha & Jun Kim
To cite this article: HeeSung Cha & Jun Kim (2020): A study on 3D/BIM-based on-site
performance measurement system for building construction, Journal of Asian Architecture and
Building Engineering, DOI: 10.1080/13467581.2020.1763364
To link to this article:
© 2020 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group on behalf of the Architectural
Institute of Japan, Architectural Institute of
Korea and Architectural Society of China.
Published online: 22 Jun 2020.
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A study on 3D/BIM-based on-site performance measurement system for
building construction
HeeSung Cha
and Jun Kim
Department of Architectural Engineering, Ajou University, Suwon, South Korea
Performance measurement has become conventional in construction management. However,
it is non-trivial to establish an eective system to capture, evaluate, and feedback the on-site
data. Whilst 3D objectbased technology prevails in the industry, project managers remain
accustomed to text-based system. Unstructured data format requires a tremendous amount of
time and eort when analyzing a project in detail. To overcome this issue, an extensive
literature review in conjunction with industry feedback has been conducted. Based on this
preliminary investigation, the authors have proposed an on-site performance measurement
system that interlinks 3D object with a spreadsheet. The system enables project managers to
recognize the performance and/or productivity of a project in real time and helps nd timely
remedies in updating the original plan. One of the key ndings includes that the identied
eight on-site productivity factors should be embedded into a 3D model in eectively measur-
ing the performance for a particular project. The new system also provides a useful tool in
managing the productivity of a project. The main contribution of this study is the proposition
and verication of a novel methodology that eectively manages on-site performance infor-
mation by relating 3D objects with productivity factors in building construction.
Received 1 November 2019
Accepted 8 April 2020
3D/BIM; information
technology; on-site
information; performance
measurement; productivity
1. Introduction
1. 1. Research background
Accurate project planning and control have long
been important for determining the success of pro-
jects (Russell 1993; El-Gohary, Aziz, and Abdel-Khalek
2017; Song and AbouRizk 2008; Tserng and Lin 2004;
Taneja et al. 2010). However, the accuracy of project
performance management depends on a wide range
of data accumulation parameters such as time, cost,
quality, and other issues (Cha and Kim 2011). Many
endeavors have been devoted to acquiring detailed
information from current and historical projects to
eectively measure performance. Traditionally, sev-
eral on-site construction information sources (i.e.
laborers, materials, equipment, weather, work pro-
gress, locations) have been utilized to acquire data
in the form of daily reports. In many cases, project
performance has been dicult to manage because
of poor data-capturing techniques (Martínez-Rojas,
Marín, and Vila 2015; Feng, Chen, and Huang 2010).
First, it takes considerable time and eort to extract
on-site productivity information from project docu-
ments (Xie, Fernando, and AbouRizk 2011; Oral and
Oral 2007). Moreover, additional eorts must be
taken to use captured historical data. Second, on-
site information is stored in the form of text. Thus,
it is troublesome to determine the precise character-
istics of a target project. Third, at construction job
sites, project managers have diculty inputting
detailed levels of performance data into the informa-
tion management system. Therefore, seemingly trivial
data is often neglected (McCullouch and Gunn 1993).
Fourth, project performance is heavily inuenced by
various project characteristics (size, location, weather,
and labor availability). Even if the data were to be
successfully captured, additional eorts would be
required to verify whether they were meaningful.
Therefore, project practitioners are often reluctant
to employ on-site performance management systems
(Gelisen and Gris2014, Nasir et al. 2012). As such,
project performance is commonly measured from
personal experience, judgment, and industry stan-
dards, which are not specic to target projects.
Building information management (BIM) systems
have been widely developed for the construction
industry and used in various disciplines, including
design-error detection, document management, and
energy and risk analyses (Eastman et al. 2008). It is
important to note that BIM can be used as eective
performance measurement tools from the perspective
of one or multiple projects having a digital library (Cha
and Lee 2018).
This study proposes a 3D/BIM framework that col-
lects, analyzes, and predicts project performance by
generating eld information for minimizing additional
management work and demonstrating validity by pre-
dicting and tracking future project.
CONTACT HeeSung Cha Department of Architectural Engineering, Ajou University, Suwon, Korea
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and
Architectural Society of China.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. 2. Research methodology
Project performance is a function of input and output.
Well-performed projects produce the same output
with less input. For the purpose of research, output is
conned to project percent completion, whereas input
is conned to project resources (material, manpower,
and equipment). A case study of the proposed system
was demonstrated within the steel erection work of
a commercial building project. The information in the
study was derived from standard daily reports widely
used by general contractors in South Korea. In terms of
productivity factors, the research used predetermined
inuential factors based on literature reviews.
A detailed research methodology is depicted in
Figure 1.
Upon clarifying problems, the authors investigated
prior studies related to construction productivity man-
agement. This led to important considerations on per-
formance measurement systematization that
overcomes research barriers. Additionally, a eld sur-
vey was conducted to account for the potential pro-
ductivity factors specic to the steel erection work.
From previous studies, a BIM-based system was
proposed and system requirements were established.
Systems were constructed to suggest robust
approaches in managing productivity information.
