تلفیق مدل سازی معادله ساختاری و شبکه باور بیزین در تحلیل ابعاد ریسک بر اهداف پروژه‌های عمرانی شهرداری اصفهان
محورهای موضوعی : مدیریت پروژه
امیرحسین نادعلی جلوخانی
1
(دانشجوی دکتری گروه مدیریت دولتی، واحد دهاقان، دانشگاه آزاد اسلامی، دهاقان، ایران)
مهدی کرباسیان
2
(دانشیار گروه مهندسی صنایع، دانشگاه صنعتی مالک اشتر، اصفهان، ایران.)
سید رسول آقاداوود
3
(استادیار گروه مدیریت دولتی، واحد دهاقان، دانشگاه آزاد اسلامی، دهاقان، ایران)
عبدالمجید عبدالباقی
4
(استادیار گروه مدیریت مالی، دانشکده صنایع و مدیریت، دانشگاه صنعتی شاهرود، شاهرود، ایران)
کلید واژه: تحلیل ریسک, مدیریت ریسک پروژه, شبکه باور بیزین, پروژه های عمرانی شهرداری, ساختار شکست ریسک,
چکیده مقاله :
افزایش جمعیت و به‌تبع آن گسترش شهرنشینی موجب افزایش تعداد پروژه‌های عمرانی در کلان‌شهرها شده است. اجرا و مدیریت پروژه‌های مختلف ازجمله پروژه‌های عمرانی، دارای موارد مبهم و ناشناخته فراوانی است و با توجه به ویژگی‌های خاص هر پروژه و شرایطی که در شهرداری‌ها می‌باشد ریسک پروژه بر اهداف پروژه تأثیر می‌گذارد. هدف این پژوهش، بررسی تحلیلی تأثیر مؤلفه‌های ساختار شکست ریسک بر اهداف پروژه‌های عمرانی شهرداری شامل رضایت شهروندان، هزینه، زمان، کیفیت، محدوده و ایمنی با استفاده از شبکه باور بیزین است. در این پژوهش کاربردی، شیوه گردآوری داده‌ها-توصیفی پیمایشی از نوع همبستگی و جامعه آماری بر اساس نمونه‌گیری هدفمند مطابق با جامعهٔ متخصص در شهرداری اصفهان مرتبط با موضوع 45 مدیر، معاون، مسؤول مرتبط و متخصص انتخاب شدند. زمان پژوهش برای شناسایی ریسک پروژه‌ها سال 1395 تا پایان 1397 را در بر می گیرد، ابزار مورداستفاده در پژوهش پرسشنامه است که اطلاعات جمع‌آوری‌شده با استفاده از تدوین ساختار شکست ریسک پروژه‌ها جهت دسته‌بندی و شناسایی ریسک‌ها و ماتریس پذیرش ریسک مورد استفاده قرار گرفت. جهت اعتبار سنجی از مدل‌سازی معادله ساختاری به روش حداقل مربعات جزئی و در خصوص ارزیابی تأثیر هم‌زمان ابعاد ریسک بر اهداف پروژه‌ها از مدل‌سازی احتمالی علت و اثر بر مبنای الگوی باور بیزین صورت پذیرفته است. تحلیل داده‌های این پژوهش نشان داد که مؤلفه‌های ریسک پروژه‌ها، تأثیر مثبتی روی اهداف پروژه‌ها دارد. نوآوری و ویژگی این پژوهش تلفیق مدل‌سازی معادلات ساختاری با شبکه باور بیزین و استفاده از تکنیک تجزیه‌وتحلیل حالات خطا و آثار ریسک در ساختار شکست ریسک می‌باشد که در فرآیند مدیریت ریسک منجر به رفع عدم اطمینان بین روابط ابعاد ریسک و دقیق سازی اولویت‌بندی و تحلیل ابعاد ریسک‌ها شده است.
