Exploring the risk transmission characteristics among unsafe behaviors within urban railway construction accidents
Abstract
Various construction accidents are proven to be caused by multiple unsafe behaviors (e.g., wrong use of PPE), but the risk transmission among different behaviors remains unclear. This paper provides insight into risk transmission through behavioral risk chain that leads to accidents from a system safety perspective. To better understand the coupling mechanism of various unsafe behaviors, integrate different behavioral risk chains and present the risk transmission process, a directed-weighted complex network (DWCN) method was adopted. Historical urban railway construction accidents in China are investigated to extract behavioral risk chain. A DW-BRCNA is applied to integrated behavioral risk chain and the behavioral risk transmission characteristics are explored and clarified by the five network properties, including degree and degree distribution, node strength and node strength distribution, average path length and diameter, weighted clustering coefficient and betweenness centrality. The results show that DW-BRCNA has the characteristics of a small-world, scale-free and hierarchical network, indicating that some unsafe behaviors are of greater importance in the process of risk transmission through behavioral risk chains. In addition, risk transmission in critical behavioral risk chains is more potentially to lead to accidents. This study proposed a new perspective of accident causation analysis from risk transmission among unsafe behaviors. It explains the risk transmission characteristics by a DWCN method based on behavioral risk chains. The findings also provide a practical guidance for developing control strategies on sites to prevent risk transmission and reduce accidents.
Keyword : unsafe behavior, behavioral risk chain, complex network, accident prevention, urban railway construction
How to Cite
Tang, B., Guo, S., Li, J., & Lu, W. (2022). Exploring the risk transmission characteristics among unsafe behaviors within urban railway construction accidents. Journal of Civil Engineering and Management, 28(6), 443–456. https://doi.org/10.3846/jcem.2022.16924
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Guo, S., Zhou, X., Tang, B., & Gong, P. (2020). Exploring the behavioral risk chains of accidents using complex network theory in the construction industry. Physica A: Statistical Mechanics and Its Applications, 560, 125012. https://doi.org/10.1016/j.physa.2020.125012
Health and Safety Executive. (2021). Work-related fatal injuries in Great Britain. Great Britain. https://www.hse.gov.uk/statistics/fatals.htm
Heinrich, H. W., Petersen, D., & Roos, N. (1950). Industrial accident prevention. McGraw-Hill.
Jitwasinkul, B., & Hadikusumo, B. H. (2011). Identification of important organisational factors influencing safety work behaviours in construction projects. Journal of Civil Engineering and Management, 17(4), 520–528. https://doi.org/10.3846/13923730.2011.604538
Kim, N. K., Rahim, N. F. A., Iranmanesh, M., & Foroughi, B. (2019). The role of the safety climate in the successful implementation of safety management systems. Safety Science, 118, 48–56. https://doi.org/10.1016/j.ssci.2019.05.008
Levitin, G., Xing, L., & Luo, L. (2019). Influence of failure propagation on mission abort policy in heterogeneous warm standby systems. Reliability Engineering & System Safety, 183, 29–38. https://doi.org/10.1016/j.ress.2018.11.006
Li, C. Z., Hong, J., Xue, F., Shen, G. Q., Xu, X., & Mok, M. K. (2016). Schedule risks in prefabrication housing production in Hong Kong: a social network analysis. Journal of Cleaner Production, 134, 482–494. https://doi.org/10.1016/j.jclepro.2016.02.123
Liu, J., Schmid, F., Zheng, W., & Zhu, J. (2019). Understanding railway operational accidents using network theory. Reliability Engineering & System Safety, 189, 218–231. https://doi.org/10.1016/j.ress.2019.04.030
Lu, X., & Davis, S. (2016). How sounds influence user safety decisions in a virtual construction simulator. Safety Science, 86, 184–194. https://doi.org/10.1016/j.ssci.2016.02.018
Ministry of Emergency Management of the People’s Republic of China (2018, July 25). The situation of safety production in the construction industry. China. http://www.mem.gov.cn/gk/tzgg/tb/201807/t20180725_230568.shtml
Ministry of Housing and Urban-Rural Development of the People Republic of China. (2016). Technical code for safety of working at height of building construction (No. JGJ 80-2016). Chinese Building & Construction Industry Standard.
