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scelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only ICCREM 2018 Construction Enterprises and Project Management Edited by Yaowu Wang; Yimin Zhu; Geoffrey Q. P. Shen; and Mohamed Al-Hussein Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Published by American Society of Civil Engineers 1801 Alexander Bell Drive Reston, Virginia, 20191-4382 www.asce.org/publications | ascelibrary.org Any statements expressed in these materials are those of the individual authors and do not necessarily represent the views of ASCE, which takes no responsibility for any statement made herein. No reference made in this publication to any specific method, product, process, or service constitutes or implies an endorsement, recommendation, or warranty thereof by ASCE. The materials are for general information only and do not represent a standard of ASCE, nor are they intended as a reference in purchase specifications, contracts, regulations, statutes, or any other legal document. ASCE makes no representation or warranty of any kind, whether express or implied, concerning the accuracy, completeness, suitability, or utility of any information, apparatus, product, or process discussed in this publication, and assumes no liability therefor. The information contained in these materials should not be used without first securing competent advice with respect to its suitability for any general or specific application. Anyone utilizing such information assumes all liability arising from such use, including but not limited to infringement of any patent or patents. ASCE and American Society of Civil Engineers—Registered in U.S. Patent and Trademark Office. Photocopies and permissions. Permission to photocopy or reproduce material from ASCE publications can be requested by sending an e-mail to [email protected] or by locating a title in ASCE's Civil Engineering Database (http://cedb.asce.org) or ASCE Library (http://ascelibrary.org) and using the “Permissions” link. Errata: Errata, if any, can be found at https://doi.org/10.1061/9780784481752 Copyright © 2018 by the American Society of Civil Engineers. All Rights Reserved. ISBN 978-0-7844-8175-2 (PDF) Manufactured in the United States of America. ICCREM 2018 iii Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 © ASCE ICCREM 2018 Organizational Evolution of Megaprojects in China under Co-Effects of Politics and Markets ........................................................................................................... 89 Yun Le and Jiayi Liu Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 © ASCE vii ICCREM 2018 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 © ASCE viii ICCREM 2018 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Risk Analysis of Pension Real Estate Based on Gray Fuzzy Comprehensive Evaluation Model ....................................................................................... 299 Xiaozhuang Yang, Jun Wang, and Yongjun Chen © ASCE ix ICCREM 2018 1 Analyzing Risks in Public-Private-Partnership Projects: An Integrated Model of Sensitive Analysis and Monte Carlo Simulation Chong Feng1 and Yaowu Wang2 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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) © ASCE Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. ICCREM 2018 2 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 © ASCE Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 © ASCE 3 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. ICCREM 2018 4 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 © ASCE Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. ICCREM 2018 5 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 © ASCE ICCREM 2018 6 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 © ASCE ICCREM 2018 7 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 © ASCE 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. Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 Ebrahimnejad, S., Mousavi, S.M. and Seyrafian-pour, H. (2010). “Risk identification and 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 © ASCE 8 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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.” Project Management Technology, (8), 43–46. (in Chinese). Mo, L.Q. (2016). “Infrastructure PPP project financing risk analysis and case study.” Journal of Engineering Management, (5), 71–76. (in Chinese). Wang, L. (2016). “Research on risk factors and sharing policy of PPP project.” Times Finance, (36), 232–233. (in Chinese). © ASCE 9 ICCREM 2018 10 Stakeholder Value Systems on Disaster Resilience of Residential Buildings Mahdy Taeby1 and Lu Zhang2 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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 © ASCE
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