Population growth and the subsequent surge in urbanization has drastically increased the number of construction projects in metropolitan cities implementation and management of which may involve numerous ambiguous and unknown issues or risks that can impact project objectives depending on the specific project characteristics and municipal conditions. Thus, the purpose of the current applied descriptive correlational survey was to examine the extent to which components of risk failure structure can impact the goals of municipal construction projects including citizen satisfaction, cost, time, quality, range and safety using Bayesian Belief Network (BBN). The purposeful research sample comprised 45 managers, deputies, relevant officials and experts in Isfahan municipality who were consulted to identify the project risk dimensions during 2016 and 2018. A questionnaire was employed to collect the research data which were further analyzed using the project risk structure breakdown to classify and identify risks and matrices. Partial Least Squares Method of Structural Equation Modelling (SEM) was employed to validate the questionnaire data. The simultaneous impact of risk dimensions on project goals was investigated using probable cause and effect modeling based on Bayesian Belief Model. The findings verified the significant positive impact of project risk components on the project objectives. The innovative characteristic of the current study was the integration of SEM and Bayesian BBN and application of error state analysis technique and risk effects in risk failure structure which can establish certainty concerning the analysis of risk dimensions and their relationship with precision of priorities in risk management process.
Adams, F. K. (2006). Expert elicitation and Bayesian analysis of construction contract risks: an investigation. Construction Management and Economics, 24(1), 81-96.
Analytics, C. R. (2004). Inc. About Bayesian belief networks. Cam bridge: Charles River Analytics: Inc, 1-14.
Arunraj, N., Mandal, S., & Maiti, J. (2013). Modeling uncertainty in risk assessment: an integrated approach with fuzzy set theory and Monte Carlo simulation. Accident Analysis & Prevention, 55(3), 242-255.
Assaf, S., Hassanain, M. A., & Al-Zahrani, S. (2015). Causes of Contractors. Research Journal of Applied Sciences, Engineering and Technology, 9(3), 158-164.
Baccarini, D., & Archer, R. (2001). The risk ranking of projects: a methodology. International Journal of Project Management, 19(3), 139-145.
Bahrami, M., Bazzaz, D. H., & Sajjadi, S. M. (2012). Innovation and improvements in project implementation and management; using FMEA technique. Procedia-Social and Behavioral Sciences, 41, 418-425.
Carbone, T. A., & Tippett, D. D. (2004). Project risk management using the project risk FMEA. Engineering Management Journal, 16(4), 28-35.
Chapman, C., & Ward, S. (2002). Managing project risk and uncertainty: A constructively simple approach to decision making: John Wiley & Sons, 1-512.
Chapman, R. J. (2001). The controlling influences on effective risk identification and assessment for construction design management. International Journal of Project Management, 19(3), 147-160.
Chen, Z., Yuan, J., & Li, Q. (2017). Financing risk analysis and case study of Public-private partnerships infrastructure project. Paper presented at the Proceedings of the 20th International Symposium on Advancement of Construction Management and Real Estate, 9(35), 405-416.
Cheng, J., Greiner, R., Kelly, J., Bell, D., & Liu, W. (2002). Learning Bayesian networks from data: an information-theory based approach. Artificial intelligence, 137(1-2), 43-90.
Del Cano, A., & de la Cruz, M. P. (2002). Integrated methodology for project risk management. Journal of Construction Engineering and Management, 128(6), 473-485.
Dikmen, I., Birgonul, M. T., & Han, S. (2007). Using fuzzy risk assessment to rate cost overrun risk in international construction projects. International Journal of Project Management, 25(5), 494-505.
Edwards, P. J., & Bowen, P. A. (1998). Risk and risk management in construction: a review and future directions for research. Engineering, Construction and Architectural Management, 5(4), 339-349.
El-Sayegh, S. M. (2008). Risk assessment and allocation in the UAE construction industry. International Journal of Project Management, 26(4), 431-438.
Fan, C.-F., & Yu, Y.-C. (2004). BBN-based software project risk management. Journal of Systems and Software, 73(2), 193-203.
The fifth five-year plan of Isfahan Municipality with a Strategic Approach (2017). Assistance Planning Research and Information Technology, Revision 1, 2, 3 and 4 Isfahan 1400(In Persian).
Nadali Jelokhani, A. H., Agha Davood, S. R., Karbassian, M., & Abdul Baghi, A. M. (2018). A model for Measuring the Effect of Risk Breakdown Structure on the Purpose of the Construction Projects of Isfahan Municipality by Structural Equations Approach. Urban Economics, 3(1), 97-116. (In Persian).
Nadali Jelokhani A H, Agha Davood S R, Karbassian M, Abdul Baghi A M. Evaluating and Ranking Safety Risks of Isfahan Municipality Construction Projects Using Taxonomic Techniques and Risk Breakdown Structure Approach. ohhp. 2018; 2(2) :89-102(In Persian).
