Analysis of stochastic process to model safety risk in construction industry
Abstract
There are many factors leading to construction safety accident. The rule presented under the influence of these factors should be a statistical random rule. To reveal those random rules and study the probability prediction method of construction safety accident, according to stochastic process theory, general stochastic process, Markov process and normal process are respectively used to simulate the risk-accident process in this paper. First, in the general-random-process-based analysis the probability of accidents in a period of time is calculated. Then, the Markov property of the construction safety risk evolution process is illustrated, and the analytical expression of probability density function of first-passage time of Markov-based risk-accident process is derived to calculate the construction safety probability. In the normal-process-based analysis, the construction safety probability formulas in cases of stationary normal risk process and non-stationary normal risk process with zero mean value are derived respectively. Finally, the number of accidents that may occur on construction site in a period is studied macroscopically based on Poisson process, and the probability distribution of time interval between adjacent accidents and the time of the nth accident are calculated respectively. The results provide useful reference for the prediction and management of construction accidents.
Keyword : civil engineering construction, safety accidents, probability prediction, Markov process, Normal process, Poisson process
How to Cite
Zhang, Z., Li, W., & Yang, J. (2021). Analysis of stochastic process to model safety risk in construction industry. Journal of Civil Engineering and Management, 27(2), 87-99. https://doi.org/10.3846/jcem.2021.14108
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Bhatia, K., Khan, F., Patel, H., & Abbassi, R. (2019). Dynamic risk-based inspection methodology. Journal of Loss Prevention in the Process Industries, 62, 103974. https://doi.org/10.1016/j.jlp.2019.103974
Chen, C.-W., Leu, S-S., LiN, C-C., & Fan, C. (2010). Characteristic analysis of occupational accidents at small construction enterprises. Safety Science, 48(6), 698–707. https://doi.org/10.1016/j.ssci.2010.02.001
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Choe, S., & Leite, F. (2020). Transforming inherent safety risk in the construction Industry: A safety risk generation and control model. Safety Science, 124, 104–594. https://doi.org/10.1016/j.ssci.2019.104594
de Lamos, T., Eaton, D., Betts, M., & de Almeida, L. T. (2004). Risk management in the Lusoponte concession-a case study of the two bridges in Lisbon, Portugal. International Journal of Project Management, 22(1), 63–73. https://doi.org/10.1016/S0263-7863(03)00013-9
Ding, L., Ji, J., & Khan, F. (2020). Combining uncertainty reasoning and deterministic modeling for risk analysis of fireinduced domino effects. Safety Science, 129, 104802. https://doi.org/10.1016/j.ssci.2020.104802
Don, M. G., & Khan, F. (2019). Dynamic process fault detection and diagnosis based on a combined approach of hidden Markov and Bayesian network model. Chemical Engineering Science, 201, 82–96. https://doi.org/10.1016/j.ces.2019.01.060
Faber, M. H. (2003). Risk and safety in civil, surveying and environmental engineering. Swiss Federal Institute of Technology.
Forteza, F. J., Sesé, A., & Carretero-Gómez, J. M. (2016). CONSRAT. Construction sites risk assessment tool. Safety Science, 89, 338–354. https://doi.org/10.1016/j.ssci.2016.07.012
Golizadeh, H., Hon, C. K. H., Drogemuller, R., & Hosseini, M. R. (2018). Digital engineering potential in addressing causes of construction accidents. Automation in Construction, 95, 284–295. https://doi.org/10.1016/j.autcon.2018.08.013
Hassan, J., & Khan, F. (2012). Risk-based asset integrity indicators. Journal of Loss Prevention in the Process Industries, 25(3), 544–554. https://doi.org/10.1016/j.jlp.2011.12.011
Hoła, B., & Szóstak, M. (2014). Analysis of the development of accident situations in the construction industry. Procedia Engineering, 91, 429–434. https://doi.org/10.1016/j.proeng.2014.12.088
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
Isaac, S., & Edrei, T. (2016). A statistical model for dynamic safety risk control on construction sites. Automation in Construction, 63, 66–78. https://doi.org/10.1016/j.autcon.2015.12.006
Jamot, D. G. C, & Park, J. Y. (2019). System theory based hazard analysis for construction site safety: A case study from Cameroon. Safety Science, 118, 783–794. https://doi.org/10.1016/j.ssci.2019.06.007
Jin, R., Wang, F., & Liu, D. (2020). Dynamic probabilistic analysis of accidents in construction projects by combining precursor data and expert judgments. Advanced Engineering Informatics, 44, 101062. https://doi.org/10.1016/j.aei.2020.101062
Kang, K., & Ryu, H. (2019). Predicting types of occupational accidents at construction sites in Korea using random forest model. Safety Science, 120, 226–236. https://doi.org/10.1016/j.ssci.2019.06.034
Khakzad, N., Khan, F., & Amyotte, P. (2012). Dynamic risk analysis using bow-tie approach. Reliability Engineering & System Safety, 104, 36–44. https://doi.org/10.1016/j.ress.2012.04.003
Khakzad, N., Khan, F., & Paltrinieri, N. (2014). On the application of near accident data to risk analysis of major accidents. Reliability Engineering & System Safety, 126, 116–125. https://doi.org/10.1016/j.ress.2014.01.015
Lee, Y-C., Shariatfar, M., Rashidi, A., & Lee, H-W. (2020). Evidence-driven sound detection for prenotification and identification of construction safety hazards and accidents. Automation in Construction, 113, 103–127. https://doi.org/10.1016/j.autcon.2020.103127
Li, G., Cao, H., & Li, S. (1993). Structural dynamic reliability theory and application. Earthquake Press.
