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ICCREM
2018
Innovative Technology and
Intelligent Construction
Edited by
Yaowu Wang; Yimin Zhu;
Geoffrey Q. P. Shen; and
Mohamed Al-Hussein
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ICCREM 2018
INNOVATIVE TECHNOLOGY AND INTELLIGENT
CONSTRUCTION
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON
CONSTRUCTION AND REAL ESTATE MANAGEMENT 2018
August 9–10, 2018
Charleston, South Carolina
SPONSORED BY
Modernization of Management Committee
of the China Construction Industry Association
The Construction Institute
of the American Society of Civil Engineers
EDITORS
Yaowu Wang
Yimin Zhu
Geoffrey Q. P. Shen
Mohamed Al-Hussein
Published by the American Society of Civil Engineers
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ICCREM 2018
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Preface
We would like to welcome you to the 2018 International Conference on Construction and Real
Estate Management (ICCREM 2018). Harbin Institute of Technology, Louisiana State University,
Hong Kong Polytechnic University, University of Alberta, Luleå University of Technology,
Heriot-Watt University, Marquette University, Karlsruhe Institute of Technology, Guangzhou
University. The Conference is a continuation of the ICCREM series which have been held annually
since 2003.
The theme for this conference is “Innovation Technology and Intelligent Construction”. It
especially highlights the importance of innovation technology for construction engineering and
management. The conference proceedings include 138 peer-review papers covered fourteen
important subjects. And all papers went through a two-step peer review process. The proceedings
of the congress are divided into four parts:
Innovative Technology and Intelligent Construction
Sustainable Construction and Prefabrication
Analysis of Real Estate and Construction Industry
Construction Enterprises and Project Management
On behalf of the Construction Institute, the American Society of Civil Engineers and the 2018
ICCREM Organizing Committee, we welcome you and wish you leave with a wonderful
experience and memory at ICCREM 2018.
Professor Yaowu Wang
Professor Yimin Zhu
Harbin Institute of Technology
Louisiana State University
P. R. of China
USA
Acknowledgments
Organized by
Harbin Institute of Technology, P.R. China
Louisiana State University, USA
Hong Kong Polytechnic University, P.R. China
University of Alberta, Canada
Luleå University of Technology, Sweden
© ASCE
ICCREM 2018
iv
Heriot-Watt University, UK
Marquette University, USA
Karlsruhe Institute of Technology, Germany
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Guangzhou University, P.R. China
Executive Editors
Yue Cao
Zhuyue Li
Xuewen Gong
Jia Ding
Xianwei Meng
Mengping Xie
Jiaqing Chen
Tianqi Zhang
Yushan Wang
Chong Feng
Xiangkun Qi
Jingjing Yang
Xiaoting Li
Yu Hua
Wenting Chen
Xiaowen Sun
Hang Shang
Shiwei Chen
Tongyao Feng
Conference website: http://www.iccrem.com/
Email:
[email protected]
Conference Committee
Committee Chairs
Prof. Yaowu Wang, Harbin Institute of Technology, P.R. China
Prof. Geoffrey Q.P. Shen, Hong Kong Polytechnic University, P.R. China
Conference Executive Chair
Prof. Yimin Zhu, Louisiana State University, USA
Conference Co-Chairs
Prof. Mohamed Al-Hussein, University of Alberta, Canada
Director Katerina Lachinova, Construction Institute of ASCE.(ASCE members), USA
Prof. Thomas Olofsson, Luleå University of Technology, Sweden
Prof. Ming Sun, Heriot Watt University, UK
Prof. Yong Bai, Marquette University, USA
Prof. Kunibert Lennerts, Karlsruhe Institute of Technology, German
Prof. Xiaolong Xue, Guangzhou University, P.R. China
© ASCE
ICCREM 2018
Organizing Committee and Secretariat
General Secretariat
Asso. Prof Qingpeng Man, Harbin Institute of Technology, P.R. China
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Deputy General Secretariat
Asso. Prof. Hongtao Yang, East China University of Science and Technology, P.R. China
Asso. Prof. Xiaodong Li, Tsinghua University, P.R. China
Asso. Prof. Chengshuang Sun, Beijing University of Civil Engineering and Architecture,
P.R. China
Committee Members
Dr. Yuna Wang, Harbin Institute of Technology, P.R. China
Dr. Tao Yu, Harbin Institute of Technology, P.R. China
Mr. Yongyue Liu, Harbin Institute of Technology, P.R. China
Mr. Zixin Han, Harbin Institute of Technology, P.R. China
Mr. Zhenzong Zhou, Harbin Institute of Technology, P.R. China
© ASCE
v
ICCREM 2018
vi
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Contents
On-Site Safety Uncertainties Assessment of Construction Based on Cloud
Model and Fuzzy Sets Theory................................................................................................... 1
Yongyue Liu, Yaowu Wang, Zhihe Yang, and Tao Yu
USA Professionals’ Perception of Key BIM Maturity Indicators ......................................... 13
Yunfeng Chen, Dylan John, and Robert F. Cox
Research on a Technical Framework in Smart Construction Based on
Big Data ................................................................................................................................... 26
Zixin Han and Yaowu Wang
Bibliometric Review of Artificial Intelligence (AI) in Construction
Engineering and Management ................................................................................................ 32
Chao Xiao, Yang Liu, and Amin Akhnoukh
