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ICCREM
2018
Construction Enterprises
and Project Management
Edited by
Yaowu Wang; Yimin Zhu;
Geoffrey Q. P. Shen; and
Mohamed Al-Hussein
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ICCREM 2018
CONSTRUCTION ENTERPRISES AND PROJECT
MANAGEMENT
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
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vi
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Contents
Analyzing Risks in Public-Private-Partnership Projects: An Integrated
Model of Sensitive Analysis and Monte Carlo Simulation ...................................................... 1
Chong Feng and Yaowu Wang
Stakeholder Value Systems on Disaster Resilience of Residential
Buildings .................................................................................................................................. 10
Mahdy Taeby and Lu Zhang
A Comparative Study on Evaluation Methods of Domestic and Foreign
Enterprises’ Brand Value ....................................................................................................... 18
Xuewen Gong and Yaowu Wang
Probabilistic Estimation for Microtunneling Projects’ Penetration Time ............................ 27
Emad Elwakil and Mohamed Hegab
Comparing Life Cycle Cost of Public and PPP Transportation Infrastructure
in Thailand: An Empirical Evidence ...................................................................................... 34
Nakhon Kokkaew and Veerasak Likhitruangsilp
Research on Life-Cycle Cost of Bridge Based on the Method of Monte Carlo
Simulation................................................................................................................................ 41
Hang Shang and Lixin Sun
Conceptual Proposal and Modeling of a Construction Surety Reinsurance
Company with a Quasi-Public Function: Empirical Evidence from China.......................... 46
Yanguang Xue, Xiaomei Deng, Kai Luo, Jiyi Wan, and Ke Feng
Systematic Model Study on the Investment Influencing Factors of Utility
Tunnel Based on PPP Mode.................................................................................................... 56
Xuan Zhang and Jun Fang
How Does Employee Competence Affect Job Performance in Indonesia:
The Mediating Role of Person-Job Fit ................................................................................... 64
Weiwei Wu, Zhou Liang, Yexin Liu, and Sanjaya Regina
Evaluation of Retrofit and Maintenance Schemes on Transport Infrastructure
Based on VE Theory: An Example of Urban Bridge ............................................................. 72
Lili Gao, Yulong Li, Guijun Li, and Zhiye Huang
Develop an Assessment Model for Healthcare Facilities: A Framework to
Prioritize the Asset Criticality for the Capital Renewals....................................................... 82
Dalia Salem and Emad Elwakil
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ICCREM 2018
Organizational Evolution of Megaprojects in China under Co-Effects
of Politics and Markets ........................................................................................................... 89
Yun Le and Jiayi Liu
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The Research on Construction Safety Evaluation Based on Rough Set ............................... 96
Lijuan Luo, Chengjie Xu, and Tingting Chen
Research on Factors Affecting Social Responsibility of Construction
Enterprises ............................................................................................................................ 102
Zeyu Wang, Jianfeng Lu, Xiaolong Xue, and Xuetong Wang
Dynamic Research on Stakeholders of the Utility Tunnel PPP Project .............................. 114
Hong Zhang and Bingjie Wang
An Empirical Study of the Influence of Authentic Leadership and the
Unethical Pro-Organizational Behavior Based on Organizational Identity ....................... 121
Guang Xu, Huimingmei Li, and Jiarui Wang
Research on Equilibrium of Revenue Sharing Contract in Existing
PPP Projects Based on the Theory of Share Tenancy ......................................................... 128
Yanhua Du, Jun Fang, and Jun Hu
High-Risk Nodes Determination for the Urban Rail Transit Station ................................. 139
Hui Xu, Yongtao Tan, Shulin Chen, Junwei Zheng, and Ningxin Shen
Research on Selection of Characteristic Towns Based on Fuzzy
Comprehensive Evaluation ................................................................................................... 147
Jingjing Yang, Zhiwei Liao, Han Bao, and Jiaqing Chen
Evaluation System Design for Application of Innovative Teaching
Methods in Major of Construction Management: Case Study in a
University of Finance and Economics .................................................................................. 157
Hao Wang, Changyun Cao, Nishang Guan, and Zhiye Huang
Research on the Transformation Path from Traditional Construction
Method to Off-Site Construction: Taking Chinese Enterprises as Example ...................... 167
Fangyun Xie, Chao Mao, and Guiwen Liu