Li, S., Li, S., Zhang, Q., Xue, Y., Liu, B., Su, M., Wang, Z., & Wang, S. (2010). Predicting geological hazards during tunnel construction. Journal of Rock Mechanics and Geotechnical Engineering, 2(3), 232–242. https://doi.org/10.3724/SP.J.1235.2010.00232
Mwakali, J. A. (2006). A review of causes and remedies of construction related accidents: The Uganda experience. In Proceedings from the International Conference on Advances in Engineering and Technology (pp. 285 –299). https://doi.org/10.1016/B978-008045312-5/50032-7
Nnaji, C., & Karakhan, A. A. (2020). Technologies for safety and health management in construction: Current use, implementation benefits and limitations, and adoption barriers. Journal of Building Engineering, 29, 101–212. https://doi.org/10.1016/j.jobe.2020.101212
Rey, G., Clair, D., Fogli, M., & Bernardin, F. (2011). Reliability analysis of roadway departure risk using stochastic processes. Mechanical Systems and Signal Processing, 25(4), 1377–1392. https://doi.org/10.1016/j.ymssp.2010.11.015
Ross, S. M. (1996). Stochastic processes (2nd ed.). John Wiley & Sons.
Sanni-Anibire, M. O., Mahmoud, A. S., Hassanain, M. A., & Salami, B. A. (2010). A risk assessment approach for enhancing construction safety performance. Safety Science, 121, 15–29. https://doi.org/10.1016/j.ssci.2019.08.044
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
Shohet, I. M., Luzi, M., & Tarshish, M. (2018). Optimal allocation of resources in construction safety: Analytical-empirical model. Safety Science, 104, 231–238. https://doi.org/10.1016/j.ssci.2018.01.005
Stewart, M. G. (2001). Reliability-based assessment of ageing bridges using risk ranking and lifecycle cost decision analyses. Reliability Engineering and System Safety, 74(3), 263–273. https://doi.org/10.1016/S0951-8320(01)00079-5
Tang, C. T., Zhang, Z. H., & He, Z. G. (2012). Research summary of bridge and tunnel construction safety risk evaluation method. Applied Mechanics and Materials, 209–211, 1402–1405. https://doi.org/10.4028/www.scientific.net/AMM.209-211.1402
Williamson, E. B., & Winget, D. G. (2005). Risk management and design of critical bridges for terrorist attacks. Journal of Bridge Engineering, 10(1), 96–106. https://doi.org/10.1061/(ASCE)1084-0702(2005)10:1(96)
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
Winge, S., Albrechtsen, E., & Mostue, B. A. (2019). Causal factors and connections in construction accidents. Safety Science, 112, 130–141. https://doi.org/10.1016/j.ssci.2018.10.015
Yang, H., Chew, D. A. S., Wu, W., Zhou, Z., & Li, Q. (2012). Design and implementation of an identification system in construction site safety for proactive accident prevention. Accident Analysis & Prevention, 48, 193–203. https://doi.org/10.1016/j.aap.2011.06.017
Yang, W.-J., & Zhang, Z. (2011). Structural dynamic reliability study based on first-passage time probability analysis of continuous Markov processes. Engineering Mechanics, 28(7), 124–129.
