An integrated fuzzy DEMATEL-fuzzy ANP model for evaluating construction projects by considering interrelationships among risk factors
Abstract
Construction projects are associated with a number of uncertainties due to their expanse, complex nature, uniqueness, and dynamic states. Risks in construction projects are, indeed, the events or uncertain situations that can have negative or positive consequences on the project objectives. Many of the risks inherent in construction projects affect each other. For example, the time risk in construction projects can affect the cost risk and vice versa. The intertwined relations between risk factors are ignored in the traditional construction risk assessment methods. To fulfill this gap, this paper proposes an integrated fuzzy DEMATEL-fuzzy ANP model to evaluate construction projects and their overall risks by considering intertwined relations among risk factors. Fuzzy DEMATEL is used to determine the interrelationships and interdependencies among risk factors. The network structure for implementing the fuzzy ANP method is extracted based on the results of fuzzy DEMATEL. The fuzzy ANP is applied to assess the relative importance of risk factors and alternatives and prioritize construction projects. The proposed integrated model is used to evaluate five construction projects based on risk factors in Isfahan, Iran. The results of applying the integrated model reveal that the time, cost and safety risks with the weight values of 0.056, 0.038, and 0.034 are the most important factors among construction risks, respectively. The results reveal that the proposed model can help managers to evaluate the overall risks of construction projects, and select the best project that has the lowest risk.
Keyword : construction projects, risk assessment, interrelations among risk factors, fuzzy DEMATEL, fuzzy analytic network process (ANP)
How to Cite
Hatefi, S. M., & Tamošaitienė, J. (2019). An integrated fuzzy DEMATEL-fuzzy ANP model for evaluating construction projects by considering interrelationships among risk factors. Journal of Civil Engineering and Management, 25(2), 114-131. https://doi.org/10.3846/jcem.2019.8280
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Geneva: Battelle Geneva Research Center.
Ghasemi, F., Sari, M. H. M., Yousefi, V., Falsafi, R., & Tamošaitienė, J. (2018). Project portfolio risk identification and analysis, considering project risk interactions and using Bayesian networks. Sustainability, 10(5), 1609. https://doi.org/10.3390/su10051609
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Opricovic, S., & Tzeng, G. (2003). Defuzzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11, 635-652. https://doi.org/10.1142/S0218488503002387
Özdemir, A., & Tüysüz, F. (2017). An integrated fuzzy DEMATEL and fuzzy ANP based balanced scorecard approach: application in Turkish higher education institutions. Journal of Multiple-Valued Logic & Soft Computing, 28(2/3), 251-287.
Ren, J., & Sovacool, B. K. (2014). Quantifying, measuring, and strategizing energy security: Determining the most meaningful dimensions and metrics. Energy, 76, 838-849. https://doi.org/10.1016/j.energy.2014.08.083
Saaty, T. L. (1996). The analytic network process. Pittsburgh: RWS Publications.
Saaty, T. L. (2005). Theory and applications of the analytic network process decision making with benefits, opportunities, costs and risks. Pittsburgh: RWS Publications.