Research on Risk Sharing of PPP Project Based on Shapley Value ................................... 176
Qing Wang
Research on Engineering Credit Consultation Enterprise Credit Evaluation
Based on System Dynamics ................................................................................................... 186
Yalan Xu and Deyi Chen
Application of Multilevel Extension Method in Synergy Evaluation of
Construction Program Management .................................................................................... 192
Changlin Niu, Lei Zhang, Huishan Li, and Ran Wang
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Discussion about the Analysis and Design of Over-Height High-Rise
Structure ................................................................................................................................ 200
Jun Xie, Difei Jiang, Zhengtai Bao, and Qiguo Li
Design of Performance Evaluation System of the Urban Utility Tunnel
Based on PPP Mode .............................................................................................................. 215
Jun Fang, Baifeng Wang, and Yu Zhang
Empirical Analysis on How Urban Infrastructure Influence Residents’
Satisfaction ............................................................................................................................ 223
Pengyu Wang and Shiying Shi
The Feasibility Research of Houses-for-Pension: Based on Analysis of
the Houses-for-Pension Situation in Tianjin ........................................................................ 231
Zhenxiang Shi and Hui Wang
Modeling the Effect of Group Norms on Construction Workers’ Safety
Behavior................................................................................................................................. 238
Qingting Xiang, Xiaoli Gong, Gui Ye, Qinjun Liu, and Yuhe Wang
Efficiency Analysis of the Listed Construction Enterprises in USA Based
on DEA Model from 2007 to 2016 ........................................................................................ 245
Bingzhen He, Yulong Li, Yanqiu Song, and Jie Lin
Hierarchical Structure Analysis of Influencing Factors of Tracking Audit
Risk in the Whole Process of PPP Project ............................................................................ 254
Suping Ren and Jun Fang
Exploration on the Methods of Forming an IPD Project Team and the
Responsibility of Team Members ......................................................................................... 263
Junfeng Guan
A Theory Calculation Model of Safety Detection Cycle for Existing
Reinforced Concrete Structures ........................................................................................... 268
Ying Wang, Yang Chen, and Legang Cai
Research on Risk Allocation Model of PPP Projects Based on Fuzzy
TOPSIS .................................................................................................................................. 277
Guiying Zhang and Jun Fang
A Study of Undergraduate Engineering Management Education Reform
Using CDIO Engineering Education Model ......................................................................... 284
Jiehui Zhang and Renhua Wu
Study on the Performance Evaluation of Construction Project Based
on Matter: Element Analysis Method .................................................................................. 291
Jingtao Feng and Junwu Wang
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Risk Analysis of Pension Real Estate Based on Gray Fuzzy
Comprehensive Evaluation Model ....................................................................................... 299
Xiaozhuang Yang, Jun Wang, and Yongjun Chen
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1
Analyzing Risks in Public-Private-Partnership Projects: An Integrated Model of Sensitive
Analysis and Monte Carlo Simulation
Chong Feng1 and Yaowu Wang2
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1
Postgraduate, Dept. of Construction Management, Harbin Institute of Technology, Harbin,
China 150001 (corresponding author). E-mail:
[email protected]
2
Professor, 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, Dept. of Construction Management, Harbin Institute of
Technology, Harbin, China 150001. E-mail:
[email protected]
ABSTRACT
Compared with traditional financing mode of construction, public-private-partnership (PPP)
mode has the great opportunity that private enterprises develop rapidly and solved the
shortcomings that the amount of infrastructure investment is large and governments lack funds.
Thus PPP mode is being adapted extensively. The keys to successfully implement PPP mode are
effectively identifying and analyzing risks in PPP projects, in order to achieve the risk
management of PPP projects. The research is aimed to establish a risk analysis model of PPP
projects combining the sensitive analysis and Monte Carlo simulation. Then it uses a real case
“Shijiazhuang International Exhibition Center” to verify this model and proposes strategies to
deal with the main risks. The result of this case study proved effectiveness of the proposed
model, which can be used in further risk analysis of PPP projects.
