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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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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: https://doi.org/10.1080/13467581.2020.1763364
© 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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CONSTRUCTION MANAGEMENT
A study on 3D/BIM-based on-site performance measurement system for
building construction
HeeSung Cha
a
and Jun Kim
a
a
Department of Architectural Engineering, Ajou University, Suwon, South Korea
ABSTRACT
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.
ARTICLE HISTORY
Received 1 November 2019
Accepted 8 April 2020
KEYWORDS
3D/BIM; information
technology; on-site
information; performance
measurement; productivity
factor
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 hscha@ajou.ac.kr Department of Architectural Engineering, Ajou University, Suwon, Korea
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING
https://doi.org/10.1080/13467581.2020.1763364
© 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 (http://creativecommons.org/licenses/by/4.0/), 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.
2H. CHA AND J. KIM
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
improvement.
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
conditions.
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
management
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.
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 3
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
orders,andlaborissues).Furthermore,therewere
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.
4H. CHA AND J. KIM
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
TM
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
section.
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
construction.
(1) 1
st
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
TM
). 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
nd
Step: Daily Work Progress Reporting
After 3D/BIM modeling, daily work progress was
reported using Excel
TM
, as shown in Figure 2. Using
the standard form, specic work progress information
was included with the aforementioned eight
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 5
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
rd
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.
6H. CHA AND J. KIM
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
becauseofthevariouslevelsofproductivity-factor
impacts.
(4) 4
th
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
th
(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
system.
Figure 4. Case study project overview.
(a). project exterior view, (b). construction work in progress c. project schedule)
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 7
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.
8H. CHA AND J. KIM
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.
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 9
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
10 H. CHA AND J. KIM
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.
Acknowledgments
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.
References
Boussabaine, A. H., and A. R. Du. 1996. An Expert-
simulation System for Construction Productivity
Forecasting: Conformation Method Used to Illustrate the
Uncertainty Associated with Experts Knowledge.Building
Research and Information 24 (5): 279286. doi:10.1080/
09613219608727543.
Cha, H., and C. Kim. 2011.Quantitative Approach for Project
Performance Measurement on Building Construction in
South Korea.KSCE Journal of Civil Engineering 15 (8):
13191328. doi:10.1007/s12205-011-1323-5.
Cha, H., and D. Lee. 2018.Framework Based on Building
Information Modeling for Information Management by
Linking Construction Document to Design Objects.
Journal of Asian Architecture and Building Engineering 17
(2): 329336. doi:10.3130/jaabe.17.329.
Dai, J., P. M. Goodrum, and W. F. Maloney. 2009.
Construction Craft WorkersPerceptions of the Factors
Aecting Their Productivity.Journal of Construction
Engineering and Management 135 (3): 217226.
doi:10.1061/(ASCE)0733-9364(2009)135:3(217).
Dozzy, S. P., and S. M. AbouRizk. 1993.Productivity in
Construction. Ottawa, Canada: Institute for Research in
Construction National Research Council.
Durdyev, S., S. Ismail, and N. Kandymov. 2018.Structural
Equation Model of the Factors Aecting Construction
Labor Productivity.Journal of Construction Engineering
and Management 144 (4): 04018007. doi:10.1061/(ASCE)
CO.1943-7862.0001452.
Eastman, C., P. Teicholz, R. Sacks, and K. Liston. 2008.BIM
Handbook: A Guide to Building Information Modeling for
Owners, Managers, Designers, Engineers and Contractors.
John Wiley & Sons .
El-Gohary, K. M., R. F. Aziz, and H. A. Abdel-Khalek. 2017.
Engineering Approach Using ANN to Improve and
Predict Construction Labor Productivity under Dierent
Inuences.Journal of Construction Engineering and
Management 143 (8): 04017045. doi:10.1061/(ASCE)
CO.1943-7862.0001340.
Ellk, L. D. S. 2018.Establishing an Expert System for the
I Dentifying Causing Problems to Reduce Productivity of
Human Resources in Construction Projects.Applied
Research Journal 4 (1): 111.
Feng, C. W., Y. J. Chen, and J. R. Huang. 2010.Using the MD
CAD Model to Develop the Timecost Integrated Schedule
for Construction Projects.Automation in Construction 19
(3): 347356. doi:10.1016/j.autcon.2009.12.009.
Gelisen, G., and F. H. Gris. 2014.Automated
Productivity-based Schedule Animation:
Simulation-based Approach to Time-cost Trade-o
Analysis.Journal of Construction Engineering and
Management 140 (4): B4013007.
Heravi, G., and E. Eslamdoost. 2015.Applying Articial
Neural Networks for Measuring and Predicting
Construction-labor Productivity.Journal of Construction
Engineering and Management 141 (10): 04015032.
doi:10.1061/(ASCE)CO.1943-7862.0001006.
Jarkas, A. M., and C. G. Bitar. 2012.Factors Aecting
Construction Labor Productivity in Kuwait.Journal of
Construction Engineering and Management 138 (7):
811820. doi:10.1061/(ASCE)CO.1943-7862.0000501.