Samantra, C., Datta, S., & Mahapatra, S. S. (2017). Fuzzy based risk assessment module for metropolitan construction project: An empirical study. Engineering Applications of Artificial Intelligence, 65, 449-464. https://doi.org/10.1016/j.engappai.2017.04.019
Seker, S., & Zavadskas, E. K. (2017). Application of fuzzy DEMATEL method for analyzing occupational risks on construction sites. Sustainability, 9, 2083. https://doi.org/10.3390/su9112083
Tah, J. H. M., & Carr, V. (2000). A proposal for construction project risk assessment using fuzzy logic. Construction Management and Economics, 18(4), 491-500. https://doi.org/10.1080/01446190050024905
Tamošaitienė, J., Turskis, Z., & Zavadskas, E. K. (2008). Modeling of contractor selection taking into account different risk level. In The 25th International Symposium on Automation and Robotics in Construction (ISARC 2008) (pp. 676-681). https://doi.org/10.22260/ISARC2008/0099
Tamošaitienė, J., Zavadskas, E. K., & Turskis, Z. (2013). Multi-criteria risk assessment of a construction project. Procedia Computer Science, 17, 129-133. https://doi.org/10.1016/j.procs.2013.05.018
Taylan, O., Bafail, A. O., Abdulaal, R. M. S., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116. https://doi.org/10.1016/j.asoc.2014.01.003
Tsai, S. B., Chien, M. F., Xue, Y., Li, L., Jiang, X., Chen, Q., & Wang, L. (2015). Using the fuzzy DEMATEL to determine environmental performance: A case of printed circuit board industry in Taiwan. PLOS ONE, 10(6), 0129153. https://doi.org/10.1371/journal.pone.0129153
Tseng, M. L. (2011). Using a hybrid MCDM model to evaluate firm environmental knowledge management in uncertainty. Applied Soft Computing, 11(1), 1340-1352. https://doi.org/10.1016/j.asoc.2010.04.006
Tzeng, G., Chiang, C., & Li, C. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028-1044. https://doi.org/10.1016/j.eswa.2006.02.004
Uygun, Ö., & Dede, A. (2016). Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Computers & Industrial Engineering, 102, 502-511. https://doi.org/10.1016/j.cie.2016.02.020
Valipour, A., Yahaya, N., Noor, N. Md., Antuchevičienė, J., & Tamošaitienė, J. (2017). Hybrid SWARA-COPRAS method for risk assessment in deep foundation excavation project: an Iranian case study. Journal of Civil Engineering and Management, 23(4), 524-532. https://doi.org/10.3846/13923730.2017.1281842
Wang, T., Wang, S., Zhang, L., Huang, Z., & Li, Y. (2016). A major infrastructure risk-assessment framework: Application to a cross-sea route project in China. International Journal of Project Management, 34(7), 1403-1415. https://doi.org/10.1016/j.ijproman.2015.12.006
Wei, W. W., & Yu, T. L. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499-507. https://doi.org/10.1016/j.eswa.2005.12.005
Winch, G. (2002). Managing construction projects: An information processing approach. Wiley.
Yazdani-Chamzini, A. (2014). Proposing a new methodology based on fuzzy logic for tunneling risk assessment. Journal of Civil Engineering and Management, 20(1), 82-94. https://doi.org/10.3846/13923730.2013.843583
Zavadskas, E. K., Turskis, Z., & Tamošaitienė, J. (2010). Risk assessment of construction projects. Journal of Civil Engineering and Management, 16(1), 33-46. https://doi.org/10.3846/jcem.2010.03
Zhou, J. L., Bai, Z. H., & Sun, Z. Y. (2014). A hybrid approach for safety assessment in high-risk hydropower-construction-project work systems. Safety Science, 64, 163-172. https://doi.org/10.1016/j.ssci.2013.12.008
Chatterjee, K., Zavadskas, E. K., Tamošaitienė, J., Adhikary, K., & Kar, S. (2018). A Hybrid MCDM technique for risk Management in construction projects. Symmetry, 10(2), 46. https://doi.org/10.3390/sym10020046
Chau Ngoc, D., Long, L. H., Soo-Yong, K., Chau Van, N., Young-Dai, L., & Sun-Ho, L. (2017). Identification of risk patterns in Vietnamese road and bridge construction: Contractor’s perspective. Built Environment Project and Asset Management, 7(1), 59-72. https://doi.org/10.1108/BEPAM-11-2015-0065
Chien, K. F., Wu, Z. H., & Huang, S. C. (2014). Identifying and assessing critical risk factors for BIM projects: Empirical study. Automation in Construction, 45, 1-15. https://doi.org/10.1016/j.autcon.2014.04.012
Ebrahimnejad, S., Mousavi, S. M., Tavakkoli-Moghaddam, R., Hashemi, H., & Vahdani, B. (2012). A novel two-phase group decision making approach for construction project selection in a fuzzy environment. Applied Mathematical Modelling, 36(9), 4197-4217. https://doi.org/10.1016/j.apm.2011.11.050
El-Sayegh, S. M., & Mansour, M. H. (2015). Risk assessment and allocation in highway construction projects in the UAE. Journal of Management in Engineering, 31(6), 15-22. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000365
Flanagan, R., & Norman, G. (1993). Risk management and construction. Victoria: Blackwell, Science Pty Ltd.
Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Geneva: Battelle Geneva Research Center.
Ghasemi, F., Sari, M. H. M., Yousefi, V., Falsafi, R., & Tamošaitienė, J. (2018). Project portfolio risk identification and analysis, considering project risk interactions and using Bayesian networks. Sustainability, 10(5), 1609. https://doi.org/10.3390/su10051609
Iqbal, S., Choudhry, R. M., Holschemacher, K., Ali, A., & Tamošaitienė, J. (2015). Risk management in construction projects. Technological and Economic Development of Economy, 21(1), 65-78. https://doi.org/10.3846/20294913.2014.994582
Islam, M. S., Nepal, M. P., Skitmore, M., & Attarzadeh, M. (2017). Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projects. Advanced Engineering Informatics, 33, 112-131. https://doi.org/10.1016/j.aei.2017.06.001
Jeng, D. J. F., & Tzeng, G. H. (2012). Social influence on the use of clinical decision support systems: Revisiting the unified theory of acceptance and use of technology by the fuzzy DEMATEL technique. Computers & Industrial Engineering, 62(3), 819-828. https://doi.org/10.1016/j.cie.2011.12.016
KarimiAzari, A., Mousavi, N., Mousavi, S. F., & Hosseini, S. (2011). Risk assessment model selection in construction industry. Expert Systems with Applications, 38(8), 9105-9111. https://doi.org/10.1016/j.eswa.2010.12.110
Leung, L. C., & Cao, D. (2000). On consistency and ranking of alternatives in fuzzy AHP. European Journal of Operational Research, 124, 102-113. https://doi.org/10.1016/S0377-2217(99)00118-6
Liang, H., Ren, J., Gao, Z., Gao, S., Luo, X., Dong, L., & Scipioni, A. (2016). Identification of critical success factors for sustainable development of biofuel industry in China based on grey decision-making trial and evaluation laboratory (DEMATEL). Journal of Cleaner Production, 131, 500-508. https://doi.org/10.1016/j.jclepro.2016.04.151
Lin, C., & Wu, W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205-213. https://doi.org/10.1016/j.eswa.2006.08.012
Liou, J. J. H., Tamošaitienė, J., Zavadskas, E. K., & Tzeng, G.-H. (2016). New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management. International Journal of Production Research, 54(1), 114-134. https://doi.org/10.1080/00207543.2015.1010747
Nieto-Morote, A., & Ruz-Vila, F. (2011). A fuzzy approach to construction project risk assessment. International Journal of Project Management, 29(2), 220-231. https://doi.org/10.1016/j.ijproman.2010.02.002
Opricovic, S., & Tzeng, G. (2003). Defuzzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11, 635-652. https://doi.org/10.1142/S0218488503002387
Özdemir, A., & Tüysüz, F. (2017). An integrated fuzzy DEMATEL and fuzzy ANP based balanced scorecard approach: application in Turkish higher education institutions. Journal of Multiple-Valued Logic & Soft Computing, 28(2/3), 251-287.
Ren, J., & Sovacool, B. K. (2014). Quantifying, measuring, and strategizing energy security: Determining the most meaningful dimensions and metrics. Energy, 76, 838-849. https://doi.org/10.1016/j.energy.2014.08.083
Saaty, T. L. (1996). The analytic network process. Pittsburgh: RWS Publications.