INTRODUCTION
With the rapid development of the economy and the acceleration of urbanization in China,
the demand for infrastructure has been constantly increasing. Due to the large amount of
investment and lack of government funds in infrastructure construction under the traditional
mode, this has to a certain extent restricted the development of infrastructure construction.
Therefore, it is of crucial importance to seek a new operation mode of the project which adapts to
China’s national conditions for infrastructure construction.
The PPP mode refers to that government and private organizations cooperate to build urban
infrastructures or provide some public goods and services. PPP mode takes China’s national
conditions into account that the investment for infrastructure construction is huge, government
funds could not meet the construction demands and the development of private economy is fast,
so it is necessary and feasible for the development of infrastructure construction. And then PPP
mode gets vigorously promoted in China. However, there are also risks in the application of PPP
mode. As practitioners of the PPP mode, we need to study risk identification and analysis of PPP
projects (Li and Shi 2017), and propose countermeasures to achieve risk management, in order to
ensure the successful implementation of PPP projects.
PPP mode first appeared in Britain in 1982, now it has been extensively adapted abroad. For
the risk study of PPP projects, foreign experts have shifted from shallow and qualitative analysis
to deep-seated and quantitative analysis. Li and Zou (2015) analyzed risks of PPP highway
projects with AHP method, and identify the key risk factors of the project. Ebrahimnejad et al.
(2010) used Delphi method and fuzzy mathematics method to establish F-AHP risk assessment
model to identify and analyze risks during the project implementation. Ghorbani et al. (2014)
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ICCREM 2018
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established a risk evaluation model of PPP project with survey of inventory and FMEA method,
and concluded the conclusion that the construction phase is the most likely stage of the risk
appearance.
Although it is not a long time since China applied PPP mode, Chinese scholars have made
some achievements for the risk study of PPP projects. Mo (2016) selected 17 important risk
factors of PPP projects through literature reading and established a risk assessment model based
on hierarchical structure and expert scoring. Then he did case studies of Beijing Metro Line 4
and India power plant project. Liu et al. (2011) used an integrated model of AHP and gray
correlation degree analysis to evaluate comprehensively risks of four main participants in a PPP
rail transit project. From the predecessors’ studies on PPP project risks, we can see that they are
mainly based on the expert’s experience and thus are subjective. Therefore, the research methods
need to be improved to ensure the objectivity of the study of PPP project risks.
In this paper, the risk factors of PPP project are firstly determined through literature review.
Then the risk analysis model of PPP project is established by using an integrated method of
sensitivity analysis and Monte Carlo simulation. Finally, the case study of Shijiazhuang
International Exhibition Center is used to verify the model. In addition, this paper also proposes
corresponding strategies for main risks of the project, which provides reference for future study
and enrich practical researches of PPP projects, so as to promote development of PPP mode in
China.
Design
risk
Construction
risk
design
construction
improperly cost
overruns
design
unqualified
changes
construction
quality
duration
delays
insufficient
construction
investment
Table 1. Risk Factors of PPP Projects.
Government
Operation
Financial Income
behavior
risk
risk
risk
risk
operation approval
interest rate services
cost
delays
changes
price
overruns
changes
unqualified changes in high
market
operation policies and financing demand
and service industry
risks
changes
quality
standards
operator
government debt risk
financial
default
changes
and
subsidy
liquidity
changes
risk
government Bankruptcy inflation
default
of project
company
Law risk
Other
risks
the third
force
party
majeure
default
risks
contract
documents
conflict
RISK IDENTIFICATION OF PPP PROJECTS
In the life cycle management of PPP projects, risk identification is of utmost importance and
throughout the entire project implementation process. It is the basis and primary task of the risk
management of PPP projects. In simple terms, risk identification is to identify what kinds of
events will affect the successful proceedings of projects during the implementation of projects,
and to classify the risks and their characteristics so as to analyze and study.