During this phase, a step-by-step guide was prepared
for the case project. Finally, the utilization of perfor-
mance information was validated in terms of its applic-
ability in a real case study of a construction project.
2. Literature review
2. 1. Denition
Performance and productivity have long been inter-
changeably used in the industry. (Dozzy and AbouRizk
1993). Although performance is regarded as the out-
come of work, productivity is more often related to
work eciency. Historically, project performance was
regarded a measure of consequences of past actions
focused on nancial elements. Recently, however, lag-
ging nancial measures were criticized because they
neglected continuous knowledge, information, and
client satisfaction improvements. Productivity denes
how much and how well a project performs based on
the resources used. Productivity is, therefore, a key
performance area that many project managers con-
sider as crucial. A comprehensive research review was
conducted in terms of denition, inuential factors,
measurement, and management of performance and/
or productivity information.
2. 2. Productivity/performance inuential factors
Construction productivity has recently acquired
research attention because of inuential factors that
have been scrutinized across a variety of construction
processes and building types (Thomas and Yiakoumis
1987; Zayed and Halpin 2004; Dai, Goodrum, and
Maloney 2009; Khanh and Kim 2014; Nguyen and
Nguyen 2012; Sonmez and Rowings 1998; Gelisen
Figure 1. Research process.
and Gris2014; Sherekar and Tatikonda 2016;
Durdyev, Ismail, and Kandymov 2018; Jarkas and Bitar
2012, Heravi and Eslamdoost 2015; Boussabaine and
Du1996; Ellk 2018). These authors classied eight
categories based on the characteristics of key inuen-
tial issues: management, laborers, buildings/objects,
communications, environments, tools/machines,
materials, and jobs/methods. All of the eight inuential
factors are respectively delineated as follows.
Management Eorts can be further divided into
manpower, capability, and conditions related to con-
trolling particular projects. Among others, manpower
(i.e. lack of supervision, inadequate sta) directly
aects construction productivity. Capability issues are
linked to the trustworthiness of supervision, proper
coordination, and eective communication. These fac-
tors can indirectly inuence project performance.
Finally, management conditions are important,
because job circumstances, administration support,
and project management styles can cause productivity
Laborer Issues are divided into labor availability,
conditions, and motivations. Labor availability
includes capacity, team support, crew size, skillful-
ness, qualication, and technical excellence. Labor
conditions indicate time, space, and environment of
labor, including percent overtime, physical fatigue,
shortage of experienced labor, lack of periodical
meetings, lack of rest, sucient facilities, and accom-
modation. Labor motivation can be either attitude or
mindset. It may include absenteeism, accidents,
levels of familiarity with current job, or other
Building/Object Issues are related to design charac-
teristics (project type, size, and complexity). This may
include building materials, project statuses, work
types, dynamic structures, design complexities, or
poor buildability. Notably, building design is consid-
ered a critical productivity factor.
Communication Issues are related to the manage-
ment of information generated at the job site. This
issue inuences acquisition, transfer, and transfor-
mation of information among project participants.
Examples of this factor include communication,
coordination errors, and unrealistic deadlines.
Additionally, drawing errors, complexity of construc-
tion information, and ambiguity of specications
can impact communication problems. Similarly,
sequencing problems, schedule compressions, and
frequent change orders are issues related to pro-
ductivity improvement.
Environment Issues relate to both physical and reg-
ulatory connement, including weather, policy, and
contractual limitations. Traditionally, poor weather
conditions (i.e. high/low temperature, humidity, wind,
precipitation) are direct factors in productivity loss. The
strictness of a regulatory system (i.e. engineering
standards, municipal policies, permit approvals,
working days, project organizational cultures) aects
productivity negatively, because it dierentiates con-
ventional work practices by requiring additional eorts
when executing a project. The examples of contractual
limitations include subcontract percentages, incentive
schemes, payment delays, levels of direct or subcon-
tract labor, and client inuence.
Equipment Issues are direct inuential factors of pro-
ductivity. Equipment type, maximum capacity, avail-
ability, and machine superiority strongly inuence
jobsite productivity. Proper decision-making when
selecting equipment results in tremendous productiv-
ity improvements. Machine reliability, availability, and
lack of tools/operatives are also closely linked to con-
struction eciency.
Material Issues provide a key resource. Inferior mate-
rials, scattered materials, and unavailable materials
negatively inuence project productivity. Late, inade-
quate, unsuitable, or over-costed supplies are also fac-
tors under this category.
Job/Method Issues aect construction methods and
environments. A specic building method (i.e. excava-
tion, soil treatment, roong, and cladding) explicitly
aects productivity, because they vary in terms of
experience. Other issues include rework, space availabil-
ity, quality problems, and inspection by the engineers.
2. 3. On-site productivity information
The above eight categories are commonly addressed
in daily work progress reports at construction job sites.
Daily report is a key document in which important
information is recorded to manage and control
a project. However, the use of this information is very
limited, because the data is text-based and hard to
organize. There is no linkage between daily reports
and building objects as well. Accordingly, it is non-
trivial to extract exact performance amounts asso-
ciated with particular activities. As such, enormous
amounts of data often remain useless and disappear
when the project is completed. This type of poor data
management results from the disorganization of data
structures and failures to link data with building com-
ponents and objects (Cha and Lee 2018).
