Assessing construction labours’ safety level: a fuzzy MCDM approach
Abstract
Risk decision matrix has widely been favoured by the researchers in the area of construction safety risk assessment. Although it provides the construction safety professionals with the final illustration of the risks magnitude, it suffers from major shortcomings, including inability to considering the importance of probability and severity, impaired analysis resulting from the use of raw numbers for ratings, and the limited range of classifications for assessing the risks. All these shortages give an impaired insight to the concerned parties, deteriorating the involved workers’ safety. As such, this paper aims to develop a novel Risk Assessment Model (RAM) through the integration of the Fuzzy Best Worst Method (FBWM) with the Interval-Valued Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IVFTOPSIS). Based on the application of RAM to a real-life case study, it was observed that the developed RAM contributes to the body of construction safety risk assessment in five unique ways: (1) computing the importance of the two risk parameters (i.e. probability and severity) using fuzzy-reference-based comparisons, (2) obviating the needs for having statistical data, (3) prioritizing the identified risks using the combination of interval-valued triangular fuzzy numbers with TOPSIS, (4) providing the safety analysts with wider ranges of classifications for conducting risk assessment, and (5) providing the safety professionals with appropriate evaluation strategies for controlling the analysed risks. The developed model in the study can be applied to any projects, giving a conclusive plan to the concerned safety professionals for adopting the further prudent mitigation measurements.
Keyword : safety risk assessment, construction safety, interval-valued fuzzy number, multi-criteria decision-making method, fuzzy best-worst method, fuzzy TOPSIS, interval-valued fuzzy TOPSIS
How to Cite
Mohandes, S. R., Sadeghi, H., Mahdiyar, A., Durdyev, S., Banaitis, A., Yahya, K., & Ismail, S. (2020). Assessing construction labours’ safety level: a fuzzy MCDM approach. Journal of Civil Engineering and Management, 26(2), 175-188. https://doi.org/10.3846/jcem.2020.11926
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hatami-Marbini, A., Tavana, M., Moradi, M., & Kangi, F. (2013). A fuzzy group Electre method for safety and health assessment in hazardous waste recycling facilities. Safety Science, 51(1), 414–426. https://doi.org/10.1016/j.ssci.2012.08.015
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Li, J., Bao, C., & Wu, D. (2018). How to design rating schemes of risk matrices: A sequential updating approach. Risk Analysis, 38(1), 99–117. https://doi.org/10.1111/risa.12810
Liou, J. J. H., Yen, L., & Tzeng, G.-H. (2008). Building an effective safety management system for airlines. Journal of Air Transport Management, 14(1), 20–26. https://doi.org/10.1016/j.jairtraman.2007.10.002
Lu, S. T., Lin, C. W., & Ko, P. H. (2008, September 5–7). Application of Analytic Network Process (ANP) in assessing construction risk of urban bridge project. In Second International Conference on Innovative Computing, Information and Control, ICICIC 2007, Kumamoto, Japan. https://doi.org/10.1109/ICICIC.2007.172
McCann, M. (2013). Deaths and injuries involving elevators and escalators. Silver Spring, MD: The Center for Construction Research and Training.