As PPP projects have the special structure of cooperation between government and social
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ICCREM 2018
capital, and have the characteristics of diversity, risk factors of PPP projects are more
complicated. In order to ensure the integrity, systematicity, high efficiency, normativity and
meticulousness of risk identification of PPP projects, we should use the combination of empirical
judgment method, brainstorming method, Delphi method, checklist method, flow chart, scenario
analysis method and pre-analytic methods to identify the risks existing in PPP projects in a
targeted manner, so as to deepen the understanding of the PPP project risks.
At present, in the research area of risk identification, the research results have been
sufficient. Guo (2016) classifies the risks of PPP projects into political risks, economic risks,
natural risks, social risks and man-made risks from the perspective of risk categories. Wang
(2016) divides the risks of PPP projects into law changes, project approval delays, force majeure
risks, project financing risks and market risks from the perspective of risk contents.
Based on the study of previous research results and actual cases of various PPP projects, this
paper summarizes the existing risks of PPP projects as design risk, construction risk, operation
risk, government behavior risk, financial risk, income risk, law risk and other risks. The risk
factors of each type are shown in Table 1 above.
RISK ANALYSIS MODEL OF PPP PROJECTS
The establishment of risk analysis model of PPP projects is based on an integrated method of
sensitivity analysis and Monte Carlo simulation. First, the variables and evaluation indexes of the
model are determined. And then the sensitivity variables of the model are analyzed by sensitivity
analysis. Finally, the risk status of PPP projects is simulated and analyzed by Monte Carlo
method. Thereby we could achieve the risk evaluation of PPP projects.
Evaluation index and analysis variables: PPP projects have the characteristics of large
investment and long duration. During the implementation of PPP projects, there are many factors
that are randomly changing, which will affect the decision-making of the project. When making
decisions for the project, most of project managers mainly consider economic impact. Therefore,
the net present value (NPV) that reflects the economic effect is selected as the risk evaluation
index of the project. In addition, risk analysis variables of the project are determined according
to the identified risks of PPP projects above, considering the following economic risk factors as
risk analysis variables.
Operating income (OI): the service income during the project operation stage, which
mainly reflects the income risk.
Government subsidies (GS): the government payments for the project during its operation
stage, which mainly reflect the risk of government actions.
Construction investment (CI): capital investment and costs during the construction stage,
which mainly reflect the construction risk.
Operating costs (OC): costs during the operation stage, which mainly reflect operational
risks.
Expenses of taxation (ET): taxes paid, which reflect income and financial risks.
Interest on loans (IL): changes in interest rates, which mainly reflect financial risks.
According to the risk evaluation index and analysis variables, the following economic
evaluation model (Formula 1) is established:
NPV (OI GS CI OC ET IL) (1 i )t
(1)
Sensitive risk factors are determined through sensitivity analysis: Sensitivity analysis
refers to establishing a function model, making each variable of the model fluctuate in a certain
range of changes and then studying the impacts of these independent variables on the dependent
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ICCREM 2018
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variable values. Sensitivity analysis is used to study the influence of deterministic risk variables
on projects, considering influencing degree of single variable or multiple variables on project
objectives. Sensitivity coefficient is commonly used to indicate sensitivity of variables, The
Formula 2 is as follows:
A / A
S AF
(2)
F / F
SAF: Sensitivity coefficients
A/A: change rate of evaluation index
△F/F: change rate of analysis variables
For the determination of sensitivity risk factors, we should mainly consider the values of the
sensitivity coefficients. In addition we also need to consider the importance degree of the
variables studied. Both variables with high sensitivity coefficient and key variables should be
considered as sensitivity variables for further research and analysis.
According to the variables and evaluation index selected by the risk analysis model of PPP
projects above, univariate sensitivity analysis is carried out. If the change in a risk variable has
little effect on the NPV of PPP projects, it is considered as a non-sensitivity factor. But if a risk
variable leads to a great impact on the NPV of PPP projects, it is considered as a sensitivity risk
factor of projects. The final determined sensitive risk factors are random variables of Monte
Carlo simulation in the next step.