There have been many studies on construction
information using various technologies to derive
future productivity. For example, Tserng and Lin
(2004) conducted a research to accumulate and utilize
activity-level information. In detail, it presented a novel
process for data accumulation, classication, storage,
and reuse. This information system was intended to be
used for various types of management. Boussabaine
and Du(1996) proposed a method to estimate pro-
ductivity by constructing an expert system based on
survey results.
Recently, a new technology-driven data analysis is
another research endeavor, including advanced site
information management organizer (ASIMO), 3D mod-
eling, multi-dimensional personal digital assistant (MD-
PDA) (Oh, Park, and Kim 2005; Tserng, Ho, and
Jan 2014; Xie, Fernando, and AbouRizk 2011). In this
area, many researchers analyze productivity using BIM
(Gelisen and Gris2014, Feng, Chen, and Huang 2010;
Shan and Goodrum 2014). BIM provides 3D objects for
visually managing information. This is a very powerful
technique for eld data managers (Tserng, Ho, and
Jan 2014).
In the study of Tserng, Ho, and Jan (2014), the
performance of the construction was visually displayed
on a screen by using BIM to enhance information
usability. However, it has a disadvantage in that
a tremendous amount of information was needed at
an initial stage. As such, a computerized system should
be developed to maximize the benet.
3. Preliminary industry investigation
3. 1. Expert survey results
To understand current on-site productivity/perfor-
mance measurements, a rigorous expert survey
was conducted. There were 10 participants having
industry experience of 1020 years. Based on the
survey, productivity/performance data were mana-
ged using a standard format, including a daily work
report. However, this reporting system was
designed to show day-to-day work progress by
describing resource inputs, performance outputs,
and specic site conditions (i.e. weather, change
signicant inuential factors regarding the perfor-
mance levels of the jobsite. Thus, there was no
eective tool for capturing, storing, and recognizing
the factors.
Many practitioners have agreed that the informa-
tion from a daily work report is not fully used or
eectively analyzed. Furthermore, reporting systems
are regarded as unimportant. Thus, this process is
typically delegated to inexperienced personnel.
A vastly improved and easy-to-manage daily report
information management system is required. Thus,
this study analyzes daily reports based on the men-
tioned eight categorized inuential factors linked to
the current daily reporting system, as shown in
Figure 2.
As seen in Figure 2 (upper), laborer, environmen-
tal, material, tool/machine, job/method issues are
easily recognized. Likewise, management and com-
munication issues can be inferred from the com-
ments or special-note sections. Finally, the building/
object issues can be obtained when the 3D model is
linked to the daily report as depicted. A detailed
strategy of the datalink structure between the 3D/
BIM model and daily report is provided in the follow-
ing section.
Figure 2. Example of daily progress report.
3. 2. Salient ndings from investigations
As described earlier, a rigorous literature review
revealed that there were eight categories related to
the construction productivity inuential factors.
Because productivity plays a crucial role in performance
enhancement, project managers were asked to identify
and control one or more factors during the construction
phase. Currently, on-site construction productivity and/
or performance information must be recorded in
a standard form. On-site construction data require tre-
mendous and tedious workloads in a capture and retrie-
val process. In practice, construction engineers and
managers use a standard worksheet-based program to
reduce this workload. Microsoft Excel
is the dominant
software tool. Current practices, however, fail to record,
store, analyze, retrieve, and re-use construction produc-
tivity data captured in this way. Rather, much data is
dispersed and buried once the project is completed.
This practice shows that construction productivity is as
bad or even worse than before, as claimed. Finally,
recent BIM technologies have enabled the wide applica-
tion of information systems at construction job sites. By
linking productivity and/or performance data with 3D/
BIM objects, a more powerful information management
system can be developed, enabling project managers to
eciently interlink a variety of data with design ele-
ments. Moreover, when meaningful information can
be used later, storage should be visually identied and
distinguished. From the preliminary investigation, it is
strongly required to develop a 3D/BIM-linked informa-
tion management system to recognize/identify/control
the productivity/performance of a project from the per-
spective of a job-site activity.
4. Development on-site performance
measurement system (OPMS)
4. 1. Conceptual model of OPMS
The system has two dierent types of data ow: text-
and object-based. Because construction data are gen-
erated from daily progress reports, these two formats
are extracted from a standard Excel template. The
unstructured dataset is transferred into a ready-to-
use database by linking the 3D objects embedded in
the 3D/BIM model. The 3D object model can instantly
provide both locational and quantiable data.
By tracking the pre-determined 3D object library,
project progress can be measured eectively. Without
this tracking process, the raw data obtained from the
jobsite must be re-organized. In current practice, this
work is often neglected because of insucient on-site
human resources. When the raw data are generated or
inter-linked with the 3D/BIM model, a productivity
database can be established, as shown in the bottom
of Figure 3.