BIM Implementation in Public Sector of Pakistan Construction Industry .......................... 42
Babar Ali, Hafiz Zahoor, Khwaja M. Mazher, and Ahsen Maqsoom
Construction of Information Management Model of Engineering Project
Based on BIM Technology ...................................................................................................... 52
Yu Hua and Yaowu Wang
Qualitatively Exploring the Impact of BIM on Construction Performance ......................... 60
Yunfeng Chen, Dylan John, and Robert F. Cox
A Novel IoT-Cloud-BIM Based Intelligent Information Management
System in Building Industrialization ...................................................................................... 72
Chao Han and Haowen Ye
Integration of Building Information Modeling (BIM) and Prefabrication
for Lean Construction ............................................................................................................. 78
Manisha Goyal and Zhili Gao
Additive Manufacturing: A Revolutionized Power for Construction
Industrialization ...................................................................................................................... 85
Qiancheng Wang, Shujia Zhang, Dongyang Wei, and Zijian Ding
Life Cycle Carbon Emissions of Industrialized Buildings Based on BIM ............................ 95
Zhiye Huang and Yanyan Fan
A Critical Review on BIM Research Process and Future Trends at Home
and Abroad ............................................................................................................................ 104
Jiaqing Chen and Qingpeng Man
© ASCE
ICCREM 2018
Effect of BIM Technology on Green Buildings .................................................................... 113
Wenbo Zheng and Jianguo Chen
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Study on the Post Evaluation of Construction Science and Technology
Research Project Performance Based on Fuzzy Synthetic Evaluation ............................... 118
Yunpin Hu, Tianyun Zhong, Zhongguo Tang, and Chongbo Gao
A Review of the Application of BIM in the Assembly Construction ................................... 125
Tianqi Zhang and Qingpeng Man
Research on Lean Construction System in the Background of Construction
Industrialization .................................................................................................................... 132
Lixia Chen, Jun Chen, and Jianxin Zhang
BIM Application Research of Assembly Building Design: Take ALLPLAN
as an Example........................................................................................................................ 138
Jun Xie, Difei Jiang, Zhentai Bao, and Pin Zhou
Application of BIM Technology in the Refined Management of
Construction Cost ................................................................................................................. 148
Qiangnian Li, Jingzhong Zhao, and Haohao Zhang
Building Industrialization Oriented Profit Allocation in Supply Chains
Using Weighted Shapley Value ............................................................................................. 156
Xiyue Jin and Xiaolin Yang
Research on the Development of 3D Printing Construction Industry Based
on Diamond Model ................................................................................................................ 164
Jingkuang Liu and Guokai Li
Policy Trends and Policy Analysis for the Industrialization of the
Construction Industry: A Review ......................................................................................... 177
Xiaoting Li, Yuna Wang, Yunpeng Geng, and Qian Wang
Evaluation of Construction Industrialization Policy Based on PMC Index
Model ..................................................................................................................................... 192
Yuanxin Zhang, Yali Jin, and Xiaolong Xue
Research on Sharing and Construction of Multi-Subject Smart Community
Based on Game Theory ......................................................................................................... 202
Lijun Wan and Qi Li
Research on the Integrity Control of Engineering Consulting Enterprises
with Interconnection BIM in the Context of New Era ......................................................... 209
Yan Song and Deyi Chen
Study on the Durability of Modified Recycled Concrete in Cold Region ........................... 215
Shuqing Lv and Lin Tian
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ICCREM 2018
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Study on Multi-Objective Project Planning and Scheduling Model with
Competitive Collaboration under Resource Constraints .................................................... 223
Chong Zhu, Yun Kan, Zhenqiang Bao, Weizhong Wang, and Zhaoyue Zhang
City Scale’s Effects on Income of Urban Residents in Different Industries:
Based on the Empirical Evidence of CHIP2009 ................................................................... 231
Yanju Liang and Zhiguo Gao
Effect of Aggregate Proportion on Properties of Modified Recycled Concrete
in Cold Region ....................................................................................................................... 238
Lin Tian
“Internet +” Speed Up for the Wisdom Sites ....................................................................... 243
Qiangnian Li and Yuan Zeng