Saaty, T. L. (2005). Theory and applications of the analytic network process decision making with benefits, opportunities, costs and risks. Pittsburgh: RWS Publications.
Samantra, C., Datta, S., & Mahapatra, S. S. (2017). Fuzzy based risk assessment module for metropolitan construction project: An empirical study. Engineering Applications of Artificial Intelligence, 65, 449-464. https://doi.org/10.1016/j.engappai.2017.04.019
Seker, S., & Zavadskas, E. K. (2017). Application of fuzzy DEMATEL method for analyzing occupational risks on construction sites. Sustainability, 9, 2083. https://doi.org/10.3390/su9112083
Tah, J. H. M., & Carr, V. (2000). A proposal for construction project risk assessment using fuzzy logic. Construction Management and Economics, 18(4), 491-500. https://doi.org/10.1080/01446190050024905
Tamošaitienė, J., Turskis, Z., & Zavadskas, E. K. (2008). Modeling of contractor selection taking into account different risk level. In The 25th International Symposium on Automation and Robotics in Construction (ISARC 2008) (pp. 676-681). https://doi.org/10.22260/ISARC2008/0099
Tamošaitienė, J., Zavadskas, E. K., & Turskis, Z. (2013). Multi-criteria risk assessment of a construction project. Procedia Computer Science, 17, 129-133. https://doi.org/10.1016/j.procs.2013.05.018
Taylan, O., Bafail, A. O., Abdulaal, R. M. S., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116. https://doi.org/10.1016/j.asoc.2014.01.003
Tsai, S. B., Chien, M. F., Xue, Y., Li, L., Jiang, X., Chen, Q., & Wang, L. (2015). Using the fuzzy DEMATEL to determine environmental performance: A case of printed circuit board industry in Taiwan. PLOS ONE, 10(6), 0129153. https://doi.org/10.1371/journal.pone.0129153
Tseng, M. L. (2011). Using a hybrid MCDM model to evaluate firm environmental knowledge management in uncertainty. Applied Soft Computing, 11(1), 1340-1352. https://doi.org/10.1016/j.asoc.2010.04.006
Tzeng, G., Chiang, C., & Li, C. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028-1044. https://doi.org/10.1016/j.eswa.2006.02.004
Uygun, Ö., & Dede, A. (2016). Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Computers & Industrial Engineering, 102, 502-511. https://doi.org/10.1016/j.cie.2016.02.020
Valipour, A., Yahaya, N., Noor, N. Md., Antuchevičienė, J., & Tamošaitienė, J. (2017). Hybrid SWARA-COPRAS method for risk assessment in deep foundation excavation project: an Iranian case study. Journal of Civil Engineering and Management, 23(4), 524-532. https://doi.org/10.3846/13923730.2017.1281842
Wang, T., Wang, S., Zhang, L., Huang, Z., & Li, Y. (2016). A major infrastructure risk-assessment framework: Application to a cross-sea route project in China. International Journal of Project Management, 34(7), 1403-1415. https://doi.org/10.1016/j.ijproman.2015.12.006
Wei, W. W., & Yu, T. L. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499-507. https://doi.org/10.1016/j.eswa.2005.12.005
Winch, G. (2002). Managing construction projects: An information processing approach. Wiley.
Yazdani-Chamzini, A. (2014). Proposing a new methodology based on fuzzy logic for tunneling risk assessment. Journal of Civil Engineering and Management, 20(1), 82-94. https://doi.org/10.3846/13923730.2013.843583
Zavadskas, E. K., Turskis, Z., & Tamošaitienė, J. (2010). Risk assessment of construction projects. Journal of Civil Engineering and Management, 16(1), 33-46. https://doi.org/10.3846/jcem.2010.03
Zhou, J. L., Bai, Z. H., & Sun, Z. Y. (2014). A hybrid approach for safety assessment in high-risk hydropower-construction-project work systems. Safety Science, 64, 163-172. https://doi.org/10.1016/j.ssci.2013.12.008