The database is automatically produced from the
daily reports and 3D objects. Thus, a detailed level
of analysis, in terms of productivity and performance
of a particular project, can be conducted. For exam-
ple, daily productivity loss/gain trend can be recog-
nized by plotting the time-series graph of the target
project. Project managers can forecast the perfor-
mance of a project by predicting each productivity
inuential factor. Thus, the OPMS comprises three
components: BIM-based product models, Excel-
based work reports, and graphic-based analyses.
The BIM-based product model is a tailored 3D object
model that shows embedded building objects and
detailed construction information. Excel is the stan-
dard format linked to the predened database. The
nal output of performance/productivity is graphi-
cally provided to present the accurate status of the
target project. A detailed demonstration of this con-
ceptual algorithm is discussed in the following
4. 2. Case study demonstration
To verify the applicability of the OPMS, the authors pilot-
tested a voluntary construction project. This building
was a commercial oce project located in Seoul. Total
construction costs amounted to 55 USDM with
a construction period of 26 months (November 2016
to December 2018) (Figure 4). Owing to site restrictions,
the project applied top-down non-shoring excavation
technology, whereby both excavation and structural
work occurred simultaneously. For the purpose of this
study, a 7-month period (June to December 2017) was
chosen to demonstrate the system. The entire set of
daily work reports was analyzed and incorporated into
the system. As shown in Figure 4(a), the major activities
included earthwork and sulfate-resisting cement
(1) 1
Step: 3D Modeling
In OPMS, 3D/BIM modeling is a prerequisite for
input performance data in the system. For this project,
the authors completed a 3D/BIM model using two-
dimensional (2D) drawings, as shown in Figure 5.
Using the structural drawings of the project (upper
part of Figure 5), the steel components were modeled
using 3D design software (i.e. Sketch-up
). In the
model, each structural member (i.e. bracket, strut, PRD
pile, H-beam, supporter, raker, and gusset plate) were
identied and embedded in the BIM library, as shown in
Figure 6. When modeling each component, property
information (i.e. size, dimension, location, schedule, zon-
ing, weight) were inputted into the BIM model.
(2) 2
Step: Daily Work Progress Reporting
After 3D/BIM modeling, daily work progress was
reported using Excel
, as shown in Figure 2. Using
the standard form, specic work progress information
was included with the aforementioned eight
productivity inuential factors. Project performance,
obtained by computing how much the steel members
were delivered, was automatically computed using the
pre-established BIM model. As seen in Figure 7, the
green steel members (right) were installed on site, and
the delivered items were colored yellow for June 2017
(left). The daily amount of installation amounted to
940 kg, as seen on the screen.
(3) 3
Step: Database Analysis
The 7-month daily progress reports were incorpo-
rated to analyze project performance. The total
quantity of work performed for the specic date was
automatically computed based on the 3D/BIM soft-
ware when the object was linked to the assigned
database. Dierent colors were used to allocate
whether the object was delivered,”“installed,or
completed.When the model is linked with the loca-
tion information in conjunction with the daily work
report, one can easily recognize progress. Because
the conventional reporting system is dicult to under-
stand with regard to work performance, a graphical
chart is useful for identifying project performance. As
Figure 3. Conceptual data model for OPMS.
seen in Figure 8, OPMS eectively demonstrates the
trend of performance in terms of laborers, material
deliveries, and installations of steel for the analysis
period. Additionally, the productivity information
embedded in the daily work progress report could be
eectively monitored in real time, as seen in Figure 7.
All productivity information is linked to the sys-
tem-generated database. The demonstration result
is depicted in Figure 8 in terms of manpower,
material delivery, and installation. The results
appear volatile according to date, and installation
performance is dependent on both material delivery
and labor availability. The performance level of late
June is higher than that of mid-September,
although the two resources are nearly the same.
This phenomenon frequently happens at job sites
(4) 4
Step: Productivity/Performance Feedback
The nal step of the OPMS nds the relationship
between project performance and productivity.
This eort is required to predict the future perfor-
mance and to provide corrective actions to
improve productivity. By transforming the data-
base of the previous step into a graphical diagram,
the construction personnel can easily recognize
the current status of the project in terms of
productivity and performance (see Figure 9). The
input information (i.e. manpower, materials, equip-
ment, work contents, productivity factors) is stored
in the database and analyzed to monitor produc-
tivity and predict performance. As seen in Figure 9,
the day-to-day cumulative number of steel mem-
bers of actual and planned work are depicted. This
graph is obtained from the OPMS database. The
manpower productivity trend is provided in the
form of a bar chart. This chart is created by
dividing day-to-day steel-member installations by
the number of laborers on site. Thus, the project
managers can easily monitor project performance
in consideration of resource inputs and outputs.
OPMS also enables project participants to provide
critical factors that can be linked to poor performance
by indicating pre-dened productivity inuential fac-
tors. For example, Figure 9 shows a signicant drop in
labor productivity after June 8
(5 t/person). The OPMS
system can promptly link the daily report to the
corresponding day. As seen in Figure 9, the database
shows that there were poor levels of productivity inu-
ential factors (i.e. site environments, tools/machines,
and job/method categories). Thus, the raw data related
to on-site information of the daily report would be
more valuable if it were managed via the proposed
Figure 4. Case study project overview.