Han, S. H., & Diekmann, J. E. (2001). Approaches for making risk-based go/no-go decision for international projects. Journal of Construction Engineering and Management, 127(4), 300-308.
Heckerman, D. (1997). Bayesian networks for data mining. Data mining and knowledge discovery, 1(1), 79-119.
Joslin, R., & Müller, R. (2015). Relationships between a project management methodology and project success in different project governance contexts. International Journal of Project Management, 33(6), 1377-1392.
Junying Liu, Feng Jin, Qunxia Xie, Martin Skitmore .(2017). Improving risk assessment in financial feasibility of international engineering projects: A risk driver perspective. International Journal of Project Management 35(2), 204–211.
Kangari, R., & Riggs, L. S. (1989). Construction risk assessment by linguistics. IEEE transactions on engineering management, 36(2), 126-131.
Khaksar, M., shafei, r., & Visi, B. a. (2009). Recognition the risk roots in constructional projects and the methods of their management. (A case study).Scientific Journal Management System, 2(4(7)), 139-160. (In Persian).
Kendrick, T. (2009). Identifying and Managing project Risk: Essential Tools for Failure-Proofing Your Project. New York, 1-357.
Khodeir, L. M., & Mohamed, A. H. M. (2015). Identifying the latest risk probabilities affecting construction projects in Egypt according to political and economic variables. From January 2011 to January 2013. HBRC Journal, 11(1), 129-135.
Kim, S.-Y., Van Tuan, N., & Ogunlana, S. O. (2009). Quantifying schedule risk in construction projects using Bayesian belief networks. International Journal of Project Management, 27(1), 39- 50.
Kuo, Y.-C., & Lu, S.-T. (2013). Using fuzzy multiple criteria decision making approach to enhance risk assessment for metropolitan construction projects. International Journal of Project Management, 31(4), 602-614.
Lechler, T. G., & Dvir, D. (2010). An alternative taxonomy of project management structures: linking project management structures and project success. IEEE transactions on engineering management, 57(2), 198-210.
Lee, E., Park, Y., & Shin, J. G. (2009). Large engineering project risk management using a Bayesian belief network. Expert Systems with Applications, 36(3), 5880-5887.
Lyons, T., & Skitmore, M. (2004). Project risk management in the Queensland engineering construction industry: a survey. International Journal of Project Management, 22(1), 51-61.
McCabe, B., AbouRizk, S. M., & Goebel, R. (1998). Belief networks for construction performance diagnostics. Journal of Computing in Civil Engineering, 12(2), 93-100.
Mousavi, S. M., Tavakkoli-Moghaddam, R., Azaron, A., Mojtahedi, S., & Hashemi, H. (2011). Risk assessment for highway projects using jackknife technique. Expert Systems with Applications, 38(5), 5514-5524.
Mustafa, M. A., & Al-Bahar, J. F. (1991). Project risk assessment using the analytic hierarchy process. IEEE transactions on engineering management, 38(1), 46-52.
Nasir, D., McCabe, B., & Hartono, L. (2003). Evaluating risk in construction–schedule model (ERIC–S): construction schedule risk model. Journal of Construction Engineering and Management, 129(5), 518-527.
Nieto-Morote, A., & Ruz-Vila, F. (2011). A fuzzy approach to construction project risk assessment. International Journal of Project Management, 29(2), 220-231.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. morgan kauffman pub, 1-552.
Raz, T., & Michael, E. (2001). Use and benefits of tools for project risk management. International Journal of Project Management, 19(1), 9-17.
Rezaie, K., Amalnik, M. S., Gereie, A., Ostadi, B., & Shakhseniaee, M. (2007). Using extended Monte Carlo simulation method for the improvement of risk management: Consideration of relationships between uncertainties. Applied Mathematics and Computation, 190(2), 1492-1501.
Rose, K. H. (2013). A Guide to the Project Management Body of Knowledge (PMBOK® Guide)-Fifth Edition. Project management journal, 44(3), 1-616.
Romina,E.,Momizan,A.(2015).Risks in projects of modernization and reconstruction of old urban tissue Case Study: Mashhad Majd project. territory, 12(47), 81-92(In Persian).