Monte Carlo simulation analysis: Monte Carlo simulation refers to firstly constructing the
probability distribution of random variables, secondly extracting random numbers into sampling
values, to constitute basic data of project evaluation, thirdly determining values of evaluation
index through simulation calculation based on these basic data, finally organizing simulation
results including expected value, variance, standard deviation and its probability distribution of
evaluation index. So we could calculate risk status of PPP projects and do some evaluations.
Figure 1. Distribution gallery.
Commonly used software of Monte Carlo simulation is like Matlab, Oracle Crystal Ball and
so on. This paper uses the Oracle Crystal Ball which is an embedded software in Excel, so the
data of random variables and evaluation index need to be entered into Excel in a certain
relationship. The specific simulation analysis process is as follows:
Probability Distribution of Random Variables. The probability distribution of random
variables can be determined by data fitting, searching documents and asking experts.
Data fitting can be achieved with Oracle Crystal Ball, and the probability distribution of
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ICCREM 2018
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random variables should be selected by choosing the best fitting result.
Simulation Settings. Firstly, we need to define hypotheses for the random variables in the
hypothetical unit. There are 21 common distributions and one customized distribution in
Oracle Crystal Bal distribution gallery, as shown in Figure 1, which can basically meet
the definition requirements of random variables.
In addition, as the evaluation index of PPP project risk analysis model, NPV also needs to be
defined as forecast in Figure 2.
Figure 2. Define forecast.
Lastly, we need to set the number of trials to run, sampling methods, and confidence level .
In general, the number of simulations should be more than 300,000, the confidence level should
reach more than 95%, and Monte Carlo sampling method is used to extract random numbers.
Specific settings as shown in Figure 3 below. Then we could run simulations.
Figure 3. Simulation settings.
Simulation Results. Oracle Crystal Ball outputs the sensitivity data table and forecast
figures after running simulation. Through the sensitivity data table, we can further
compare the impacts of each random variable on the PPP project risk evaluation index:
the greater the variance contribution of random variables, and the stronger the rank
correlation, then the greater the sensitivity and stronger the impacts on NPV.
The forecast figures include the forecasting distribution frequency, the statistic values and the
percentage points related to the NPV. In the economic and risk evaluation, especially for PPP
projects, it is generally considered that the NPV is greater than zero as an important indicator of
the project investment decision, as shown in Table 2. By viewing the distribution frequency of
NPV in the forecast figures and calculating the probability that NPV is greater than zero, the risk
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status of the project can be obtained to determine whether the PPP project is feasible.
Table 2. Relationship between NPV and Level of Risk.
Level of Risk
Probability (NPV>0) (%)
Significant
<50
50%-75
Moderate
75%-99
Small
100
None
Model evaluation: Compare results of the sensitivity data table that Oracle Crystal Ball
outputs with results obtained from sensitivity analysis. If the results are consistent, it shows that
the risk analysis model of the entire PPP project is appropriate for describing the sensitive risk
factors.
In addition, compare simulated NPV, IRR and other economic indicators that Oracle Crystal
Ball outputs with actual cash flow values of PPP projects. If it is basically consistent and there
isn’t great difference, then we could say Monte-Carlo simulation for distribution fitting of each
random variables and forecasting of NPV is nice.
Through the case study below, the validity of analysis results of PPP project risk analysis
model will be verified, which lays the foundation for applying the risk analysis model to more
PPP projects.
CASE STUDY
Project background: “Shijiazhuang International Exhibition Center” is a PPP project. The
project investment amounted to 2.75 billion yuan, 20% of which is its own fund. The project
company is jointly established by the government sponsor representative and the social capital.
The franchise contract period is 30 years, including a two-year construction period and a 28-year
operation period.
Project risk analysis: According to the actual situation of the project, four risk factors,
including operating income, construction investment, operating cost and income tax rate, are
selected as study variables of risk analysis, and NPV is selected as evaluation index.
Table 3. Sensitivity Coefficients.
Variables
Sensitivity Coefficients
Operation income
0.84
Construction investment
2.05
Operation cost
0.61
Income tax rate
0.30
Through sensitivity analysis, construction investment, operating income and operating cost
are determined as sensitivity factors of the project, so as random variables for the following
Monte Carlo simulation. The sensitivity coefficient is shown in Table 3.