(a). project exterior view, (b). construction work in progress c. project schedule)
4. 3. System validation
It is widely accepted that an academic idea or a system
can be validated through direct examination of the
knowledge base from an expert or a non-expert. The
proposed system validation was conducted using an
expert survey. In the construction area, the required
number of experts surveyed was 810, each having at
least 5 yearsexperience (Yoon and Cha 2018). The
proposed system was veried in terms of eectiveness
and eciency. The detailed questions and the mean
value of each question are provided in Table 1.
The survey was conducted with 10 eld construction
managers having over 7 yearssite work experience. The
scores were estimated using a Likert seven-point scale
(i.e. 1 = very dissatised; 2 = moderately dissatised;
3 = slightly dissatised;4=neutral;5=slightlysatised;
6 = moderately satised; and 7 = very satised). These
results illustrate that performance improvements mea-
sured using OPMS were valid and reasonable when
compared with the current system. Every expert con-
rmed that the current system lacked performance ana-
lysis and that the proposed system could provide an
appropriate solution for this limitation.
From the survey, an average score of 5.13 indicates
that OPMS was eective in measuring on-site perfor-
mance. For Q.4, a relatively low score of 4.58 was
obtained, because it took some time for respondents
to link performance data to the 3D model. Therefore, it
required additional eort when inputting the daily
work data into the system. It is, therefore, recom-
mended to automatically link the text and object
data by providing a standard input system at the
beginning stages to reduce the time-consuming eort.
It is noteworthy, however, the validation is limited in
Figure 5. 2D drawings (up) and 3D modelling (down).
Figure 6. Screenshot of exemplary 3D object.
terms of statistical signicance. A full scale validation is
required by expanding the number of respondents
5. Conclusions
This research proposed a novel methodology for input-
ting, processing, and outputting data to solve the pro-
blem of on-site productivity and/or performance
information not being eectively managed. By develop-
ing an OPMS, it was found that productivity information
was not properly managed and that there was much
room for improvement at the job site. From preliminary
investigations, eight productivity factors were derived
and incorporated into the system presented in this study.
To prevent on-site administrators from spending
unnecessary time with documentation, an informa-
tion input method was suggested based on the 3D/
BIM technology, such that it could be easily used in
productivity analysis. Through this process, it is now
possible to input productivity information using
a process similar to existing daily recording prac-
tices. Furthermore, it is possible to eectively man-
age information input by storing it in a BIM
database provided by the system. This case study
demonstrates that the productivity and perfor-
mance of information is eectively managed and
useful when predicting future trends and providing
potential corrective actions.
The main contribution of this research is twofold.
First, the study identied eight on-site productivity
factors that play crucial roles in measuring the levels
of performance in a particular project. Second, the new
system (i.e. OPMS) provided a useful tool for managing
the productivity and performance of construction
information and project performance. More speci-
cally, BIM object-based productivity/performance
management information oers higher potential for
capturing real time performance data.
Figure 7. Automatic quantity computation of 3D model.
Figure 8. Graphical data analysis from OPMS system.
However, there are some limitations of this study as
follows. First, the individual productivity factors are dif-
cult to dene and are inuenced by other factors.
Although key elements were derived from previous
studies and interviews, factor quantications and
relative importance should be incorporated. It is also
recommended that the inuential factors should be
quantitatively measured to be linked with the captured
data. Second, the 3D/BIM model objects containing on-
site productivity data should be linked and
Figure 9. Performance trend linked to productivity database.
Table 1. System validation results.
Question Mean Average score
1. Compared to the current system, how eective is OPMS in measuring on-site productivity? 5.31 5.13
2. Compared to the current system, how much does OPMS improve the capture of on-site information? 5.55
3. Compared to the current system, how much more convenient is the OPMS for performance measurement? 5.07
4. Compared to the current system, how time-ecient is the proposed system in measuring project performance? 4.58
standardized. In this study, information was based on
material and labor used for steel construction work. In
the case of steel materials, shapes can be made dier-
ently for each company, and they may be customized
according to project conditions. In the future, more
standard models can be established with object libraries
that can be expanded. Third, the data-capturing process
of the proposed system remains manual-based. The
construction jobsite is volatile, and data measurement
is time- and eort-consuming. Thus, a new technology
for data capture and measurement will be very bene-
cial (Taneja et al. 2010, Lee, Son, and Lee 2014). When
considering laser scanning, drone technologies, and
image analyses, the proposed system would become
more powerful and ecient.
This research was supported by a grant (19RERP-B099826-05)
from Residential Environment Research Program (RERP)
funded by Ministry of Land, Infrastructure and Transport of
Korean government.
Disclosure statement
No potential conict of interest was reported by the authors.
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... Recently, there has been increasing attention for using BIM to stimulate multifaceted transitions in aspects of construction management (Borrmann et al., 2018). As researchers aim to accomplish a higher level in terms of automation in construction, the applications of BIM have increased, almost BIM has been integrated with all the abovementioned methods (computer vision-based, machine learning, photogrammetry and manual observations) (Arif and Khan, 2020;Cha and Kim, 2020;Heydarian and Golparvar-Fard, 2011;Lee et al., 2014Lee et al., , 2017Omar et al., 2018;Zhang et al., 2018) to monitor the productivity of different construction activities. ...