Mohammadfam, I., Ghasemi, F., Kalatpour, O., & Moghimbeigi, A. (2017). Constructing a bayesian network model for improving safety behavior of employees at workplaces. Applied Ergonomics, 58, 35–47. https://doi.org/10.1016/j.apergo.2016.05.006
National Standards Bureau. (1986). The classification for casualty accidents of enterprise staff and workers (No. GB 6441-1986). Chinese standard.
National Standards Bureau. (2010). Code for construction and acceptance of crane installation engineering (No. GB 50278-2010). Chinese standard.
National Standards Bureau. (2011). Quality and safety check points of urban rail transit engineering (In Chinese).
Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. https://doi.org/10.1016/j.socnet.2010.03.006
Pagani, G. A., & Aiello, M. (2013). The power grid as a complex network: a survey. Physica A: Statistical Mechanics and Its Applications, 392(11), 2688–2700. https://doi.org/10.1016/j.physa.2013.01.023
Ravasz, E., & Barabási, A.-L. (2003). Hierarchical organization in complex networks. Physical Review E, 67(2), 026112. https://doi.org/10.1103/PhysRevE.67.026112
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Shuang, D., Heng, L., Skitmore, M., & Qin, Y. (2019). An experimental study of intrusion behaviors on construction sites: The role of age and gender. Safety Science, 115, 425–434. https://doi.org/10.1016/j.ssci.2019.02.035
Singh, M. (2020). Underground metro construction, development in India. In P. Ghosh (Ed.), The mind of an engineer: Vol. 2 (pp. 273–277). Springer. https://doi.org/10.1007/978-981-15-1330-5_34
Stewart, J. M. (2001). The turnaround in safety at the Kenora pulp & paper mill. Professional Safety, 46(12), 34–44.
Tang, Y., Wang, G., Li, H., & Cao, D. (2018). Dynamics of collaborative networks between contractors and subcontractors in the construction industry: evidence from national quality award projects in China. Journal of Construction Engineering and Management, 144(9), 05018009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001555
Valipour, A., Yahaya, N., Md Noor, N., 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, J., Mo, H., Wang, F., & Jin, F. (2011). Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach. Journal of Transport Geography, 19(4), 712–721. https://doi.org/10.1016/j.jtrangeo.2010.08.012
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442. https://doi.org/10.1038/30918
Winge, S., & Albrechtsen, E. (2018). Accident types and barrier failures in the construction industry. Safety Science, 105, 158–166. https://doi.org/10.1016/j.ssci.2018.02.006
Wu, X., Li, Y., Yao, Y., Luo, X., He, X., & Yin, W. (2018). Development of construction workers job stress scale to study and the relationship between job stress and safety behavior: An empirical study in Beijing. International Journal of Environmental Research and Public Health, 15(11), 2409. https://doi.org/10.3390/ijerph15112409
Xing, L., & Levitin, G. (2010). Combinatorial analysis of systems with competing failures subject to failure isolation and propagation effects. Reliability Engineering & System Safety, 95(11), 1210–1215. https://doi.org/10.1016/j.ress.2010.06.014
Yang, R. J., & Zou, P. X. (2014). Stakeholder-associated risks and their interactions in complex green building projects: A social network model. Building and Environment, 73, 208–222. https://doi.org/10.1016/j.buildenv.2013.12.014
Yin, R. K. (2017). Case study research and applications: Design and methods (6th ed). Sage Publications.