Khanh, H. D., and S. Y. Kim. 2014. Determining Labor
Productivity Diagram in High-rise Building Using
Straight-line Model.KSCE Journal of Civil Engineering 18
(4): 898908. doi:10.1007/s12205-014-0521-3.
Lee, C., J. Son, and S. Lee. 2014.ASystem Model for
Analyzing and Accumulating Construction Work Crew
S Productivity Data Using Image Processing
Technologies.Journal of Asian Architecture and Building
Engineering 13 (3): 547554. doi:10.3130/jaabe.13.547.
Martínez-Rojas, M., N. Marín, and M. A. Vila. 2015.The Role of
Information Technologies to Address Data Handling in
Construction Project Management.Journal of Computing
in Civil Engineering 30 (4): 04015064. doi:10.1061/(ASCE)
CP.1943-5487.0000538.
McCullouch, B. G., and P. Gunn. 1993.Construction Field
Data Acquisition with Pen-based Computers.Journal of
Construction Engineering and Management 119 (2):
374384. doi:10.1061/(ASCE)0733-9364(1993)119:2(374).
Nasir, H., C. T. Haas, J. H. Rankin, A. R. Fayek, D. Forgues, and
J. Ruwanpura. 2012.Development and Implementation of
a Benchmarking and Metrics Program for Construction
Performance and Productivity Improvement.Canadian
Journal of Civil Engineering 39 (9): 957967. doi:10.1139/
l2012-030.
Nguyen, L. D., and H. T. Nguyen. 2012.Relationship between
Floor Number and Labor Productivity in Multistory
Structural Work: A Case Study.In Construction Research
Congress 2012: Construction Challenges in a Flat World,
15201529. West Lafayette, Indiana: ASCE. https://asceli
brary.org/doi/10.1061/9780784412329.153
Oh, S. W., S. G. Park, and Y. S. Kim. 2005.The Development of
a Schedule Management Support System Using
Productivity Information of Construction Activities.
Korean Journal of Construction Engineering and
Management 6 (6): 181192.
Oral, M., and E. Oral 2007.A Computer Based System for
Documentation and Monitoring of Construction Labour
Productivity.In Proceedings 24th CIB W78 Conference
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 11
Bringing ICT knowledge to work. June 2629 2007,
Maribor, Slovenia.
Russell, A. D. 1993.Computerized Daily Site Reporting.Journal
of Construction Engineering and Management 119 (2):
385402. doi:10.1061/(ASCE)0733-9364(1993)119:2(385).
Shan,Y.,andP.M.Goodrum.2014.Integration of Building
Information Modeling and Critical Path Method Schedules to
Simulate the Impact of Temperature and Humidity at the Project
Level.Buildings 4 (3): 295319. doi:10.3390/buildings4030295.
Sherekar, V., and M. Tatikonda. 2016.Impact of Factor
Aecting on Labour Productivity in Construction Projects
by AHP Method.International Journal of Engineering and
Computer Science 6: 6.
Song, L., and S. M. AbouRizk. 2008.Measuring and Modeling
Labor Productivity Using Historical Data.Journal of
Construction Engineering and Management 134 (10):
786794. doi:10.1061/(ASCE)0733-9364(2008)134:10(786).
Sonmez, R., and J. E. Rowings. 1998.Construction Labor
Productivity Modeling with Neural Networks.Journal of
Construction Engineering and Management 124 (6):
498504. doi:10.1061/(ASCE)0733-9364(1998)124:6(498).
Taneja, S., B. Akinci, J. H. Garrett, L. Soibelman, E. Ergen,
A. Pradhan, P. Tang, et al. 2010.Sensing and Field Data
Capture for Construction and Facility Operations.Journal
of Construction Engineering and Management 137 (10):
870881. doi:10.1061/(ASCE)CO.1943-7862.0000332.
Thomas, H. R., and I. Yiakoumis. 1987.Factor Model of
Construction Productivity.Journal of Construction
Engineering and Management 113 (4): 623639.
doi:10.1061/(ASCE)0733-9364(1987)113:4(623).
Tserng, H. P., S. P. Ho, and S. H. Jan. 2014. Developing
BIM-assisted As-built Schedule Management System for
General Contractors.Journal of Civil Engineering and
Management 20 (1): 4758. doi:10.3846/
13923730.2013.851112.
Tserng, H. P., and Y. C. Lin. 2004.Developing an
Activity-based Knowledge Management System for
Contractors.Automation in Construction 13 (6): 781802.
doi:10.1016/j.autcon.2004.05.003.
Xie, H., S. Fernando, and S. AbouRizk 2011.Integrating
Realtime Project Pro Gress Input into a Construction
Simulation Model.In Proceedings of the 2011 Winter
Simulation Conference (WSC), 34433454. Phoenix,
Arizona: IEEE.
Yoon, J., and H. Cha. 2018.Optimal FM Strategy for
Commercial Oce Buildings Using Fuzzy Synthetic
Evaluation.ASCE Journal of Performance of Constructed
Facilities 32: 3.
Zayed, T. M., and D. W. Halpin. 2004.Quantitative Assessment
for Piles Productivity Factors.Journal of Construction
Engineering and Management 130 (3): 405414.
doi:10.1061/(ASCE)0733-9364(2004)130:3(405).
12 H. CHA AND J. KIM
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