... Table 4 shows some of the tools and technologies that have been integrated together and the monitored activities. For instants, BIM is integrated with computer vision-based (Lee et al., 2014), photogrammetry (Omar et al., 2018), manual observations (Arif and Khan, 2020;Cha and Kim, 2020;Lee et al., 2017;Zhang et al., 2018) to monitor construction productivity for formwork, excavation, columns, slab pouring, steel fixing and design activities. ANN has been integrated with manual observations (Golnaraghi et al., 2019) to monitor the productivity for formwork, steel, concrete pouring activities and also integrated with low energy bluetooth to monitor the location of workers (Mohanty et al., 2020). ...
... Thus, a new technology for data capture and measurement will be very beneficial. for instance, considering drone technologies and image analyses, the systems would become more powerful and efficient (Cha and Kim, 2020). ...
Purpose - The unique nature of the construction sector makes it fall behind other sectors in terms of productivity. Monitoring construction productivity is crucial for the construction project's success. Current practices for construction productivity monitoring are time-consuming, manned and error prone. Although previous studies have been implemented toward reducing these limitations, a gap still exists in the automated monitoring of construction productivity. Design/methodology/approach - This study aims to investigate and assess the different techniques used for monitoring productivity in building construction projects. Therefore, a mixed review methodology (bibliometric analysis and systematic review) was adopted. All the related publications were collected from different databases, which were further screened to get the most relevant based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria. Findings - A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. Also, it was observed that current studies did not cover all the complex construction job sites and they were applied based on a small sample of construction workers and machines separately. Originality/value - This review paper contributes to the literature on construction management by providing insight into different productivity monitoring techniques.
... For a certain something, the high gear reliance prompts high administration costs, with Li DAR hardware costing a huge number of dollars, as well as the significant expense of the UAV gear expected for slant photography, as well as the high upkeep costs while being used, making it challenging to apply in the real administration process [2]. ...
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The developing intricacy and extent of building projects, as well as the way that construction schedule management is still mostly done by hand, has resulted in low efficiency in construction schedule management, resulting in cost overruns and legal disputes as a result of schedule delays in many projects. Existing 3D reconstruction algorithms frequently result in large there may be holes, distortions, or hazy regions in remade 3D models, but AI-based 3D remaking strategies regularly reestablish straightforward isolated parts and portray them as 3D boxes. Accordingly, these algorithmic systems are generally not sufficient for certifiable use. The primary objective of this paper is to apply the creation ill-disposed network strategy to 3D recreation works on the nature of the underlying 3D recreation model via preparing a generative ill-disposed network model to a joined state. As solo examples, just recently noticed 2D pictures are required, with no dependence on earlier information on the 3D hidden shape or reference insights. Test results show that this algorithmic structure outflanks present status of-the-workmanship 3D reproduction approaches on an average 3D recreation test set. On the common 3D remaking test set, exploratory outcomes uncover that this algorithmic structure beats the current cutting edge 3D reproduction techniques.
... For example, some scene space is relatively open, and it needs to be realized by combining corresponding construction technology and building materials in construction. For example, the content of the scene expression is comprehensive, and the building construction technology needs to be used to integrate the architectural space and the external environment of the building as a whole to form a unified scene space [20]. Deep learning is to simulate the visual mechanism of the human brain by combining low-level features to form more abstract high-level features or attribute categories to achieve complex function approximation and distributed representation of input data. ...
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The architectural drawings of traditional building constructions generally require some design knowledge of the architectural plan to be understood. With the continuous development of the construction industry, the use of three-dimensional (3D) virtual models of buildings is quickly increased. Using three-dimensional models can give people a more convenient and intuitive understanding of the model of the building, and it is necessary for the painter to manually draw the 3D model. By analyzing the common design rules of architectural drawing, this project designed and realized a building three-dimensional reconstruction system that can automatically generate a stereogram (3 ds format) from a building plan (dxf format). The system extracts the building information in the dxf plan and generates a three-dimensional model (3 ds format) after identification and analysis. Three-dimensional reconstruction of architectural drawings is an important application of computer graphics in the field of architecture. The technology is based on computer vision and pattern recognition, supported by artificial intelligence, three-dimensional reconstruction, and other aspects of computer technology and engineering domain knowledge. It specializes in processing architectural engineering drawings with rich semantic information and various description forms to automatically carry out architectural drawing layouts. The high-level information with domain meanings such as the geometry and semantics/functions of graphics of the buildings can be analyzed for forming a complete and independent research system. As a new field of computer technology, the three-dimensional reconstruction drawings are appropriate for demonstrating the characteristics of architectural constructions.