Salah, A., & Moselhi, O.(2016). Risk identification and assessment for engineering procurement construction management projects using fuzzy set theory. Canadian Journal of Civil Engineering, 43(5), 429-442.
Shen, L., Wu, G. W., & Ng, C. S. (2001). Risk assessment for construction joint ventures in China. Journal of Construction Engineering and Management, 127(1), 76-81.
Shiyu Mu a, Hu Cheng, Mohamed Chohr, Wei Peng (2013).Assessing risk management capability of contractors in subway projects in mainland China .International Journal of Project Management, 32(3), 452-460.
Simister, S. J. (1994). Usage and benefits of project risk analysis and management. International Journal of Project Management, 12(1), 5-8.
Tchankova, L. (2002). Risk identification–basic stage in risk management. Environmental Management and Health, 13(3), 290-297.
Termini, M. J. (1999). Strategic project management: Tools and techniques for planning, decision making, and implementation: Society of Manufacturing Engineers.
Troldborg, M., Aalders, I., Towers, W., Hallett, P. D., McKenzie, B. M., Bengough, A. G., . . . Hough, R. L. (2013). Application of Bayesian Belief Networks to quantify and map areas at risk to soil threats: Using soil compaction as an example. Soil and Tillage Research, 132, 56-68.
Ugwoeri, J. C. (2012). A holistic survey of risk management in building construction project. Paper presented at the Proceedings of 4th West Africa Built Environment Research (WABER) Conference, 24À26 July, Abuja, Nigeria.
Ülengin, F., Önsel, Ş., Topçu, Y. I., Aktaş, E., & Kabak, Ö. (2007). An integrated transportation decision support system for transportation policy decisions: The case of Turkey. Transportation Research Part A: Policy and Practice, 41(1), 80-97.
Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological modelling, 203(3-4), 312-318.
Van Der Gaag, L. C. (1996). Bayesian belief networks: odds and ends. The Computer Journal, 39(2), 97-113.
Wang, F., Ding, L., Luo, H., & Love, P. E. (2014). Probabilistic risk assessment of tunneling-induced damage to existing properties. Expert Systems with Applications, 41(4), 951-961.
Williams, T. (1993). Risk-management infrastructures. International Journal of Project Management, 11(1), 5-10.
Zavadskas, E. K., Turskis, Z., & Tamošaitiene, J. (2010). Risk assessment of construction projects. Journal of civil engineering and management, 16(1), 33-46.
Zou, P. X., Zhang, G., & Wang, J. (2007). Understanding the key risks in construction projects in China. International Journal of Project Management, 25(6), 601-614.
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Adams, F. K. (2006). Expert elicitation and Bayesian analysis of construction contract risks: an investigation. Construction Management and Economics, 24(1), 81-96.
Analytics, C. R. (2004). Inc. About Bayesian belief networks. Cam bridge: Charles River Analytics: Inc, 1-14.
Arunraj, N., Mandal, S., & Maiti, J. (2013). Modeling uncertainty in risk assessment: an integrated approach with fuzzy set theory and Monte Carlo simulation. Accident Analysis & Prevention, 55(3), 242-255.
Assaf, S., Hassanain, M. A., & Al-Zahrani, S. (2015). Causes of Contractors. Research Journal of Applied Sciences, Engineering and Technology, 9(3), 158-164.
Baccarini, D., & Archer, R. (2001). The risk ranking of projects: a methodology. International Journal of Project Management, 19(3), 139-145.
Bahrami, M., Bazzaz, D. H., & Sajjadi, S. M. (2012). Innovation and improvements in project implementation and management; using FMEA technique. Procedia-Social and Behavioral Sciences, 41, 418-425.
Carbone, T. A., & Tippett, D. D. (2004). Project risk management using the project risk FMEA. Engineering Management Journal, 16(4), 28-35.
Chapman, C., & Ward, S. (2002). Managing project risk and uncertainty: A constructively simple approach to decision making: John Wiley & Sons, 1-512.
Chapman, R. J. (2001). The controlling influences on effective risk identification and assessment for construction design management. International Journal of Project Management, 19(3), 147-160.
Chen, Z., Yuan, J., & Li, Q. (2017). Financing risk analysis and case study of Public-private partnerships infrastructure project. Paper presented at the Proceedings of the 20th International Symposium on Advancement of Construction Management and Real Estate, 9(35), 405-416.