Then by data fitting, construction investment basically obeys the normal distribution of N
(137498, 275002). The first year’s operating income obeys the Beta distribution that the
minimum is 2515.40, the maximum is 5961.36, Alpha is 0.66 and Beta is 0.8. Operating income
grows at an average annual rate of 7.5%. Lastly operation cost obeys a negative binomial
distribution with a probability of 0.00359 and a shape of 27. After data fitting, this paper does
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some simulation settings and then run simulation. The results of simulation include sensitivity
data table (see Table 4), simulation values of NPV and IRR (see Table 5), and the predicted NPV
distribution frequency graph (see Figure 4).
Hypothetical unit
Construction investment
Operation income
Operation cost
NPV
IRR
Table 4. Sensitivity Data.
Variance contribution
62.7%
29.6%
7.7%
rank relation
-0.77
0.53
-0.27
Table 5. Simulation Values and Project Data.
Simulation values (Mean)
Project data
206563200
248219300
6.711%
6.743%
Figure 4. Predicted NPV distribution frequency.
According to Table 4, construction investment has the greatest impact on NPV of the project,
followed by operating income. Operating cost has the least impact on NPV of the project and is a
relatively minor risk. From Figure 5, we know that the probability that NPV is greater than zero
is 62.9640% within [50%, 75%]. So the risk status of this PPP project is moderate and the project
is feasible, but corresponding measures need to be taken to deal with risks of this project.
Project risk response: According to the outputs of the risk analysis, we proposed the
corresponding strategies for the main risks. In terms of construction investment risk, the
government needs to adopt a reasonable financing model, determine a reasonable investment and
financing structure, reduce the impact of financial risks such as financing failure, high financing
costs and excessive interest rate changes on the project, in order to ensure the stability of
construction investment.
Operating income and operating cost risks should be dealt with by the project company.
Operating income aspect mainly depends on the market demand and service price. The project
company needs to improve the operation management degree of the project, stimulate the
consumption of service, and ensure the market demand. At the same time, the market survey
should be conducted to determine a reasonable service price. And sometimes, to a certain extent,
operation risks can be transferred to the users of the project, to ensure stable growth of operating
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ICCREM 2018
revenue. As for operating cost aspect, the project manager should adopt reasonable measures to
control operation and maintenance costs, mitigate risks as much as possible, or reduce the impact
of operating cost risk on the project, in order to ensure the successful implementation of the
project.
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CONCLUSION
In the “Shijiazhuang International Exhibition Center” case study, by comparing Table 3 and
Table 4, we could know that the results of Monte Carlo simulation and sensitivity analysis are
consistent for the study of project risk factors’ sensitivity. It is Sensitivity (construction
investment) > Sensitivity (operation income) > Sensitivity (operation cost), which shows that
the risk analysis model of PPP projects appropriately describes the sensitive risk factors.
What’s more, this model basically matches the simulation values of economic evaluation
index, such as NPV, and actual cash flow data of this project, as shown in Table 5, which shows
that the fitting effect of the model is significant.
The contents above are good illustrations for the accuracy of risk analysis model in risk
simulation and description of the sensitivity risk factors, and verify the validity of the model.
Then this risk analysis model can be applied to more types of PPP projects, such as
transportation infrastructure projects and public-building projects, so as to promote the practical
research of PPP projects. By evaluating the project’s risk status and investment feasibility, it is
beneficial for project company to make reasonable decisions and take targeted risk response
strategies, so as to achieve risk management in the life cycle of PPP projects, ensure the
successful implementation of projects and then further promote the development of the PPP
mode in China.
However, there are also shortcomings in this study. The risk analysis model of PPP projects
established in this paper only considers variables that can be quantitatively researched in detail
and does not conduct specific analysis and research on qualitative variables, which can be used
as a direction for future research to further improve the risk analysis model of PPP projects.
ACKNOWLEDGEMENTS
This research is funded by the National Natural Science Foundation of China (No. 51378160)
and the National Key Research and Development Program of China (No. 2016YFC0701904).