... A lot of research has been conducted on the topic of automated building construction schedule management with various technologies, but the existing research is hardly applicable to the complex building construction management practices [1][2][3]. ese existing researches mainly focus on three aspects: management based on BIM (Building Information Modeling) technology [4][5][6], management based on RFID technology combined with BIM [7][8][9], and management based on Scan to BIM technology combined with 3D reconstruction technology [10][11][12]. For example, in schedule management, [13] conducted a study on building construction progress based on UAVs carrying Li DAR technology combined with BIM technology to achieve automatic monitoring of outdoor progress at building construction sites [14]. ...
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The increasing complexity and enormity of construction projects, as well as the fact that the actual operation of construction schedule management still mainly relies on traditional manual management methods, have led to low efficiency of construction schedule management and caused many construction projects to have cost overruns and legal disputes due to schedule delays. Existing 3D reconstruction algorithms often lead to significant voids, distortions, or blurred parts in the reconstructed 3D models, while the machine learning-based 3D reconstruction algorithms are often only to reconstruct simple separated objects and represent them as 3D boxes. A novel architecture of semisupervised 3D reconstruction algorithm is proposed. The algorithm iteratively improves the quality of the original 3D reconstruction model by training a generative adversarial network model to a converged state. Only the prior observed 2D images are required as weakly supervised samples, without any dependence on prior knowledge of the 3D structure shape or reference observations. Experimental results show that this algorithmic framework has significant advantages over the current state-of-the-art 3D reconstruction methods on the standard 3D reconstruction test set.
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Construction sector productivity is of great importance for the government and policymakers because it determines a nation's future living standards and creates a competitive business environment. Addressing the factors influencing labor productivity is crucial for improving the productivity performance of the sector. A theoretical structural model is developed to understand the influence of six key factors on construction labor productivity and their links to labor productivity performance on a project. A structural equation modeling technique is used to analyze data collected via a questionnaire survey of 185 respondents consisting of government authorities and construction actors. The final model adapts 29 attributes across six labor productivity factors, namely, management and control, workforce, financial, external, project, and material and equipment. The outcomes of the final structural equation model confirm the significance of management team competency level and workforce quality in enhancing labor productivity. Moreover, client support and efforts by government authorities are found to be significant for labor productivity improvements. Despite the limitations of the study, it is hoped that the research outcomes will significantly contribute to improve the labor productivity performance in the Malaysian construction industry for faster delivery of construction projects with lower cost and higher quality.
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Steel construction activities are often undertaken in an environment with limited climate control. Both hot and cold temperatures can physically and psychologically affect construction workers, thus decreasing their productivity. Temperature and humidity are two factors that constantly exert forces on workers and influence their performance and efficiency. Previous studies have established a relationship between labor productivity and temperature and humidity. This research is built on the existing body of knowledge and develops a framework of integrating building information modeling (BIM) with a lower level critical path method (CPM) schedule to simulate the overall impact of temperature and humidity on a healthcare facility’s structural steel installation project in terms of total man hours required to build the project. This research effort utilized historical weather data of four cities across the U.S., with each city having workable seasons year-round and conducted a baseline assessment to test if various project starting dates and locations could significantly impact the project’s schedule performance. It was found that both varied project start dates and locations can significantly contribute to the difference in the man hours required to build the model project and that the project start date and location can have an interaction effect. This study contributes to the overall body of knowledge by providing a framework that can help practitioners better understand the overall impact of a productivity influencing factor at a project level, in order to facilitate better decision making.
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Many studies of construction labor productivity are at a macro level. These macrolevel studies attempt to quantify the productivity or changes in productivity over the entire industry or a segment of the industry. This study is aimed at a micro or project level examination of productivity. The tool developed allows a company to predict, manage, and even optimize the productivity associated with a project or levels of projects. Automated productivity-based schedule animation (APBSA) is a dynamic scheduling methodology that stochastically utilizes weekly trended construction labor productivity data in order to automatically update the baseline schedule that was created at the beginning of the project by the construction manager. APBSA reports the construction activity duration variances and animates the progress of the job with most current data available in a four-dimensional (4D) environment. Contractors can improve their planning by utilizing APBSA with building information modeling (BIM) which will help them to improve their craft productivity. As a case study, a three-story Systems Engineering Facility III of Hanscom Air Force Base (AFB) was modeled. The model was developed in order to demonstrate the impacts of time and cost-based stochastic productivity indices on the baseline schedule. A time-cost trade-off analysis was also performed in order to show effects of duration variations on the cost of the project.
Most construction project information is stored in unstructured documents, despite its increasing scale and complexity. Owing to ineffective management of construction information, the construction industry is significantly less productive than other industries. Although building information modelling (BIM) is widely employed in the industry, it is difficult to model the project execution phase data owing to its high level of detail. Therefore, this study proposes a framework based on BIM that enables the storing and searching of construction project data through an effective linkage of documents with 3D design objects. The information breakdown structure and spatial breakdown structure, which are the media for integrating construction data with information technology, have been established through the identification and classification of information from unstructured documents from construction sites. The efficiency of the proposed framework in linking documents with 3D objects was validated through a pilot test project and industry survey. The proposed methodology is expected to assist in resolving issues in information management, including enhanced communication, accumulation of know-how, and minimization of disputes, if the utility of the methodology is improved by continuously discovering the best practices at the industrial or organizational level through its extended application.