Cheng, J., Greiner, R., Kelly, J., Bell, D., & Liu, W. (2002). Learning Bayesian networks from data: an information-theory based approach. Artificial intelligence, 137(1-2), 43-90.
Del Cano, A., & de la Cruz, M. P. (2002). Integrated methodology for project risk management. Journal of Construction Engineering and Management, 128(6), 473-485.
Dikmen, I., Birgonul, M. T., & Han, S. (2007). Using fuzzy risk assessment to rate cost overrun risk in international construction projects. International Journal of Project Management, 25(5), 494-505.
Edwards, P. J., & Bowen, P. A. (1998). Risk and risk management in construction: a review and future directions for research. Engineering, Construction and Architectural Management, 5(4), 339-349.
El-Sayegh, S. M. (2008). Risk assessment and allocation in the UAE construction industry. International Journal of Project Management, 26(4), 431-438.
Fan, C.-F., & Yu, Y.-C. (2004). BBN-based software project risk management. Journal of Systems and Software, 73(2), 193-203.
The fifth five-year plan of Isfahan Municipality with a Strategic Approach (2017). Assistance Planning Research and Information Technology, Revision 1, 2, 3 and 4 Isfahan 1400(In Persian).
Nadali Jelokhani, A. H., Agha Davood, S. R., Karbassian, M., & Abdul Baghi, A. M. (2018). A model for Measuring the Effect of Risk Breakdown Structure on the Purpose of the Construction Projects of Isfahan Municipality by Structural Equations Approach. Urban Economics, 3(1), 97-116. (In Persian).
Nadali Jelokhani A H, Agha Davood S R, Karbassian M, Abdul Baghi A M. Evaluating and Ranking Safety Risks of Isfahan Municipality Construction Projects Using Taxonomic Techniques and Risk Breakdown Structure Approach. ohhp. 2018; 2(2) :89-102(In Persian).
Han, S. H., & Diekmann, J. E. (2001). Approaches for making risk-based go/no-go decision for international projects. Journal of Construction Engineering and Management, 127(4), 300-308.
Heckerman, D. (1997). Bayesian networks for data mining. Data mining and knowledge discovery, 1(1), 79-119.
Joslin, R., & Müller, R. (2015). Relationships between a project management methodology and project success in different project governance contexts. International Journal of Project Management, 33(6), 1377-1392.
Junying Liu, Feng Jin, Qunxia Xie, Martin Skitmore .(2017). Improving risk assessment in financial feasibility of international engineering projects: A risk driver perspective. International Journal of Project Management 35(2), 204–211.
Kangari, R., & Riggs, L. S. (1989). Construction risk assessment by linguistics. IEEE transactions on engineering management, 36(2), 126-131.
Khaksar, M., shafei, r., & Visi, B. a. (2009). Recognition the risk roots in constructional projects and the methods of their management. (A case study).Scientific Journal Management System, 2(4(7)), 139-160. (In Persian).
Kendrick, T. (2009). Identifying and Managing project Risk: Essential Tools for Failure-Proofing Your Project. New York, 1-357.
Khodeir, L. M., & Mohamed, A. H. M. (2015). Identifying the latest risk probabilities affecting construction projects in Egypt according to political and economic variables. From January 2011 to January 2013. HBRC Journal, 11(1), 129-135.
Kim, S.-Y., Van Tuan, N., & Ogunlana, S. O. (2009). Quantifying schedule risk in construction projects using Bayesian belief networks. International Journal of Project Management, 27(1), 39- 50.
Kuo, Y.-C., & Lu, S.-T. (2013). Using fuzzy multiple criteria decision making approach to enhance risk assessment for metropolitan construction projects. International Journal of Project Management, 31(4), 602-614.
Lechler, T. G., & Dvir, D. (2010). An alternative taxonomy of project management structures: linking project management structures and project success. IEEE transactions on engineering management, 57(2), 198-210.
Lee, E., Park, Y., & Shin, J. G. (2009). Large engineering project risk management using a Bayesian belief network. Expert Systems with Applications, 36(3), 5880-5887.
Lyons, T., & Skitmore, M. (2004). Project risk management in the Queensland engineering construction industry: a survey. International Journal of Project Management, 22(1), 51-61.