Yu, H., Khan, F., & Garaniya, V. (2015). Nonlinear Gaussian Belief Network based fault diagnosis for industrial processes. Journal of Process Control, 35, 178–200. https://doi.org/10.1016/j.jprocont.2015.09.004
Zhang, J., Zhang, W., Xu P., & Chen N. (2019a). Applicability of accident analysis methods to Chinese construction accidents. Journal of Safety Research, 68, 187–196. https://doi.org/10.1016/j.jsr.2018.11.006
Zhang, W., Zhu, S, Zhang, X., & Zhang, T. (2020a). Identification of critical causes of construction accidents in China using a model based on system thinking and case analysis. Safety Science, 121, 606–618. https://doi.org/10.1016/j.ssci.2019.04.038
Zhang, Z., Liu, X., Zhang, Y., Zhou, M., & Chen, J. (2020b). Time interval of multiple crossings of the Wiener process and a fixed threshold in engineering. Mechanical Systems and Signal Processing, 135, 106389. https://doi.org/10.1016/j.ymssp.2019.106389
Zhang, Z., Zhou, M., & Fang, M. (2019b). First-passage probability analysis of Wiener process using different methods and its applications in the evaluation of structural durability degradation. European Journal of Environmental and Civil Engineering. https://doi.org/10.1080/19648189.2019.1601134
Zhou, C., & Ding, L. Y. (2017). Safety barrier warning system for underground construction sites using Internet-of-Things technologies. Automation in Construction, 83, 372–389. https://doi.org/10.1016/j.autcon.2017.07.005
Zhou, H., Zhao, Y., Shen, Q., Yang, L., & Cai, H. (2020). Risk assessment and management via multi-source information fusion for undersea tunnel construction. Automation in Construction, 111, 103050. https://doi.org/10.1016/j.autcon.2019.103050
Zhu, Z., Park, M-W., Koch, C., Soltani, M., Hammad, A., & Davari, K. (2016). Predicting movements of onsite workers and mobile equipment for enhancing construction site safety. Automation in Construction, 68, 95–101. https://doi.org/10.1016/j.autcon.2016.04.009
Amin, M., Khan, F., & Imtiaz, S. (2019). Fault detection and pathway analysis using a dynamic Bayesian network. Chemical Engineering Science, 195(23), 777–790. https://doi.org/10.1016/j.ces.2018.10.024
Andolfo, C., & Sadeghpour, F. (2015). A probabilistic accident prediction model for construction sites. Procedia Engineering, 123, 15–23. https://doi.org/10.1016/j.proeng.2015.10.052
Ang, A. H.-S., & Tang, W. H. (2007). Probability concepts in engineering – emphasis on applications to civil and environmental engineering. John Wiley & Sons Ltd.
Arunthavanathan, R., Khan, F., Ahmed, S., Imtiaz, S., & Rusli, R. (2020). Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique. Computers & Chemical Engineering, 134(4), 106697. https://doi.org/10.1016/j.compchemeng.2019.106697
Ayhan, B. U., & Tokdemir, O. B. (2019). Predicting the outcome of construction incidents. Safety Science, 113, 91–104. https://doi.org/10.1016/j.ssci.2018.11.001
Bhatia, K., Khan, F., Patel, H., & Abbassi, R. (2019). Dynamic risk-based inspection methodology. Journal of Loss Prevention in the Process Industries, 62, 103974. https://doi.org/10.1016/j.jlp.2019.103974
Chen, C.-W., Leu, S-S., LiN, C-C., & Fan, C. (2010). Characteristic analysis of occupational accidents at small construction enterprises. Safety Science, 48(6), 698–707. https://doi.org/10.1016/j.ssci.2010.02.001
Chi, S., & Han, S. (2013). Analyses of systems theory for construction accident prevention with specific reference to OSHA accident reports. International Journal of Project Management, 31(7), 1027–1041. https://doi.org/10.1016/j.ijproman.2012.12.004
Choe, S., & Leite, F. (2020). Transforming inherent safety risk in the construction Industry: A safety risk generation and control model. Safety Science, 124, 104–594. https://doi.org/10.1016/j.ssci.2019.104594
de Lamos, T., Eaton, D., Betts, M., & de Almeida, L. T. (2004). Risk management in the Lusoponte concession-a case study of the two bridges in Lisbon, Portugal. International Journal of Project Management, 22(1), 63–73. https://doi.org/10.1016/S0263-7863(03)00013-9
Ding, L., Ji, J., & Khan, F. (2020). Combining uncertainty reasoning and deterministic modeling for risk analysis of fireinduced domino effects. Safety Science, 129, 104802. https://doi.org/10.1016/j.ssci.2020.104802
Don, M. G., & Khan, F. (2019). Dynamic process fault detection and diagnosis based on a combined approach of hidden Markov and Bayesian network model. Chemical Engineering Science, 201, 82–96. https://doi.org/10.1016/j.ces.2019.01.060
Faber, M. H. (2003). Risk and safety in civil, surveying and environmental engineering. Swiss Federal Institute of Technology.