REFERENCES
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assessment for build-operate-transfer projects: a fuzzy multi attribute decision making
model.” Expert Systems with Applications, 37(2010), 575–586.
Ghorbani, A., Ravanshadnia, M. and Nobakht, M.B. (2014). “A survey of risks in public-privatepartnership highway projects in Iran.” International Conference on Construction & Real
Estate Management, (11), 482–492.
Guo, J.Y. (2016) “Analysis and identification of influencing factors of social risks in traffic PPP
projects.” Journal of Civil Engineering and Management, (6), 88–93. (in Chinese).
Li, J. and Zou, P.X.W. (2015). “Fuzzy AHP-based risk assessment methodology for PPP
projects.” Journal of Construction Engineering & Management, 137(12), 1205–1209. (in
Chinese).
Li, W.G. and Shi, Y.R. (2017). “Risk Factors Analysis of PPP project of pension agency based
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8
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ICCREM 2018
on ISM.” 3rd International Conference on Information Management (ICIM), Chengdu,
China, 51–55.
Liu, X.N., Wang, J.B., Zhao, H. and Chen, X.S. (2011) “Comprehensive evaluation of financing
risk of PPP project in urban rail transit based on AHP and improved gray relational theory.”
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Mo, L.Q. (2016). “Infrastructure PPP project financing risk analysis and case study.” Journal of
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10
Stakeholder Value Systems on Disaster Resilience of Residential Buildings
Mahdy Taeby1 and Lu Zhang2
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1
Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Florida International Univ.,
10555 West Flagler St., EC 2900, Miami, FL 33174. E-mail:
[email protected]
2
Assistant Professor, Moss School of Construction, Infrastructure and Sustainability, Florida
International Univ., 10555 West Flagler St., EC 2935, Miami, FL 33174 (corresponding author).
E-mail:
[email protected]
ABSTRACT
There is sorely a need to engage multi-sector stakeholders (e.g., local community,
government, private sector) in collaboratively facilitating the resilience of our built environment.
However, different stakeholders could make different decisions on disaster resilience; such
differences are deeply rooted in the different value systems of the stakeholders. Stakeholder
value systems are defined as a ranked system of things that are of importance and utilities to the
stakeholders. There is a need to integrate the value systems of multi-sector stakeholders with
resilience decision making to support stakeholder collaboration. To address the need, this paper
focuses on understanding and analyzing the value systems of different stakeholders for disaster
resilience in residential buildings. The disaster resilience concepts were identified from domain
literatures and systematic interactions (i.e., interviews, survey) with stakeholders. Both
responsible stakeholders and impacted stakeholders were involved in the study. The results show
that there is a significant difference in the stakeholders’ perspectives on the priority of disaster
resilience. This research could improve stakeholder-centered decision-making to support more
resilient built environment.
INTRODUCTION
Disaster resilience in the built environment is a rapidly growing area of study with
contributions from researchers in different domains that often involve a diverse set of
stakeholders with different perspectives, interpretations, and priorities (Perera et al. 2016).
Disaster resilience is a shared responsibility among all the stakeholders, and “achieving this kind
of resilience encompasses actions and decisions at all levels of government, in the private sector,
and in communities” (NAS 2012). However, every stakeholder is different, and may make
different decisions regarding the priorities of implementing resilience practices (e.g., making
elevators disaster adaptive versus adding more emergency stairs). Such difference is deeply
rooted in the different value systems of different stakeholders (Schwartz 2012; Zhang and ElGohary 2017). A stakeholder value system is defined as a ranked system of things that are of
importance, merit, and utilities to the stakeholders. The differences in these value systems could
cause conflicts and disputes during decision-making process, resulting in longer decision-making
time and millions of dollar losses (Maiese 2003). “Conflicts arise over how to move toward
enhancing resilience, how to manage the costs of doing so, and how to assess its effectiveness
(NAS 2012).” Thus, without identifying and integrating the different value systems of multisector stakeholders, disaster resilience decisions could become ineffective, time-consuming,
costly, and conflict-prone.
To integrate stakeholder value systems on disaster resilience, firstly, there is a need to
understand and identify the different resilience concepts in the built environment. Various
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