Customer Requirement Analysis of Engineering Project Safety Management
Intelligent System .................................................................................................................. 252
Jun Hu, Jun Fang, Ruixian Qiu, and Yuexia Wu
Application of BIM Technology in Prefabricated Building ................................................. 263
Min Luo and Deyi Chen
Mapping Global Research on the Construction Industrialization ...................................... 271
Ting Luo, Xiaolong Xue, Yongtao Tan, Yuna Wang, and Zebin Zhao
Application of Building Information Modeling (BIM) Technology in
Information Management of Steel Structure Materials ...................................................... 278
Qiangnian Li, Hui Zhang, and Lei Zhang
© ASCE
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ICCREM 2018
1
On-Site Safety Uncertainties Assessment of Construction Based on Cloud Model and Fuzzy
Sets Theory
Yongyue Liu1 ; Yaowu Wang2 ; Zhihe Yang3; and Tao Yu, Ph.D.4
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1
Ph.D. Candidate, Dept. of Construction Management, School of Civil Engineering, Harbin
Institute of Technology, Harbin 150090, China (corresponding author). E-mail:
[email protected]
2
Professor, Dept. of Construction Management, Key Lab of Structures Dynamic Behavior and
Control of the Ministry of Education, Key Lab of Smart Prevention and Mitigation of Civil
Engineering, Disasters of the Ministry of Industry and Information Technology, Harbin Institute
of Technology, Harbin 150090, China. E-mail:
[email protected]
3
Ph.D. Candidate, Dept. of Construction Management, School of Management, Harbin Institute
of Technology, Harbin 150001, China. E-mail:
[email protected]
4
Dept. of Construction Management, School of Management, Harbin Institute of Technology,
Harbin 150001, China. E-mail:
[email protected]
ABSTRACT
Safety uncertainties widely exist in every phase of construction projects, especially during
the on-site building stage. To identify different kinds of on-site safety uncertainties, a four-level
analytic hierarchy process (AHP) model is proposed based on China’s current construction
industry standards. Then analyze the influence and frequency of level-4th (L4) factors,
synthesized cloud model (SCM) is applied to sampling calculation program, with which both
randomness and fuzziness can be jointly considered. Finally, a multi-factor assessment method
(MAM) under fuzzy sets theory is used to assess level-3rd (L3), level-2nd (L2), and level-1st
(L1) factors of on-site safety system. Such methodology not only avoids the limitation of filling
in huge numbers of written documents, but also can realize the dynamic adjustment of system
factors, and may significantly decrease the difficulty of on-site safety management and safety
inspection. In addition, practical advices are put forward to avoid some limitations of standard.
INTRODUCTION
More often than not, uncertainties are inherent in every condition of building engineering
projects (Tam et al. 2004). During different construction stages, these projects may consist of
various safety uncertainties, most of which will contribute to unacceptable hazards, such as
threaten to people health, failure of mechanical equipment and damage to environment. Also in
the same construction stage, safety uncertainties may exist in different dimensions, like on-site,
off-site and coordination (Arashpour et al. 2017). According to statistical analysis of safety
accidents in China (Acebes et al. 2014), including but not limited to falls, contact with
electricity, injured by falling/swinging objects, hit by rolling/sliding/flying object, contact with
machinery/equipment moving parts, fire or explosion (Pinto 2014), large scale of which occurred
in on-site dimension. In order to control the on-site built activities and reduce potential accidents,
both government and contractors have spared no efforts to inspect regularly or irregularly on
safety conditions, which may require more and more investment in human and material
resources. The classical method of identifying the ranks of on-site safety conditions of projects in
China is to fill-out a safety inspection score summary table that contains nearly 10 sub-tables,
from which more than 500 factors must be carefully taken into inspectors’ considerations and
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ICCREM 2018
given score to every one of them (MOHURD 2011).
However, such method has limitations to execution. (1) it is inspector’s subjective idea or
experience to decide how much score should be given to each factor. This may lead to
diametrically opposed conclusion of the sub-tables or even the summary table. (2) it is hard to
measure the influence of one factor variation to the whole system and the interaction and
integrity of uncertainties were emerged in cumbersome score accounting. (3) the heavily fill-out
assignments of classical table documents will hinder from factors’ dynamic adjustment for
different projects. (4) it is mainstream of safety uncertainty inspection to establish a set of
procedures in the era of widespread application of artificial intelligence, which can take
fuzziness of human cognition and randomness of events into consideration together, equivalent
or better than to the current standard and easy to adjust dynamically with computer.