Forteza, F. J., Sesé, A., & Carretero-Gómez, J. M. (2016). CONSRAT. Construction sites risk assessment tool. Safety Science, 89, 338–354. https://doi.org/10.1016/j.ssci.2016.07.012
Golizadeh, H., Hon, C. K. H., Drogemuller, R., & Hosseini, M. R. (2018). Digital engineering potential in addressing causes of construction accidents. Automation in Construction, 95, 284–295. https://doi.org/10.1016/j.autcon.2018.08.013
Hassan, J., & Khan, F. (2012). Risk-based asset integrity indicators. Journal of Loss Prevention in the Process Industries, 25(3), 544–554. https://doi.org/10.1016/j.jlp.2011.12.011
Hoła, B., & Szóstak, M. (2014). Analysis of the development of accident situations in the construction industry. Procedia Engineering, 91, 429–434. https://doi.org/10.1016/j.proeng.2014.12.088
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
Isaac, S., & Edrei, T. (2016). A statistical model for dynamic safety risk control on construction sites. Automation in Construction, 63, 66–78. https://doi.org/10.1016/j.autcon.2015.12.006
Jamot, D. G. C, & Park, J. Y. (2019). System theory based hazard analysis for construction site safety: A case study from Cameroon. Safety Science, 118, 783–794. https://doi.org/10.1016/j.ssci.2019.06.007
Jin, R., Wang, F., & Liu, D. (2020). Dynamic probabilistic analysis of accidents in construction projects by combining precursor data and expert judgments. Advanced Engineering Informatics, 44, 101062. https://doi.org/10.1016/j.aei.2020.101062
Kang, K., & Ryu, H. (2019). Predicting types of occupational accidents at construction sites in Korea using random forest model. Safety Science, 120, 226–236. https://doi.org/10.1016/j.ssci.2019.06.034
Khakzad, N., Khan, F., & Amyotte, P. (2012). Dynamic risk analysis using bow-tie approach. Reliability Engineering & System Safety, 104, 36–44. https://doi.org/10.1016/j.ress.2012.04.003
Khakzad, N., Khan, F., & Paltrinieri, N. (2014). On the application of near accident data to risk analysis of major accidents. Reliability Engineering & System Safety, 126, 116–125. https://doi.org/10.1016/j.ress.2014.01.015
Lee, Y-C., Shariatfar, M., Rashidi, A., & Lee, H-W. (2020). Evidence-driven sound detection for prenotification and identification of construction safety hazards and accidents. Automation in Construction, 113, 103–127. https://doi.org/10.1016/j.autcon.2020.103127
Li, G., Cao, H., & Li, S. (1993). Structural dynamic reliability theory and application. Earthquake Press.
Li, S., Li, S., Zhang, Q., Xue, Y., Liu, B., Su, M., Wang, Z., & Wang, S. (2010). Predicting geological hazards during tunnel construction. Journal of Rock Mechanics and Geotechnical Engineering, 2(3), 232–242. https://doi.org/10.3724/SP.J.1235.2010.00232
Mwakali, J. A. (2006). A review of causes and remedies of construction related accidents: The Uganda experience. In Proceedings from the International Conference on Advances in Engineering and Technology (pp. 285 –299). https://doi.org/10.1016/B978-008045312-5/50032-7
Nnaji, C., & Karakhan, A. A. (2020). Technologies for safety and health management in construction: Current use, implementation benefits and limitations, and adoption barriers. Journal of Building Engineering, 29, 101–212. https://doi.org/10.1016/j.jobe.2020.101212
Rey, G., Clair, D., Fogli, M., & Bernardin, F. (2011). Reliability analysis of roadway departure risk using stochastic processes. Mechanical Systems and Signal Processing, 25(4), 1377–1392. https://doi.org/10.1016/j.ymssp.2010.11.015
Ross, S. M. (1996). Stochastic processes (2nd ed.). John Wiley & Sons.