Mikaeil, R., Ozcelik, Y., Yousefi, R., Ataei, M., & Hosseini, S. M. (2013). Ranking the sawability of ornamental stone using Fuzzy Delphi and multi-criteria decision-making techniques. International Journal of Rock Mechanics and Mining Sciences, 58, 118–126. https://doi.org/10.1016/j.ijrmms.2012.09.002
Mitropoulos, P., & Namboodiri, M. (2010). New method for measuring the safety risk of construction activities: Task demand assessment. Journal of Construction Engineering and Management, 137(1), 30–38. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000246
Mohandes, S. R., & Zhang, X. (2019). Towards the development of a comprehensive hybrid fuzzy-based occupational risk assessment model for construction workers. Safety Science, 115, 294–309. https://doi.org/10.1016/j.ssci.2019.02.018
Mohandes, S. R., Zhang, X., & Mahdiyar, A. (2019). A comprehensive review on the application of artificial neural networks in building energy analysis. Neurocomputing, 340, 55–75. https://doi.org/10.1016/j.neucom.2019.02.040
Mondal, S. P. (2016). Differential equation with interval valued fuzzy number and its applications. International Journal of System Assurance Engineering and Management, 7(3), 370–386. https://doi.org/10.1007/s13198-016-0474-7
Nilsen, T., & Aven, T. (2003). Models and model uncertainty in the context of risk analysis. Reliability Engineering & System Safety, 79(3), 309–317. https://doi.org/10.1016/S0951-8320(02)00239-9
Papazoglou, I. A., Aneziris, O. N., Bellamy, L. J., Ale, B. J. M., & Oh, J. (2017). Quantitative occupational risk model: Single hazard. Reliability Engineering and System Safety, 160, 162–173. https://doi.org/10.1016/j.ress.2016.12.010
Pinto, A. (2014). QRAM a qualitative occupational safety risk assessment model for the construction industry that incorporate uncertainties by the use of fuzzy sets. Safety Science, 63, 57–76. https://doi.org/10.1016/j.ssci.2013.10.019
Pinto, A., Nunes, I. L., & Ribeiro, R. A. (2011). Occupational risk assessment in construction industry – overview and reflection. Safety Science, 49(5), 616–624. https://doi.org/10.1016/j.ssci.2011.01.003
Rausand, M. (2013). Risk assessment: Theory, methods, and applications. John Wiley & Sons. https://doi.org/10.1002/9781118281116.ch8
Ren, Z., Xu, Z., & Wang, H. (2017). Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method. Information Sciences, 388–389, 1–16. https://doi.org/10.1016/j.ins.2017.01.024
Sun, C.-C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745–7754. https://doi.org/10.1016/j.eswa.2010.04.066
Tam, C. M., Zeng, S. X.,& Deng, Z. M. (2004). Identifying elements of poor construction safety management in China. Safety Science, 42(7), 569–586. https://doi.org/10.1016/j.ssci.2003.09.001
Tzeng, G.-H., & Huang, J.-J. (2011). Multiple attribute decision making: Methods and applications. CRC press. https://doi.org/10.1201/b11032
Wang, Y. M., Liu, J., & Elhag, T. M. S. (2008). An integrated AHP-DEA methodology for bridge risk assessment. Computers and Industrial Engineering, 54(3), 513–525. https://doi.org/10.1016/j.cie.2007.09.002
Yazdi, M. (2018). Risk assessment based on novel intuitionistic fuzzy-hybrid-modified TOPSIS approach. Safety Science, 110, 438–448. https://doi.org/10.1016/j.ssci.2018.03.005
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zarikas, V., Loupis, M., Papanikolaou, N., & Kyritsi, C. (2013). Statistical survey of elevator accidents in Greece. Safety Science, 59, 93–103. https://doi.org/10.1016/j.ssci.2013.05.001
Zayed, T., Amer, M., & Pan, J. (2008). Assessing risk and uncertainty inherent in Chinese highway projects using AHP. International Journal of Project Management, 26(4), 408–419. https://doi.org/10.1016/j.ijproman.2007.05.012
Zhang, H., Xie, J., Lu, W., Zhang, Z., & Fu, X. (2019). Novel ranking method for intuitionistic fuzzy values based on information fusion. Computers and Industrial Engineering, 133, 139–152. https://doi.org/10.1016/j.cie.2019.05.006
Zhao, D., McCoy, A., Kleiner, B., & Feng, Y. (2016). Integrating safety culture into OSH risk mitigation: a pilot study on the electrical safety. Journal of Civil Engineering and Management, 22(6), 800–807. https://doi.org/10.3846/13923730.2014.914099
Aminbakhsh, S., Gunduz, M., & Sonmez, R. (2013). Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects. Journal of Safety Research, 46, 99–105. https://doi.org/10.1016/j.jsr.2013.05.003
Amiri, M., Ardeshir, A., & Fazel Zarandi, M. H. (2017). Fuzzy probabilistic expert system for occupational hazard assessment in construction. Safety Science, 93, 16–28. https://doi.org/10.1016/j.ssci.2016.11.008
Aneziris, O. N., Papazoglou, I. A., & Kallianiotis, D. (2010). Occupational risk of tunneling construction. Safety Science, 48(8), 964–972. https://doi.org/10.1016/j.ssci.2009.11.003
Armaghani, D. J., Mohamad, E. T., Momeni, E., & Narayanasamy, M. S. (2015). An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bulletin of Engineering Geology and the Environment, 74(4), 1301–1319. https://doi.org/10.1007/s10064-014-0687-4
Ashtiani, B., Haghighirad, F., Makui, A., & ali Montazer, G. (2009). Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets. Applied Soft Computing, 9(2), 457–461. https://doi.org/10.1016/j.asoc.2008.05.005
Baybutt, P. (2018). Guidelines for designing risk matrices. Process Safety Progress, 37(1), 49–55. https://doi.org/10.1002/prs.11905
Bejarbaneh, B. Y., Bejarbaneh, E. Y., Fahimifar, A., Armaghani, D. J., & Majid, M. Z. A. (2018). Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bulletin of Engineering Geology and the Environment, 77(1), 345–361. https://doi.org/10.1007/s10064-016-0983-2
Celik, E., Gul, M., Gumus, A. T., & Guneri, A. F. (2012). A fuzzy TOPSIS approach based on trapezoidal numbers to material selection problem. Journal of Information Technology Applications & Management, 19(3), 19–30.
