Comprehensive assessment model on accident situations of the construction industry in China: a macro-level perspective
Abstract
As one of the most high-risk sections, the construction industry has traditionally been the research hotspot. Yet little attention has been paid to macro-level accident situations of the entire industry. Therefore, this study develops a comprehensive assessment model on accident situations of Chinese building industry, aiming at assisting the government to better understand and improve accident situations of the entire industry. Based on China conditions, six indicators related to accident situations are firstly selected to establish an indicator system; then structure entropy weight method is proposed to determine indicator weighs, with dynamic classification method to explore the characteristics of accident situations. The results demonstrate that the provinces with poor accident situations account for 53% of all provinces, and they are mainly distributed in the central and western regions of China where there exist the underdeveloped economy. Meanwhile, some provinces experience poor accident situations that could be out-of-control, especially for Hebei. Provinces in the southeastern and northeastern regions of China perform relatively well, but they still have much improvement room for accident situations. The findings validate the rationality of the developed model and can provide valuable insights of safety regulation strategies for the government from the macro-level perspective.
First published online 17 December 2019
Keyword : construction industry, accident situation, structure entropy weight method (SEWM), dynamic classification method (DCM), macro-level perspective
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Bellamy, L. J. (2015). Exploring the relationship between major hazard, fatal and non-fatal accidents through outcomes and causes. Safety Science, 71, 93-103. https://doi.org/10.1016/j.ssci.2014.02.009
Cai, W., Dou, L. M., Si, G. Y., Cao, A. Y., He, J., & Liu, S. (2016). A principal component analysis/fuzzy comprehensive evaluation model for coal burst liability assessment. International Journal of Rock Mechanics and Mining Sciences, 81, 62-69. https://doi.org/10.1016/j.ijrmms.2015.09.028
Chen, S., Shao, B., Chen, Y., & Zheng, X. (2015). Dynamic risk classification of facilities in hydroelectric power station. China Safety Science Journal, 25(2), 71-76 (in Chinese). http://doi.org/10.16265/j.cnki.issn1003-3033.2015.02.012
Coates, W. C. (2011). Analysis of the causes of construction fatalities from 2007 to 2009. Retrieved from http://ufdc.ufl.edu/UFE0042920/00001
Cui, Y., Feng, P., Jin, J. L., & Liu, L. (2018). Water resources carrying capacity evaluation and diagnosis based on set pair analysis and improved the Entropy weight method. Entropy, 20(5), 359. https://doi.org/10.3390/e20050359
Dai, S. Y., & Niu, D. X. (2017). Comprehensive evaluation of the sustainable development of power grid enterprises based on the model of fuzzy group ideal point method and combination weighting method with improved group order relation method and Entropy weight method. Sustainability, 9(10), 1900. https://doi.org/10.3390/su9101900
Davoudabadi, R., Mousavi, S. M., Saparauskas, J., & Gitinavard, H. (2019). Solving construction project selection problem by a new uncertain weighting and ranking based on compromise solution with linear assignment approach. Journal of Civil Engineering and Management, 25(3), 241-251. https://doi.org/10.3846/jcem.2019.8656
Depaire, B., Wets, G., & Vanhoof, K. (2008). Traffic accident segmentation by means of latent class clustering. Accident Analysis and Prevention, 40(4), 1257-1266. https://doi.org/10.1016/j.aap.2008.01.007
Ding, L., Chen, K. L., Cheng, S. G., & Wang, X. (2015). Water ecological carrying capacity of urban lakes in the context of rapid urbanization: A case study of East Lake in Wuhan. Physics and Chemistry of the Earth, 89-90, 104-113. https://doi.org/10.1016/j.pce.2015.08.004
Dong, X. S., Wang, X., Largay, J. A., Platner, J. W., Stafford, E., Cain, C. T., & Choi, S. D. (2015). Fatal falls in the U.S. residential construction industry. American Journal of Industrial Medicine, 57(9), 992-1000. https://doi.org/10.1002/ajim.22341
Dong, X. W. S., Fujimoto, A., Ringen, K., Stafford, E., Platner, J. W., Gittleman, J. L., & Wang, X. W. (2011). Injury underreporting among small establishments in the construction industry. American Journal of Industrial Medicine, 54(5), 339349. https://doi.org/10.1002/ajim.20928