Sanni-Anibire, M. O., Mahmoud, A. S., Hassanain, M. A., & Salami, B. A. (2010). A risk assessment approach for enhancing construction safety performance. Safety Science, 121, 15–29. https://doi.org/10.1016/j.ssci.2019.08.044
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
Shohet, I. M., Luzi, M., & Tarshish, M. (2018). Optimal allocation of resources in construction safety: Analytical-empirical model. Safety Science, 104, 231–238. https://doi.org/10.1016/j.ssci.2018.01.005
Stewart, M. G. (2001). Reliability-based assessment of ageing bridges using risk ranking and lifecycle cost decision analyses. Reliability Engineering and System Safety, 74(3), 263–273. https://doi.org/10.1016/S0951-8320(01)00079-5
Tang, C. T., Zhang, Z. H., & He, Z. G. (2012). Research summary of bridge and tunnel construction safety risk evaluation method. Applied Mechanics and Materials, 209–211, 1402–1405. https://doi.org/10.4028/www.scientific.net/AMM.209-211.1402
Williamson, E. B., & Winget, D. G. (2005). Risk management and design of critical bridges for terrorist attacks. Journal of Bridge Engineering, 10(1), 96–106. https://doi.org/10.1061/(ASCE)1084-0702(2005)10:1(96)
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
Winge, S., Albrechtsen, E., & Mostue, B. A. (2019). Causal factors and connections in construction accidents. Safety Science, 112, 130–141. https://doi.org/10.1016/j.ssci.2018.10.015
Yang, H., Chew, D. A. S., Wu, W., Zhou, Z., & Li, Q. (2012). Design and implementation of an identification system in construction site safety for proactive accident prevention. Accident Analysis & Prevention, 48, 193–203. https://doi.org/10.1016/j.aap.2011.06.017
Yang, W.-J., & Zhang, Z. (2011). Structural dynamic reliability study based on first-passage time probability analysis of continuous Markov processes. Engineering Mechanics, 28(7), 124–129.
Yu, H., Khan, F., & Garaniya, V. (2015). Nonlinear Gaussian Belief Network based fault diagnosis for industrial processes. Journal of Process Control, 35, 178–200. https://doi.org/10.1016/j.jprocont.2015.09.004
Zhang, J., Zhang, W., Xu P., & Chen N. (2019a). Applicability of accident analysis methods to Chinese construction accidents. Journal of Safety Research, 68, 187–196. https://doi.org/10.1016/j.jsr.2018.11.006
Zhang, W., Zhu, S, Zhang, X., & Zhang, T. (2020a). Identification of critical causes of construction accidents in China using a model based on system thinking and case analysis. Safety Science, 121, 606–618. https://doi.org/10.1016/j.ssci.2019.04.038
Zhang, Z., Liu, X., Zhang, Y., Zhou, M., & Chen, J. (2020b). Time interval of multiple crossings of the Wiener process and a fixed threshold in engineering. Mechanical Systems and Signal Processing, 135, 106389. https://doi.org/10.1016/j.ymssp.2019.106389
Zhang, Z., Zhou, M., & Fang, M. (2019b). First-passage probability analysis of Wiener process using different methods and its applications in the evaluation of structural durability degradation. European Journal of Environmental and Civil Engineering. https://doi.org/10.1080/19648189.2019.1601134
Zhou, C., & Ding, L. Y. (2017). Safety barrier warning system for underground construction sites using Internet-of-Things technologies. Automation in Construction, 83, 372–389. https://doi.org/10.1016/j.autcon.2017.07.005
Zhou, H., Zhao, Y., Shen, Q., Yang, L., & Cai, H. (2020). Risk assessment and management via multi-source information fusion for undersea tunnel construction. Automation in Construction, 111, 103050. https://doi.org/10.1016/j.autcon.2019.103050
Zhu, Z., Park, M-W., Koch, C., Soltani, M., Hammad, A., & Davari, K. (2016). Predicting movements of onsite workers and mobile equipment for enhancing construction site safety. Automation in Construction, 68, 95–101. https://doi.org/10.1016/j.autcon.2016.04.009