To bridge this gap, this paper: (1) establishes a four-level model based on current national
standards and AHP. Comparing with current experts qualitative assessment model (Pinto 2014),
the four-level model contains a larger number of factors (>500), which will have a strong
practical significance. (2) analyzes the influence and frequency calculation of L4 factors. Taking
whether to minus of a L4 factor and how much score should be reduced of that factor as two
independent probability events. For those L4 factors whose minus score must be a numerical
value, one Bernoulli distribution and Normal distributions (B-N-SCM) will be enough; for those
L4 factors whose minus score belong to a consecutive numerical range, one Bernoulli
distribution and two Normal distributions (B-2N-SCM) will be adopted. With the help of
Forward Cloud Generator Algorithm (Zhang et al. 2015), random sampling programs are
developed, from which we can find out the influence degree to L2factors dynamically when L4
factors are changing. (3) MAM is used to clarify the ranks of on-site safety system. This method
takes L3 factors as the aspects of L2factor to judge the most possible scoring range, and takes
L2factors as the aspects of L1 to rank the whole safety uncertainty system.
METHODOLOGY
Review of standard for construction safety inspection: Standard for construction safety
inspection (JGJ59-2011) was published by MOHURD (2011) during the year of 2011 for measuring
and controlling the safety situation of any building engineering projects in China, as a second edition
to replace JGJ59-99, which was no longer suitable to the new era of continuous developing
technologies and changing situations. With accumulation of comprehensive practical engineering
experience under the standard of these years, there are empirical evidences to prove that likely
accident scenarios were significantly decreased. According to the standard, a typical building
engineering construction on-site safety inspection was divided into 10 parts in the safety inspection
score summary table (see MOHURD 2011 table A). Each part was described as a sub-table, such as
Safety Management score checking sub-table (see MOHURD 2011 table B.1). There are 19 subtables can be found in the standard. The whole inspection standard can be considered as the
clarification of safety uncertainties and be analogous to a four-level event tree suitable for AHP
method (see Figure 1).In the model, there are four levels can be divided column by column from the
left to right. More detailed parts can be obtained from the Standard if needed.
To fully understand the identify model assessment process and the relationships of different
levels or factors, main accounting rules are described as Table 1.
Synthesized cloud model (SCM): Defined as the integration of different distributions to the
digital characteristics of cloud model (CM) in this paper, SCM is used to safety uncertainties
analyze between L4 factors and L3 item.
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ICCREM 2018
3
Figure 1. Safety uncertainties identify model (partial).
CM was first invented by Li et al. (1995), and has been used to eliminate both randomness
and fuzziness in event probability and human cognition (Meng et al. 2008; Li et al. 2009; Hu et
al. 2008). Supposing there are some methods to make a reflection between a qualitative concept
C and some quantitative numbers in U . Let an arbitrary number x ⊆ U , then x will be
considered as a random realization of C , and a corresponding certainty degree function
y ⊆ [0,1] will be called a cloud drop. A typical CM can be characterized with three digital
indexes, namely Ex, En, He (Zhang et al. 2015). As Normal CM plays a prominent role in
application because of its universality and stability (Li et al. 2009; Zhang et al. 2015), then
Equation 1 can be regarded as a normal cloud and ( x, y ) in Equation 1 will be a random cloud
© ASCE
ICCREM 2018
4
drop. Also, x in Equation 1 may be described as x ~ CM ( Ex, En, He) for short.
x ~ N ( Ex, En ); En ~ ∋ En, He ( ; y < exp
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2
2
, ( x , Ex )2
2 En2
(1)
As to the safety uncertainties identify model, the L4 factors (as deduction rules in MOHURD
2011 table B.1)to a L3 item (as items in MOHURD 2011 table B.1) can be defined as a vector.
Let D as a deducted score vector described as Equation 2.
D < {a1 , a2 ,Κ , am | b1, b2 ,Κ , bn }
(2)
Where ai : i th minus score of m value-deducted factors; b j : j th minus score of n rangededucted factors.
Table 1. Main Accounting Rules of Safety Uncertainties Identify Model.
Total
Main accounting rules
quantities
L1
1 (on
(1) A typical building engineering project safety
behalf of
uncertainties system will be divided into three qualitative
summary
ranks: Excellent (≥80), Qualified (70~80), Unqualified
table)
(<70).