Dong, G. H., Shen, J. Q., Jia, Y. Z., & Sun, F. H. (2018). Comprehensive evaluation of water resource security: Case study from Luoyang City, China. Water, 10(8), 1106. https://doi.org/10.3390/w10081106
Eteifa, S. O., & El-adaway, I. H. (2018). Using social network analysis to model the interaction between root causes of fatalities in the construction industry. Journal of Management in Engineering, 34(1), 1-15. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000567
Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., Foufou, S., & Bouras, A. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE Transactions on Emerging Topics in Computing, 2(3), 267-279. https://doi.org/10.1109/TETC.2014.2330519
Fang, W. L., Ding, L. Y., Luo, H. B., & Love, P. E. D. (2018). Falls from heights: A computer vision-based approach for safety harness detection. Automation in Construction, 91, 53-61. https://doi.org/10.1016/j.autcon.2018.02.018
Forteza, F. J., Carretero-Gomez, J. M., & Sese, A. (2017). Occupational risks, accidents on sites and economic performance of construction firms. Safety Science, 94, 61-76. https://doi.org/10.1016/j.ssci.2017.01.003
Hasanzadeh, S., Esmaeili, B., & Dodd, M. D. (2017). Impact of construction workers’ hazard identification skills on their visual attention. Journal Of Construction Engineering and Management, 143(10), 04017070. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001373
Hausken, K., & Zhuang, J. (2016). The strategic interaction between a company and the government surrounding disasters. Annals of Operations Research, 237(1-2), 27-40. https://doi.org/10.1007/s10479-014-1684-5
Hola, B. (2009). Methodology of estimation of accident situation in building industry. Archives of Civil and Mechanical Engineering, 9(1), 29-46. https://doi.org/10.1016/S1644-9665(12)60038-7
Hola, B., & Nowobilski, T. (2018). Classification of economic regions with regards to selected factors characterizing the construction industry. Sustainability, 10(5), 1637. https://doi.org/10.3390/su10051637
Hola, B., & Szostak, M. (2015). Analysis of the state of the accident rate in the construction industry in European Union countries. Archives of Civil Engineering, 61(4), 19-34. https://doi.org/10.1515/ace-2015-0033
Horta, I. M., & Camanho, A. S. (2014). Competitive positioning and performance assessment in the construction industry. Expert Systems with Applications, 41(4), 974-983. https://doi.org/10.1016/j.eswa.2013.06.064
Irumba, R. (2014). Spatial analysis of construction accidents in Kampala, Uganda. Safety Science, 64, 109-120. https://doi.org/10.1016/j.ssci.2013.11.024
Jain, A. K. (2008). Data clustering: 50 years beyond K-means. Berlin, Heidelberg: Springer.
Kang, Y., Siddiqui, S., Suk, S. J., Chi, S., & Kim, C. (2017). Trends of fall accidents in the US construction industry. Journal of Construction Engineering and Management, 143(8), 04017043. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001332
Karakhan, A. A., Rajendran, S., Gambatese, J., & Nnaji, C. (2018). Measuring and evaluating safety maturity of construction contractors: Multicriteria decision-making approach. Journal of Construction Engineering and Management, 144(7), 04018054. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001503
Kim, E., Yu, I., Kim, K., & Kim, K. (2011). Optimal set of safety education considering individual characteristics of construction workers. Canadian Journal of Civil Engineering, 38(5), 506-518. https://doi.org/10.1139/l11-024
Lai, C. G., Chen, X. H., Chen, X. Y., Wang, Z. L., Wu, X. S., & Zhao, S. W. (2015). A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory. Natural Hazards, 77(2), 1243-1259. https://doi.org/10.1007/s11069-015-1645-6
Li, X. X., Wang, K. S., Liu, L. W., Xin, J., Yang, H. R., & Gao, C. Y. (2011). Application of the Entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia Engineering, 26(4), 2085-2091. https://doi.org/10.1016/j.proeng.2011.11.2410
Li, G., Li, J., Sun, X., & Zhao, M. (2017). Research on a combined method of subjective-objective weighing and the its rationality. Management Review, 29(12), 17-26. http://doi.org/10.14120/j.cnki.cn11-5057/f.2017.12.002
Lin, Y. (1989). Numerical classification method and its application in rock mechanics. Shenyang: Northeast University Press.
