A Hybrid Fuzzy Risk Assessment Framework for Determining Building Demolition Safety Index

Abstract

Risk assessment is one of the most effective actions in the safety management of demolition projects. This paper provides a framework to determine building demolition safety index (BDSI), which shows the safety level of a building being demolished. Two phases are involved in this study. In the first phase, 11 potential risks in building demolition and their influencing factors were identified, evaluated, and classified using a hybrid approach consisting of the Delphi method, Fine-Kinney method, fuzzy fault tree analysis (FTA), fuzzy technique for order preference by similarity to ideal solution (TOPSIS), and fuzzy inference system (FIS). In the second phase, a checklist of the most important safety factors and sub-factors in the demolition operation was provided, and the equations needed to calculate BDSI were presented. The first phase of the study was validated by comparing the study’s results with available demographic data from Tehran Construction Engineering Organization, Iran. The second phase was validated by calculating the BDSI for two buildings and evaluating the relationship between BDSI and safety level. BDSI is useful for building demolition projects because it allows project managers to have a more realistic view of the risk level of the project and accordingly take the necessary measures to prevent accidents.

This is a preview of subscription content, access via your institution.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

References

  1. Abdelgawad M, Fayek AR (2010) Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP. Journal of Construction Engineering and Management 136(9):1028–1036, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000210

    Article  Google Scholar 

  2. Abdelgawad M, Fayek AR (2011) Fuzzy reliability analyzer: Quantitative assessment of risk events in the construction industry using fuzzy fault-tree analysis. Journal of Construction Engineering and Management 137(4):294–302, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000285

    Article  Google Scholar 

  3. Abdelgawad M, Fayek AR (2012) Comprehensive hybrid framework for risk analysis in the construction industry using combined failure mode and effect analysis, fault trees, event trees, and fuzzy logic. Journal of Construction Engineering and Management 138(5):642–651, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000471

    Article  Google Scholar 

  4. Abdollahzadeh G, Rastgoo S (2015) Risk assessment in bridge construction projects using fault tree and event tree analysis methods based on fuzzy logic. ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 1(3), DOI: https://doi.org/10.1115/1.4030779

  5. Ardeshir A, Amiri M, Ghasemi Y, Errington M (2014) Risk assessment of construction projects for water conveyance tunnels using fuzzy fault tree analysis. International Journal of Civil Engineering 12(4):396–412, http://ijce.iust.ac.ir/article-1-878-en.html

    Google Scholar 

  6. Atkinson AR, Westall R (2010) The relationship between integrated design and construction and safety on construction projects. Construction Management and Economics 28(9):1007–1017, DOI: https://doi.org/10.1080/01446193.2010.504214

    Article  Google Scholar 

  7. Cao N (2006) Supply chain performance measurement in textile and apparel industries. PhD Thesis, Hong Kong Polytechnic University, Hong Kong

    Google Scholar 

  8. Chen C-T (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems 114(1):1–9, DOI: https://doi.org/10.1016/S0165-0114(97)00377-1

    Article  Google Scholar 

  9. Ertaş H, Erdoğan AS (2017) An analysis of occupational accidents in demolition work. Civil Engineering and Architecture 5(2):37–51, DOI: https://doi.org/10.13189/cea.2017.050201

    Article  Google Scholar 

  10. Gebrehiwet T, Luo H (2019) Risk level evaluation on construction project lifecycle using fuzzy comprehensive evaluation and TOPSIS. Symmetry 11(1):12, DOI: https://doi.org/10.3390/sym11010012

    Article  Google Scholar 

  11. Gurcanli GE, Bilir S, Sevim M (2015) Activity based risk assessment and safety cost estimation for residential building construction projects. Safety Science 80:1–12, DOI: https://doi.org/10.1016/j.ssci.2015.07.002

    Article  Google Scholar 

  12. Gürcanli GE, Müngen U (2013) Analysis of construction accidents in Turkey and responsible parties. Industrial Health 51(6):581–595, DOI: https://doi.org/10.2486/indhealth.2012-0139