(2) The total score of perfect summary table or sub-tables
are all 100. Aggregation score of sub-tables will be
converted to 10, 15 or 5 (MOHURD 2011 table A). If a
sub-table get 0 score, rank of summary table will be
Unqualified.
L2
About 10
(1) L1 may not contain ten L2 factors all the same because
factors
of the uniqueness of a project. If there are missing sub(on behalf tables, the rest tables will be proportional expensed to 100.
of about
10 subtables)
L3
About 100 (1) All the L3 factors belong to two types, namely assuring
factors
items and general items. The former play a critical role to
(on behalf affect human health, mechanical equipment and
of about
environment, while the latter are not.
100 items (2) If in a sub-table, one assuring item get 0 score or total
in 10 sub- acquired score of assuring items less than 40 score,
tables)
aggregation score of this sub-table will be 0.
L4
About 500 (1) There are two types of deduction in L4 factors, one
factors
type is that deduction must be a numerical value, while the
(on behalf other is the deduction belong to a consecutive numerical
of about
range.
500
(2) Total deduction score in a set of L4 factors will be no
deduction more than the required score of a L3 item, or the acquired
rules in
score of that L3 item will be 0.
100 items)
Taking No.1 item in MOHURD 2011 table B.1 for instance: this L3 item named safety
responsibility system that contains 11 L4 factors, in which are 7 accurate value deducted factors
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ICCREM 2018
5
and 4 consecutive range deducted factors, such vector will be described as
D < {a1 , a2 ,Κ, a7 | b1 , b2 ,Κ, b4 } .
Aiming to analyze the fuzziness of safety inspectors and measure the influence of single
factor to the whole system, refer to the nature of CM, make the following hypotheses, namely:
H1: All the L4 factors are independent random variables.
H2: They are two independent probability events of whether to minus (random variable W )
of a L4 factor and how much score should be reduced (random variable H ) of that factor.
H3: To the value-deducted L4 factors, there is only one fuzziness dimension: W obey
Bernoulli distribution but not sure for p . Refer to Wang et al. (2017) and “3 En criterion”
(Zhang et al. 2015), let p obey the Normal distribution as Equation 4 and 5. The H is an
accurate number in MOHURD 2011 table B.1.
W 9 Bernoulli( p ), 0 ′ p ′ 1
(3)
2
p 9 N ( p, ρ )
(4)
(1 , p ) / 3,1 , p ; p
ρ<
(5)
p / 3,1 , p ″ p
H4: To the range-deducted L4 factors, there are two fuzziness dimensions: not sure for p in
W and fuzzy with H . Let the score which should be deducted obey the Normal CM as Equation
6. As the deducted range boundary is [Vmin ,Vmax ] in MOHURD 2011 table B.1, then Ex, En, He
will be calculated as Equation 7-9.
H 9 CM ( Ex, En, He)
(6)
V ∗V
Ex < min max
(7)
2
ζ
|
n (Vmax , Vmin ) 2
(8)
6
n2 ∗ 1
He < En / n, n = 3
(9)
Corresponding to the dimensions of D vector, p vector and ρ vector can be defined as
Equation 10 and Equation 11. Follow the steps of Table 2, ai can be calculated from H3, b j can
be calculated from H4.
p < ζ p1 , p2 ,Κ, pm p1 , p2 ,Κ, pn |
(10)
En <
ρ < ζρ 1, ρ 2 ,Κ,ρ m ρ1 ,ρ 2 ,Κ, ρ n |
(11)
MAM under fuzzy sets theory: MAM is used to assess safety uncertainties between L3 and
L2 (or L2 and L1). Assuming there are t L3 items ( Fi ) in a L2 table ( F set) as Equation 12.
Considering the eleven intervals of acquired score as E set in Equation 13, the possibilities of
every interval in E to F set represented by R matrix in Equation 14 .The weight of each Fi
can be denoted as W vector in Equation 15, as the normalization of the required score values in
MOHURD 2011 table B.1 for instance. According to fuzzy sets theory, the union and
Intersection calculate rules can be applied to get fuzzy sets Z in Equation 16 with max-min
composition of fuzzy relational equations (Zadeh 1965).
(12)
F < ζF1 , F2 ,Κ , Ft |
E < ζ( ,⁄ ,0],(0,1],Κ , (9,10]|
© ASCE
(13)
ICCREM 2018
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R1 r1,1
R r
2,1
R < 2 <
Λ Λ
r
Rt t ,1
r1,2 Κ r1,11
r2,2 Κ r2,11
Λ Ν Λ
rt ,2 Κ rt ,11
W < ΖW1 ,W2 ,Κ,Wt ∴
Z
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