Liu, A., & Wu, C. (2011). Correlation analysis between death rate per hundred million GDP and regional development level. China Safety Science Journal, 21(5), 3-9 (in Chinese). http://doi.org/10.16265/j.cnki.issn1003-3033.2011.05.019
Liu, F., Zhao, S. Z., Weng, M. C., & Liu, Y. Q. (2017). Fire risk assessment for large-scale commercial buildings based on structure Entropy weight method. Safety Science, 94, 26-40. https://doi.org/10.1016/j.ssci.2016.12.009
Ma, J., Chen, X., & Liu, W. (2015). Suggestions on the supervision and assessment of safety production in the construction industry. Construction and Architecture, 17, 34-37 (in Chinese). http://doi.org/10.3969/j.issn.0577-7429.2015.17.012
Ma, Y. H., & Zhao, Q. H. (2018). Decision-making in safety efforts: Role of the government in reducing the probability of workplace accidents in China. Safety Science, 104, 81-90. https://doi.org/10.1016/j.ssci.2017.12.038
Mahmoudzadeh, M., & Bafandeh, A. R. (2013). A new method for consistency test in fuzzy AHP. Journal of Intelligent & Fuzzy Systems, 25(2), 457-461. http://doi.org/10.3233/Ifs-120653
Marsh, S. M., & Fosbroke, D. E. (2015). Trends of occupational fatalities involving machines, United States, 1992-2010. American Journal of Industrial Medicine, 58(11), 1160-1173. https://doi.org/10.1002/ajim.22532
Mendeloff, J., & Burns, R. (2013). States with low non-fatal injury rates have high fatality rates and vice-versa. American Journal of Industrial Medicine, 56(5), 509-519. https://doi.org/10.1002/ajim.22047
Ministry of Housing and Urban-Rural Development of China (MHURD). (2019a). Announcement on the accident situation of production safety of the China housing and municipal engineering in 2018. Retrieved from http://www.mohurd.gov.cn/wjfb/201903/t20190326_239913.html
Ministry of Housing and Urban-Rural Development of China (MHURD). (2019b). The short reports of fatal accidents in China’s building construction activities. Retrieved from http://sgxxxt.mohurd.gov.cn/Public/AccidentList.aspx
National Bureau of Statistics of China (NBS). (2019). National data. Retrieved from http://data.stats.gov.cn/easyquery.htm? cn=C01
Poh, C. Q. X., Ubeynarayana, C. U., & Goh, Y. M. (2018). Safety leading indicators for construction sites: A machine learning approach. Automation in Construction, 93, 375-386. https://doi.org/10.1016/j.autcon.2018.03.022
Prascevic, N., & Prascevic, Z. (2017). Application of fuzzy AHP for ranking and selection of alternatives in construction project management. Journal of Civil Engineering and Management, 23(8), 1123-1135. https://doi.org/10.3846/13923730.2017.1388278
Raviv, G., Fishbain, B., & Shapira, A. (2017). Analyzing risk factors in crane-related near-miss and accident reports. Safety Science, 91, 192-205. https://doi.org/10.1016/j.ssci.2016.08.022
Sari, M., Selcuk, A. S., Karpuz, C., & Duzgun, H. S. B. (2009). Stochastic modeling of accident risks associated with an underground coal mine in Turkey. Safety Science, 47(1), 78-87. https://doi.org/10.1016/j.ssci.2007.12.004
Shao, B., Hu, Z., Liu, Q., Chen, S., & He, W. (2019). Fatal accident patterns of building construction activities in China. Safety Science, 111, 253-263. https://doi.org/10.1016/j.ssci.2018.07.019
Soltanzadeh, A., Mohammadfam, I., Moghimbeygi, A., & Ghiasvand, R. (2017). Exploring causal factors on the severity rate of occupational accidents in construction worksites. International Journal of Civil Engineering, 15(7A), 959-965. https://doi.org/10.1007/s40999-017-0184-9
Sunindijo, R. Y., & Zou, P. X. W. (2012). Political skill for developing construction safety climate. Journal of Construction Engineering and Management, 138(5), 605-612. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000482