Yin, W., Fu, G., Yang, C., Jiang, Z., Zhu, K., & Gao, Y. (2017). Fatal gas explosion accidents on Chinese coal mines and the characteristics of unsafe behaviors: 2000–2014. Safety Science, 92, 173–179. https://doi.org/10.1016/j.ssci.2016.09.018
Yu, Q., Ding, L., Zhou, C., & Luo, H. (2014). Analysis of factors influencing safety management for metro construction in China. Accident Analysis & Prevention, 68, 131–138. https://doi.org/10.1016/j.aap.2013.07.016
Yuan, H., He, Y., & Wu, Y. (2019). A comparative study on urban underground space planning system between China and Japan. Sustainable Cities and Society, 48, 101541. https://doi.org/10.1016/j.scs.2019.101541
Zhou, C., Ding, L., Skibniewski, M. J., Luo, H., & Jiang, S. (2017). Characterizing time series of near-miss accidents in metro construction via complex network t heory. Safety Science, 98, 145–158. https://doi.org/10.1016/j.ssci.2017.06.012
Zhou, J., Xu, W., Guo, X., & Ding, J. (2015). A method for modeling and analysis of directed weighted accident causation network (DWACN). Physica A: Statistical Mechanics and its Applications, 437, 263–277. https://doi.org/10.1016/j.physa.2015.05.112
Zhou, Z., & Irizarry, J. (2016). Integrated framework of modified accident energy release model and network theory to explore the full complexity of the Hangzhou subway construction collapse. Journal of Management in Engineering, 32(5), 05016013. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000431
Zhou, Z., Irizarry, J., & Li, Q. (2014). Using network theory to explore the complexity of subway construction accident network (SCAN) for promoting safety management. Safety Science, 64, 127–136. https://doi.org/10.1016/j.ssci.2013.11.029
Amponsah-Tawiah, K., & Mensah, J. (2016). The impact of safety climate on safety related driving behaviors. Transportation Research Part F: Traffic Psychology and Behaviour, 40, 48–55. https://doi.org/10.1016/j.trf.2016.04.002
Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512. https://doi.org/10.1126/science.286.5439.509
Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. https://doi.org/10.1073/pnas.0400087101
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D.-U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4–5), 175–308. https://doi.org/10.1016/j.physrep.2005.10.009
Chan, A. P., Wong, F. K., Hon, C. K., Javed, A. A., & Lyu, S. (2017). Construction safety and health problems of ethnic minority workers in Hong Kong. Engineering, Construction and Architectural Management, 24(6), 901–919. https://doi.org/10.1108/ECAM-09-2015-0143
Chen, H., Zhang, L., & Ran, L. (2021). Vulnerability modeling and assessment in urban transit systems considering disaster chains: A weighted complex network approach. International Journal of Disaster Risk Reduction, 54, 102033. https://doi.org/10.1016/j.ijdrr.2020.102033
Choi, B., & Lee, S. (2018). An empirically based agent-based model of the sociocognitive process of construction workers’ safety behavior. Journal of Construction Engineering and Management, 144(2), 04017102. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001421
Choudhry, R. M., & Fang, D. (2008). Why operatives engage in unsafe work behavior: Investigating factors on construction sites. Safety Science, 46(4), 566–584. https://doi.org/10.1016/j.ssci.2007.06.027
Ding, L., & Xu, J. (2017). A review of metro construction in China: Organization, market, cost, safety and schedule. Frontiers of Engineering Management, 4(1), 4–19. https://doi.org/10.15302/J-FEM-2017015
Dong, H., & Cui, L. (2015). System reliability under cascading failure models. IEEE Transactions on Reliability, 65(2), 929–940. https://doi.org/10.1109/TR.2015.2503751
Dui, H., Meng, X., Xiao, H., & Guo, J. (2020). Analysis of the cascading failure for scale-free networks based on a multi-strategy evolutionary game. Reliability Engineering & System Safety, 199, 106919. https://doi.org/10.1016/j.ress.2020.106919
Eshtehardian, E., & Khodaverdi, S. (2016). A multiply connected belief network approach for schedule risk analysis of metropolitan construction projects. Civil Engineering and Environmental Systems, 33(3), 227–246. https://doi.org/10.1080/10286608.2016.1184492
Fang, W., Ding, L., Love, P. E., Luo, H., Li, H., Pena-Mora, F., Zhong, B., & Zhou, C. (2020). Computer vision applications in construction safety assurance. Automation in Construction, 110, 103013. https://doi.org/10.1016/j.autcon.2019.103013
Fogg, B. J., & Eckles, D. (2007). The behavior chain for online participation: How successful web services structure persuasion. In Y. de Kort, W. IJsselsteijn, C. Midden, B. Eggen, & B. J. Fogg (Eds.), Lecture notes in computer science: Vol. 4744. Persuasive technology (PERSUASIVE 2007). Springer. https://doi.org/10.1007/978-3-540-77006-0_25
Guo, S., Tang, B., Liang, K., Zhou, X., & Li, J. (2021). Comparative analysis of the patterns of unsafe behaviors in accidents between building construction and urban railway construction. Journal of Construction Engineering and Management, 147(5), 04021027. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002013
Guo, S., Xiong, C., & Gong, P. (2018). A real-time control approach based on intelligent video surveillance for violations by construction workers. Journal of Civil Engineering and Management, 24(1), 67–78. https://doi.org/10.3846/jcem.2018.301
Guo, S., Zhou, X., Tang, B., & Gong, P. (2020). Exploring the behavioral risk chains of accidents using complex network theory in the construction industry. Physica A: Statistical Mechanics and Its Applications, 560, 125012. https://doi.org/10.1016/j.physa.2020.125012
Health and Safety Executive. (2021). Work-related fatal injuries in Great Britain. Great Britain. https://www.hse.gov.uk/statistics/fatals.htm
Heinrich, H. W., Petersen, D., & Roos, N. (1950). Industrial accident prevention. McGraw-Hill.
