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
How to Cite
Shao, B., Hu, Z., Tong, L., Zheng, X., & Liu, D. (2020). Comprehensive assessment model on accident situations of the construction industry in China: a macro-level perspective. Journal of Civil Engineering and Management, 26(1), 14-28. https://doi.org/10.3846/jcem.2019.11662
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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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
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