For owners of aged buildings, considerable attention needs to be devoted to efficient facility management (FM) to extend building life and achieve life-cycle cost savings. The fundamental step in this is to identify the level of FM performance. Few studies have focused on the quantitative investigation of FM performance, and there has been minimal research on the methodology required to arrive at the optimal strategy. The purpose of this study is to present a systematic algorithm that allows FM practitioners to evaluate their performance in an objective manner. In addition, the authors have formulated a FM performance evaluation index in conjunction with the fuzzy synthetic evaluation method. The framework for this evaluation includes 12 critical success factors and 18 key performance indicators according to 4 balanced scorecard perspectives. A case study on a commercial office building was conducted to demonstrate the effectiveness and validity of the proposed methodology. The results reveal an optimal FM strategy for the building recommended using the novel algorithm, and its effectiveness is validated by FM experts. The proposed method will allow industry practitioners to quantitatively evaluate FM performance and make better decisions in selecting the optimal management strategy. Furthermore, the findings can advance the body of knowledge on building performance evaluation in view of quantitative metrics development.
Construction labor productivity is a fundamental piece of information for estimating, budgeting, and scheduling a construction project. The current practice to estimate construction labor productivity relies primarily on the traditional method, which uses the published productivity data and/or the estimator's own experience, with an apparent lack of systematic approach to measuring, estimating, and predicting it. An investment for the sole purpose of productivity data collection and modeling is not likely to be successful for a construction company. To make the investment economically feasible, the productivity system should be integrated into the overall database and information system of the construction company. To achieve these advantages and overcome the current practice weaknesses, this paper introduces an engineering concept to document, control, predict, and improve the contractor's labor productivity. A wide range of influencing factors on the micro level (project management and administration) and the micro/micro level (activity level at construction site) has been considered. The proposed engineering approach was applied to model construction labor productivity of two construction crafts, carpentry and fixing reinforcing steel bars of different types of concrete foundations, using the artificial neural network (ANN) technique and utilizing the transfer function of the hyperbolic tan function (tanh). The results showed an adequate convergence with reasonable generalization capabilities, and more accurate and credible results compared with not only the traditional method, but also the existing approaches in the literature. This study contributes to the construction engineering and management body of knowledge by providing insight into using different ANN activation and transfer functions along with a wide range of influencing factors to benchmark the contractor's construction labor productivity. Moreover, the utilized engineering approach shows how a readily available practical database can help optimize several objectives. It supports two main pillars of sustainable construction: the economic dimension and the social dimension.
Recently, several studies pertaining to the measurement and analysis of construction productivity data have been attempted using image-processing technology. However, these studies have mainly focused on the recognition of individual labor and the materials at construction job sites. This research develops a new system model, which automatically analyzes and accumulates a construction work crew′s productivity data using image processing technologies. It includes the three modules as follows: 1) the acquisition of a construction job site′s video images and calibration for their recognition, 2) measurement and analysis of the construction work crew′s productivity data, and 3) creation and utilization of a productivity database. New algorithms are proposed for each module. YCbCr settings are used to develop a calibration algorithm for improving the rate of recognition. Work sampling and video editing are used to develop an algorithm for measuring and analyzing the crew′s productivity data. Then, an algorithm for productivity data accumulation and its utilization is proposed by utilizing the integration of the BIM Model. This developed system model is applied to a real construction site and validates its feasibility through two case studies.
Construction is an extremely information-dependent industry in which a project's success largely depends on good access to and management of data. Effective project management requires the characterization of its challenging issues and the use of appropriate tools for data handling. For this purpose, the construction industry is increasingly adopting the use of information and communication technologies (ICT) in recent years. Given the acknowledged potential of ICT to bring about improvements in other industries, many initiatives have been undertaken to develop appropriate tools to support various tasks during the construction project lifecycle. This paper focuses on the proposals that use ICT to provide access to the data and take advantage of this access to manage crucial issues within project management such as costs, planning, risks, safety, progress monitoring, and quality control. The authors will demonstrate that suitable data handling facilitates and improves the decision-making process and helps to carry out successful project management.
Variations in labor productivity are the result of multiple influential factors. This paper attempts to develop a labor productivity model based on multilayer feedforward neural networks trained with a backpropagation algorithm by which complex mapping of factors to labor productivity is performed. To prevent networks from overfitting and improve their generalization, early stopping and Bayesian regularization are implemented and compared. The results proved a better prediction performance for Bayesian regularization than early stopping. To demonstrate the prediction performance of the presented models, the developed models are implemented at two real power plant construction projects. Moreover, in order to extract the influence rate of each factor on the predictive behavior of the neural network models and to identify the most influential factors a sensitivity analysis is conducted. This paper focuses on the work involved in installing the concrete foundations of gas, steam, and combined cycle power plant construction projects in the developing country of Iran. This study contributes to the construction project management body of knowledge by investigating the influential factors on labor productivity and developing an artificial neural network to measure and predict labor productivity in developing countries using the Bayesian regularization and early stopping methods. This approach provides insight into better ways of modeling labor productivity.