McCabe, B., AbouRizk, S. M., & Goebel, R. (1998). Belief networks for construction performance diagnostics. Journal of Computing in Civil Engineering, 12(2), 93-100.
Mousavi, S. M., Tavakkoli-Moghaddam, R., Azaron, A., Mojtahedi, S., & Hashemi, H. (2011). Risk assessment for highway projects using jackknife technique. Expert Systems with Applications, 38(5), 5514-5524.
Mustafa, M. A., & Al-Bahar, J. F. (1991). Project risk assessment using the analytic hierarchy process. IEEE transactions on engineering management, 38(1), 46-52.
Nasir, D., McCabe, B., & Hartono, L. (2003). Evaluating risk in construction–schedule model (ERIC–S): construction schedule risk model. Journal of Construction Engineering and Management, 129(5), 518-527.
Nieto-Morote, A., & Ruz-Vila, F. (2011). A fuzzy approach to construction project risk assessment. International Journal of Project Management, 29(2), 220-231.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. morgan kauffman pub, 1-552.
Raz, T., & Michael, E. (2001). Use and benefits of tools for project risk management. International Journal of Project Management, 19(1), 9-17.
Rezaie, K., Amalnik, M. S., Gereie, A., Ostadi, B., & Shakhseniaee, M. (2007). Using extended Monte Carlo simulation method for the improvement of risk management: Consideration of relationships between uncertainties. Applied Mathematics and Computation, 190(2), 1492-1501.
Rose, K. H. (2013). A Guide to the Project Management Body of Knowledge (PMBOK® Guide)-Fifth Edition. Project management journal, 44(3), 1-616.
Romina,E.,Momizan,A.(2015).Risks in projects of modernization and reconstruction of old urban tissue Case Study: Mashhad Majd project. territory, 12(47), 81-92(In Persian).
Salah, A., & Moselhi, O.(2016). Risk identification and assessment for engineering procurement construction management projects using fuzzy set theory. Canadian Journal of Civil Engineering, 43(5), 429-442.
Shen, L., Wu, G. W., & Ng, C. S. (2001). Risk assessment for construction joint ventures in China. Journal of Construction Engineering and Management, 127(1), 76-81.
Shiyu Mu a, Hu Cheng, Mohamed Chohr, Wei Peng (2013).Assessing risk management capability of contractors in subway projects in mainland China .International Journal of Project Management, 32(3), 452-460.
Simister, S. J. (1994). Usage and benefits of project risk analysis and management. International Journal of Project Management, 12(1), 5-8.
Tchankova, L. (2002). Risk identification–basic stage in risk management. Environmental Management and Health, 13(3), 290-297.
Termini, M. J. (1999). Strategic project management: Tools and techniques for planning, decision making, and implementation: Society of Manufacturing Engineers.
Troldborg, M., Aalders, I., Towers, W., Hallett, P. D., McKenzie, B. M., Bengough, A. G., . . . Hough, R. L. (2013). Application of Bayesian Belief Networks to quantify and map areas at risk to soil threats: Using soil compaction as an example. Soil and Tillage Research, 132, 56-68.
Ugwoeri, J. C. (2012). A holistic survey of risk management in building construction project. Paper presented at the Proceedings of 4th West Africa Built Environment Research (WABER) Conference, 24À26 July, Abuja, Nigeria.
Ülengin, F., Önsel, Ş., Topçu, Y. I., Aktaş, E., & Kabak, Ö. (2007). An integrated transportation decision support system for transportation policy decisions: The case of Turkey. Transportation Research Part A: Policy and Practice, 41(1), 80-97.
Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological modelling, 203(3-4), 312-318.
Van Der Gaag, L. C. (1996). Bayesian belief networks: odds and ends. The Computer Journal, 39(2), 97-113.
Wang, F., Ding, L., Luo, H., & Love, P. E. (2014). Probabilistic risk assessment of tunneling-induced damage to existing properties. Expert Systems with Applications, 41(4), 951-961.
Williams, T. (1993). Risk-management infrastructures. International Journal of Project Management, 11(1), 5-10.
Zavadskas, E. K., Turskis, Z., & Tamošaitiene, J. (2010). Risk assessment of construction projects. Journal of civil engineering and management, 16(1), 33-46.
Zou, P. X., Zhang, G., & Wang, J. (2007). Understanding the key risks in construction projects in China. International Journal of Project Management, 25(6), 601-614.