    Article  Google Scholar 

  13. Haghshenas SS, Neshaei MAL, Pourkazem P, Haghshenas SS (2016) The risk assessment of dam construction projects using fuzzy TOPSIS (case study: Alavian Earth Dam). Civil Engineering Journal 2(4):158–167, DOI: https://doi.org/10.28991/cej-2016-00000022

    Article  Google Scholar 

  14. Hair JF, Anderson RE, Tatham RL, Black WC (1995) Multivariate data analysis with readings. Prentice Hall, Englewood Cliffs, NJ, USA

    Google Scholar 

  15. Hallowell MR, Gambatese JA (2009) Construction safety risk mitigation. Journal of Construction Engineering and Management 135(12):1316–1323, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000107

    Article  Google Scholar 

  16. Hallowell MR, Gambatese JA (2010) Qualitative research: Application of the Delphi method to CEM research. Journal of Construction Engineering and Management 136(1):99–107, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000137

    Article  Google Scholar 

  17. Hwang C-L, Yoon K (1981) Methods for multiple attribute decision making. In: Multiple attribute decision making. Springer, Berlin, Germany, 58–191, DOI: https://doi.org/10.1007/978-3-642-48318-9_3

    Google Scholar 

  18. Ilbahar E, Karaşan A, Cebi S, Kahraman C (2018) A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety Science 103:124–136, DOI: https://doi.org/10.1016/j.ssci.2017.10.025

    Article  Google Scholar 

  19. Jordan E, Javernick-Will A (2013) Indicators of community recovery: Content analysis and Delphi approach. Natural Hazards Review 14(1):21–28, DOI: https://doi.org/10.1061/(ASCE)NH.1527-6996.0000087

    Article  Google Scholar 

  20. KarimiAzari A, Mousavi N, Mousavi SF, Hosseini S (2011) Risk assessment model selection in construction industry. Expert Systems with Applications 38(8):9105–9111, DOI: https://doi.org/10.1016/j.eswa.2010.12.110

    Article  Google Scholar 

  21. Kaya T, Kahraman C (2011) An integrated fuzzy AHP-ELECTRE methodology for environmental impact assessment. Expert Systems with Applications 38(7):8553–8562, DOI: https://doi.org/10.1016/j.eswa.2011.01.057

    Article  Google Scholar 

  22. Kinney GF, Wiruth A (1976) Practical risk analysis for safety management. Naval Weapons Center, China Lake, CA, USA

    Google Scholar 

  23. Lee P-C, Wei J, Ting H-I, Lo T-P, Long D, Chang L-M (2019) Dynamic analysis of construction safety risk and visual tracking of key factors based on behavior-based safety and building information modeling. KSCE Journal of Civil Engineering 23(10):4155–4167, DOI: https://doi.org/10.1007/s12205-019-0283-z

    Article  Google Scholar 

  24. Maghsoodi AI, Khalilzadeh M (2018) Identification and evaluation of construction projects’ critical success factors employing fuzzy-topsis approach. KSCE Journal of Civil Engineering 22(5):1593–1605, DOI: https://doi.org/10.1007/s12205-017-1970-2

    Article  Google Scholar 

  25. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1):1–13, DOI: https://doi.org/10.1016/S0020-7373(75)80002-2

    Google Scholar 

  26. MCLS (2011) Safety at construction sites. Ministry of Cooperatives Labour and Social Welfare. Retrieved September 5, 2019, https://www.mcls.gov.ir/en/news/23213

  27. Mohammadi A, Tavakolan M (2013) Construction project risk assessment using combined fuzzy and FMEA. 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), June 24–28, Edmonton, AB, Canada, 232–237, DOI: https://doi.org/10.1109/IFSA-NAFIPS.2013.6608405

  28. Mohandes SR, Zhang X (2019) Towards the development of a comprehensive hybrid fuzzy-based occupational risk assessment model for construction workers. Safety Science 115:294–309, DOI: https://doi.org/10.1016/j.ssci.2019.02.018

    Article  Google Scholar 

  29. Nieto-Morote A, Ruz-Vila F (2011) A fuzzy approach to construction project risk assessment. International Journal of Project Management 29(2):220–231, DOI: https://doi.org/10.1016/j.ijproman.2010.02.002