Debnath, J., Biswas, A., Sivan, P., Sen, K. N., & Sahu, S. (2016). Fuzzy inference model for assessing occupational risks in construction sites. International Journal of Industrial Ergonomics, 55, 114–128. https://doi.org/10.1016/j.ergon.2016.08.004
Djapan, M., Macuzic, I., Tadic, D., & Baldissone, G. (2018). An innovative prognostic risk assessment tool for manufacturing sector based on the management of the human, organizational and technical/technological factors. Safety Science, 119, 280–291. https://doi.org/10.1016/j.ssci.2018.02.032
Faber, M. H., & Stewart, M. G. (2003). Risk assessment for civil engineering facilities: critical overview and discussion. Reliability Engineering & System Safety, 80(2), 173–184. https://doi.org/10.1016/S0951-8320(03)00027-9
Gunduz, M., Birgonul, M. T., & Ozdemir, M. (2016). Fuzzy structural equation model to assess construction site safety performance. Journal of Construction Engineering and Management, 143(4), 4016112. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001259
Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23–31. https://doi.org/10.1016/j.knosys.2017.01.010
Hallowell, M. R. (2008). A formal model for construction safety and health risk management. Oregon State University.
Hatami-Marbini, A., Tavana, M., Moradi, M., & Kangi, F. (2013). A fuzzy group Electre method for safety and health assessment in hazardous waste recycling facilities. Safety Science, 51(1), 414–426. https://doi.org/10.1016/j.ssci.2012.08.015
Hinze, J., Devenport, J. N., & Giang, G. (2006). Analysis of construction worker injuries that do not result in lost time. Journal of Construction Engineering and Management, 132(3), 321–326. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:3(321)
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. New York: Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9
International Labour Organization. (2014, August 24–27). Safety and health at work: A vision for sustainable prevention. In XX World Congress on Safety and Health at Work 2014: Global Forum for Prevention, Frankfurt, Germany.
Lee, C. Y., & Lim, C. H. (2014, December 9–12). Risk analysis and rescue operation for machine roomless lift: A case study. In 2014 IEEE International Conference on Industrial Engineering and Engineering Management, Bandar Sunway, Malaysia. https://doi.org/10.1109/IEEM.2014.7058841
Li, J., Bao, C., & Wu, D. (2018). How to design rating schemes of risk matrices: A sequential updating approach. Risk Analysis, 38(1), 99–117. https://doi.org/10.1111/risa.12810
Liou, J. J. H., Yen, L., & Tzeng, G.-H. (2008). Building an effective safety management system for airlines. Journal of Air Transport Management, 14(1), 20–26. https://doi.org/10.1016/j.jairtraman.2007.10.002
Lu, S. T., Lin, C. W., & Ko, P. H. (2008, September 5–7). Application of Analytic Network Process (ANP) in assessing construction risk of urban bridge project. In Second International Conference on Innovative Computing, Information and Control, ICICIC 2007, Kumamoto, Japan. https://doi.org/10.1109/ICICIC.2007.172
McCann, M. (2013). Deaths and injuries involving elevators and escalators. Silver Spring, MD: The Center for Construction Research and Training.