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
Wang, B., Wu, C., Huang, L., Zhang, L., Kang, L., & Gao, K. (2018a). Prevention and control of major accidents (MAs) and particularly serious accidents (PSAs) in the industrial domain in China: Current status, recent efforts and future prospects. Process Safety and Environmental Protection, 117, 254-266. https://doi.org/10.1016/j.psep.2018.04.025
Wang, B., Wu, C., Kang, L., Huang, L., & Pan, W. (2018b). What are the new challenges, goals, and tasks of occupational health in China’s Thirteenth Five-Year Plan (13th FYP) period? Journal of Occupational Health, 60(3), 208-228. https://doi.org/10.1539/joh.2017-0275-RA
Wang, B., Wu, C., Kang, L., Reniers, G., & Huang, L. (2018c). Work safety in China’s Thirteenth Five-Year Plan period (2016–2020): Current status, new challenges and future tasks. Safety Science, 104, 164-178. https://doi.org/10.1016/j.ssci.2018.01.012
Wang, B., Wu, C., Reniers, G., Huang, L., Kang, L., & Zhang, L. (2018d). The future of hazardous chemical safety in China: Opportunities, problems, challenges and tasks. Science of the Total Environment, 643, 1-11. https://doi.org/10.1016/j.scitotenv.2018.06.174
Wegman, F., & Oppe, S. (2010). Benchmarking road safety performances of countries. Safety Science, 48(9), 1203-1211. https://doi.org/10.1016/j.ssci.2010.02.003
Wu, X., Liu, Q., Zhang, L., Skibniewski, M. J., & Wang, Y. (2015). Prospective safety performance evaluation on construction sites. Accident Analysis and Prevention, 78, 58-72. https://doi.org/10.1016/j.aap.2015.02.003
Wu, D. F., Wang, N. L., Yang, Z. P., Li, C. Z., & Yang, Y. P. (2018). Comprehensive evaluation of coal-fired power units using grey relational analysis and a hybrid Entropy-based weighting method. Entropy, 20(4), 215. https://doi.org/10.3390/e20040215
Xiahou, X., Yuan, J. F., Li, Q. M., & Skibniewski, M. J. (2018). Validating DFS concept in lifecycle subway projects in China based on incident case analysis and network analysis. Journal of Civil Engineering and Management, 24(1), 53-66. https://doi.org/10.3846/jcem.2018.300
Yassin, A. S., & Martonik, J. F. (2004). The effectiveness of the revised scaffold safety standard in the construction industry. Safety Science, 42(10), 921-931. https://doi.org/10.1016/j.ssci.2004.05.001
Yuan, Z. (2005). Is the construction safety good or poor? Construction Enterprise Management, 5(5), 46-47 (in Chinese). http://doi.org/10.3969/j.issn.1001-9251.2005.05.024
Zhang, W., Zhang, X., Luo, X. W., & Zhao, T. S. (2019). Reliability model and critical factors identification of construction safety management based on system thinking. Journal of Civil Engineering and Management, 25(4), 362-379. https://doi.org/10.3846/jcem.2019.8652
Zhao, X., Guo, H. T., Huang, C. L., & Zhong, J. S. (2017). Teaching evaluation system research based on structure entropy weight method. Journal of Discrete Mathematical Sciences & Cryptography, 20(1), 179-191. https://doi.org/10.1080/09720529.2016.1178915
Zheng, X., Shao, B., Chen, L., Chen, S., & Ge, S. (2014). Safety entropy evaluation of hydropower construction based on Euclid theory. China Safety Science Journal, 24(6), 38-43 (in Chinese). http://doi.org/10.16265/j.cnki.issn1003-3033.2014.06.013
Zheng, X., Zhou, J., Wang, F., & Chen, Y. (2018). Routes to failure and prevention recommendations in work systems of hydropower construction. Journal of Civil Engineering and Management, 24(3), 206-222. https://doi.org/10.3846/jcem.2018.1647
Zhou, Z. P., Goh, Y. M., & Li, Q. M. (2015). Overview and analysis of safety management studies in the construction industry. Safety Science, 72, 337-350. https://doi.org/10.1016/j.ssci.2014.10.006