    Article  Google Scholar 

  30. Norouzi A, Namin HG (2019) A hybrid fuzzy TOPSIS — Best worst method for risk prioritization in megaprojects. Civil Engineering Journal 5(6):1257–1272, DOI: https://doi.org/10.28991/cej-2019-03091330

    Article  Google Scholar 

  31. Patel D, Jha K (2017) Developing a process to evaluate construction project safety hazard index using the possibility approach in India. Journal of Construction Engineering and Management 143(1): 04016081, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001205

    Article  Google Scholar 

  32. Ravanshadnia M, Rajaie H, Abbasian HR (2010) Hybrid fuzzy MADM project-selection model for diversified construction companies. Canadian Journal of Civil Engineering 37(8):1082–1093, DOI: https://doi.org/10.1139/L10-048

    Article  Google Scholar 

  33. Rozenfeld O, Sacks R, Rosenfeld Y, Baum H (2010) Construction job safety analysis. Safety Science 48(4):491–498, DOI: https://doi.org/10.1016/j.ssci.2009.12.017

    Article  Google Scholar 

  34. Shanmugapriya S, Subramanian K (2016) Developing a PLS path model to investigate the factors influencing safety performance improvement in construction organizations. KSCE Journal of Civil Engineering 20(5):1138–1150, DOI: https://doi.org/10.1007/s12205-015-0442-9

    Article  Google Scholar 

  35. Sousa V, Almeida NM, Dias LA (2015) Risk-based management of occupational safety and health in the construction industry — Part 2: Quantitative model. Safety Science 74:184–194, DOI: https://doi.org/10.1016/j.ssci.2015.01.003

    Article  Google Scholar 

  36. Stankovic M, Stankovic V (2013) Comparative analysis of methods for risk assessment-Kinney and Auva. Safety Engineering 3(3):129–136, DOI: https://doi.org/10.7562/SE2013.3.03.04

    Article  Google Scholar 

  37. Tam C, Zeng S, Deng Z (2004) Identifying elements of poor construction safety management in China. Safety Science 42(7):569–586, DOI: https://doi.org/10.1016/j.ssci.2003.09.001

    Article  Google Scholar 

  38. Tang J, Liu X, Wang W (2020) A hybrid risk prioritization method based on generalized TODIM and BWM for Fine-Kinney under interval type-2 fuzzy environment. Human and Ecological Risk Assessment: An International Journal 1–26, DOI: https://doi.org/10.1080/10807039.2020.1789840

  39. Verma AK, Srividya A, Gaonkar RP (2006) Fuzzy-reliability engineering: Concepts and applications. Narosa Publishing, New Delhi, India, 88–127

    Google Scholar 

  40. Wang W, Liu X, Qin Y (2018) A fuzzy Fine-Kinney-based risk evaluation approach with extended MULTIMOORA method based on Choquet integral. Computers & Industrial Engineering 125:111–123, DOI: https://doi.org/10.1016/j.cie.2018.08.019

    Article  Google Scholar 

  41. Zaharuddin W, Paraskevas I, Liu C (2009) Accident avoidance importance for building demolition. In: CIB W099 2009: Working together: Planning, designing and building a healthy and safe construction industry. RMIT University, Melbourne, Australia, 9–14

    Google Scholar 

  42. Zeng J, An M, Smith NJ (2007) Application of a fuzzy based decision making methodology to construction project risk assessment. International Journal of Project Management 25(6):589–600, DOI: https://doi.org/10.1016/j.ijproman.2007.02.006

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Mehdi Ravanshadnia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alipour-Bashary, M., Ravanshadnia, M., Abbasianjahromi, H. et al. A Hybrid Fuzzy Risk Assessment Framework for Determining Building Demolition Safety Index. KSCE J Civ Eng (2021). https://doi.org/10.1007/s12205-021-0812-4

Download citation

Keywords

  • Safety risk assessment
  • Fuzzy FTA
  • Fuzzy TOPSIS
  • Fuzzy inference system
  • Fine-Kinney
  • Building demolition safety index