Mikaeil, R., Ozcelik, Y., Yousefi, R., Ataei, M., & Hosseini, S. M. (2013). Ranking the sawability of ornamental stone using Fuzzy Delphi and multi-criteria decision-making techniques. International Journal of Rock Mechanics and Mining Sciences, 58, 118–126. https://doi.org/10.1016/j.ijrmms.2012.09.002
Mitropoulos, P., & Namboodiri, M. (2010). New method for measuring the safety risk of construction activities: Task demand assessment. Journal of Construction Engineering and Management, 137(1), 30–38. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000246
Mohandes, S. R., & Zhang, X. (2019). Towards the development of a comprehensive hybrid fuzzy-based occupational risk assessment model for construction workers. Safety Science, 115, 294–309. https://doi.org/10.1016/j.ssci.2019.02.018
Mohandes, S. R., Zhang, X., & Mahdiyar, A. (2019). A comprehensive review on the application of artificial neural networks in building energy analysis. Neurocomputing, 340, 55–75. https://doi.org/10.1016/j.neucom.2019.02.040
Mondal, S. P. (2016). Differential equation with interval valued fuzzy number and its applications. International Journal of System Assurance Engineering and Management, 7(3), 370–386. https://doi.org/10.1007/s13198-016-0474-7
Nilsen, T., & Aven, T. (2003). Models and model uncertainty in the context of risk analysis. Reliability Engineering & System Safety, 79(3), 309–317. https://doi.org/10.1016/S0951-8320(02)00239-9
Papazoglou, I. A., Aneziris, O. N., Bellamy, L. J., Ale, B. J. M., & Oh, J. (2017). Quantitative occupational risk model: Single hazard. Reliability Engineering and System Safety, 160, 162–173. https://doi.org/10.1016/j.ress.2016.12.010
Pinto, A. (2014). QRAM a qualitative occupational safety risk assessment model for the construction industry that incorporate uncertainties by the use of fuzzy sets. Safety Science, 63, 57–76. https://doi.org/10.1016/j.ssci.2013.10.019
Pinto, A., Nunes, I. L., & Ribeiro, R. A. (2011). Occupational risk assessment in construction industry – overview and reflection. Safety Science, 49(5), 616–624. https://doi.org/10.1016/j.ssci.2011.01.003
Rausand, M. (2013). Risk assessment: Theory, methods, and applications. John Wiley & Sons. https://doi.org/10.1002/9781118281116.ch8
Ren, Z., Xu, Z., & Wang, H. (2017). Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method. Information Sciences, 388–389, 1–16. https://doi.org/10.1016/j.ins.2017.01.024
Sun, C.-C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745–7754. https://doi.org/10.1016/j.eswa.2010.04.066
Tam, C. M., Zeng, S. X.,& Deng, Z. M. (2004). Identifying elements of poor construction safety management in China. Safety Science, 42(7), 569–586. https://doi.org/10.1016/j.ssci.2003.09.001
Tzeng, G.-H., & Huang, J.-J. (2011). Multiple attribute decision making: Methods and applications. CRC press. https://doi.org/10.1201/b11032
Wang, Y. M., Liu, J., & Elhag, T. M. S. (2008). An integrated AHP-DEA methodology for bridge risk assessment. Computers and Industrial Engineering, 54(3), 513–525. https://doi.org/10.1016/j.cie.2007.09.002
Yazdi, M. (2018). Risk assessment based on novel intuitionistic fuzzy-hybrid-modified TOPSIS approach. Safety Science, 110, 438–448. https://doi.org/10.1016/j.ssci.2018.03.005
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zarikas, V., Loupis, M., Papanikolaou, N., & Kyritsi, C. (2013). Statistical survey of elevator accidents in Greece. Safety Science, 59, 93–103. https://doi.org/10.1016/j.ssci.2013.05.001
Zayed, T., Amer, M., & Pan, J. (2008). Assessing risk and uncertainty inherent in Chinese highway projects using AHP. International Journal of Project Management, 26(4), 408–419. https://doi.org/10.1016/j.ijproman.2007.05.012
Zhang, H., Xie, J., Lu, W., Zhang, Z., & Fu, X. (2019). Novel ranking method for intuitionistic fuzzy values based on information fusion. Computers and Industrial Engineering, 133, 139–152. https://doi.org/10.1016/j.cie.2019.05.006
Zhao, D., McCoy, A., Kleiner, B., & Feng, Y. (2016). Integrating safety culture into OSH risk mitigation: a pilot study on the electrical safety. Journal of Civil Engineering and Management, 22(6), 800–807. https://doi.org/10.3846/13923730.2014.914099