Jitwasinkul, B., & Hadikusumo, B. H. (2011). Identification of important organisational factors influencing safety work behaviours in construction projects. Journal of Civil Engineering and Management, 17(4), 520–528. https://doi.org/10.3846/13923730.2011.604538
Kim, N. K., Rahim, N. F. A., Iranmanesh, M., & Foroughi, B. (2019). The role of the safety climate in the successful implementation of safety management systems. Safety Science, 118, 48–56. https://doi.org/10.1016/j.ssci.2019.05.008
Levitin, G., Xing, L., & Luo, L. (2019). Influence of failure propagation on mission abort policy in heterogeneous warm standby systems. Reliability Engineering & System Safety, 183, 29–38. https://doi.org/10.1016/j.ress.2018.11.006
Li, C. Z., Hong, J., Xue, F., Shen, G. Q., Xu, X., & Mok, M. K. (2016). Schedule risks in prefabrication housing production in Hong Kong: a social network analysis. Journal of Cleaner Production, 134, 482–494. https://doi.org/10.1016/j.jclepro.2016.02.123
Liu, J., Schmid, F., Zheng, W., & Zhu, J. (2019). Understanding railway operational accidents using network theory. Reliability Engineering & System Safety, 189, 218–231. https://doi.org/10.1016/j.ress.2019.04.030
Lu, X., & Davis, S. (2016). How sounds influence user safety decisions in a virtual construction simulator. Safety Science, 86, 184–194. https://doi.org/10.1016/j.ssci.2016.02.018
Ministry of Emergency Management of the People’s Republic of China (2018, July 25). The situation of safety production in the construction industry. China. http://www.mem.gov.cn/gk/tzgg/tb/201807/t20180725_230568.shtml
Ministry of Housing and Urban-Rural Development of the People Republic of China. (2016). Technical code for safety of working at height of building construction (No. JGJ 80-2016). Chinese Building & Construction Industry Standard.
Mohammadfam, I., Ghasemi, F., Kalatpour, O., & Moghimbeigi, A. (2017). Constructing a bayesian network model for improving safety behavior of employees at workplaces. Applied Ergonomics, 58, 35–47. https://doi.org/10.1016/j.apergo.2016.05.006
National Standards Bureau. (1986). The classification for casualty accidents of enterprise staff and workers (No. GB 6441-1986). Chinese standard.
National Standards Bureau. (2010). Code for construction and acceptance of crane installation engineering (No. GB 50278-2010). Chinese standard.
National Standards Bureau. (2011). Quality and safety check points of urban rail transit engineering (In Chinese).
Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. https://doi.org/10.1016/j.socnet.2010.03.006
Pagani, G. A., & Aiello, M. (2013). The power grid as a complex network: a survey. Physica A: Statistical Mechanics and Its Applications, 392(11), 2688–2700. https://doi.org/10.1016/j.physa.2013.01.023
Ravasz, E., & Barabási, A.-L. (2003). Hierarchical organization in complex networks. Physical Review E, 67(2), 026112. https://doi.org/10.1103/PhysRevE.67.026112
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Shuang, D., Heng, L., Skitmore, M., & Qin, Y. (2019). An experimental study of intrusion behaviors on construction sites: The role of age and gender. Safety Science, 115, 425–434. https://doi.org/10.1016/j.ssci.2019.02.035
Singh, M. (2020). Underground metro construction, development in India. In P. Ghosh (Ed.), The mind of an engineer: Vol. 2 (pp. 273–277). Springer. https://doi.org/10.1007/978-981-15-1330-5_34
Stewart, J. M. (2001). The turnaround in safety at the Kenora pulp & paper mill. Professional Safety, 46(12), 34–44.
Tang, Y., Wang, G., Li, H., & Cao, D. (2018). Dynamics of collaborative networks between contractors and subcontractors in the construction industry: evidence from national quality award projects in China. Journal of Construction Engineering and Management, 144(9), 05018009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001555
Valipour, A., Yahaya, N., Md Noor, N., 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, J., Mo, H., Wang, F., & Jin, F. (2011). Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach. Journal of Transport Geography, 19(4), 712–721. https://doi.org/10.1016/j.jtrangeo.2010.08.012
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442. https://doi.org/10.1038/30918
Winge, S., & Albrechtsen, E. (2018). Accident types and barrier failures in the construction industry. Safety Science, 105, 158–166. https://doi.org/10.1016/j.ssci.2018.02.006
Wu, X., Li, Y., Yao, Y., Luo, X., He, X., & Yin, W. (2018). Development of construction workers job stress scale to study and the relationship between job stress and safety behavior: An empirical study in Beijing. International Journal of Environmental Research and Public Health, 15(11), 2409. https://doi.org/10.3390/ijerph15112409
Xing, L., & Levitin, G. (2010). Combinatorial analysis of systems with competing failures subject to failure isolation and propagation effects. Reliability Engineering & System Safety, 95(11), 1210–1215. https://doi.org/10.1016/j.ress.2010.06.014
Yang, R. J., & Zou, P. X. (2014). Stakeholder-associated risks and their interactions in complex green building projects: A social network model. Building and Environment, 73, 208–222. https://doi.org/10.1016/j.buildenv.2013.12.014
Yin, R. K. (2017). Case study research and applications: Design and methods (6th ed). Sage Publications.
Yin, W., Fu, G., Yang, C., Jiang, Z., Zhu, K., & Gao, Y. (2017). Fatal gas explosion accidents on Chinese coal mines and the characteristics of unsafe behaviors: 2000–2014. Safety Science, 92, 173–179. https://doi.org/10.1016/j.ssci.2016.09.018
Yu, Q., Ding, L., Zhou, C., & Luo, H. (2014). Analysis of factors influencing safety management for metro construction in China. Accident Analysis & Prevention, 68, 131–138. https://doi.org/10.1016/j.aap.2013.07.016
Yuan, H., He, Y., & Wu, Y. (2019). A comparative study on urban underground space planning system between China and Japan. Sustainable Cities and Society, 48, 101541. https://doi.org/10.1016/j.scs.2019.101541
Zhou, C., Ding, L., Skibniewski, M. J., Luo, H., & Jiang, S. (2017). Characterizing time series of near-miss accidents in metro construction via complex network t heory. Safety Science, 98, 145–158. https://doi.org/10.1016/j.ssci.2017.06.012
Zhou, J., Xu, W., Guo, X., & Ding, J. (2015). A method for modeling and analysis of directed weighted accident causation network (DWACN). Physica A: Statistical Mechanics and its Applications, 437, 263–277. https://doi.org/10.1016/j.physa.2015.05.112
Zhou, Z., & Irizarry, J. (2016). Integrated framework of modified accident energy release model and network theory to explore the full complexity of the Hangzhou subway construction collapse. Journal of Management in Engineering, 32(5), 05016013. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000431
Zhou, Z., Irizarry, J., & Li, Q. (2014). Using network theory to explore the complexity of subway construction accident network (SCAN) for promoting safety management. Safety Science, 64, 127–136. https://doi.org/10.1016/j.ssci.2013.11.029