Enhancing construction safety management through multivariable grey model analysis and variable selection optimization
DOI: https://doi.org/10.3846/jcem.2025.24348Abstract
In this study, a multivariable grey model (GM(1, N)) is employed to explore how different combinations of variables impact the accuracy of construction accident prediction, using a full permutation algorithm. The aim is to optimize variable selection and improve prediction accuracy. By conducting an exhaustive analysis of 511 potential combinations involving nine variables, it was observed that by integrating crucial external variables such as macroeconomic indicators and industry scale, the multivariable model achieved a prediction accuracy error rate of less than 0.5%, thereby significantly enhancing its information capture and forecasting precision. The analysis suggests that optimal predictive performance is achieved when the number of control variables is approximately four. Additionally, further research shows that increasing the dataset size significantly enhances the model’s predictive capability. This study highlights the scientific rigor and precision of decisionmaking in preventing construction accidents and provides empirical evidence for construction safety management. The research in this paper not only enriches the connotation of the grey system prediction model theoretically, but also provides a data-driven decision support tool for urban construction and safety accident prevention in practice.
Keywords:
multivariable grey model, construction safety management, variable selection optimization, prediction accuracy, data size effectHow to Cite
Share
License
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Alkaissy, M., Arashpour, M., Ashuri, B., Bai, Y., & Hosseini, R. (2020). Safety management in construction: 20 years of risk modeling. Safety Science, 129, Article 104796. https://doi.org/10.1016/j.ssci.2020.104796
Bing, W. (2022). Using an evidence-based safety approach to develop China’s urban safety strategies for the improvement of urban safety: From an accident prevention perspective. Process Safety and Environmental Protection, 163, 336–346. https://doi.org/10.1016/j.psep.2022.05.018
Chang, T.-C., Chang, H. T., & You, M.-L. (1999). Inverse approach to find an optimum α for grey prediction model. In IEEE SMC’99 Conference Proceedings (pp. 971–976). IEEE. https://doi.org/10.1109/ICSMC.1999.814159
Chen, C.-K., & Tien, T.-L. (1996). A new transfer function model: The grey dynamic model GDM(2,2,1). International Journal of Systems Science, 27(12), 1371–1379. https://doi.org/10.1080/00207729608929343
Chen, F., Wang, H., Xu, G., Ji, H., Ding, S., & Wei, Y. (2020). Data-driven safety enhancing strategies for risk networks in construction engineering. Reliability Engineering & System Safety, 197, Article 106803. https://doi.org/10.1016/j.ress.2020.106803
Chen, M. C., Chen, F. L., & Zhang, H. (2011). Multi-dimensional grey construction deformation prediction model research. Advanced Materials Research, 287–290, 3116–3119. https://doi.org/10.4028/www.scientific.net/AMR.287-290.3116
Cheng, M., Li, J., Liu, Y., & Liu, B. (2020). Forecasting clean energy consumption in China by 2025: Using improved grey model GM(1, N). Sustainability, 12(2), Article 698. https://doi.org/10.3390/su12020698
Cheng, M., Liu, Y., & Li, J. (2023). A new modeling method of gray GM(1, N) model and its application to predicting China’s clean energy consumption. Communications in Statistics – Simulation and Computation, 52(8), 3712–3723. https://doi.org/10.1080/03610918.2021.1944641
Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System, 1(1), 1–24.
Du, X., Wu, D., & Yan, Y. (2023). Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China. Energy, 262, Article 125439. https://doi.org/10.1016/j.energy.2022.125439
Elmessery, W. M., Habib, A., Shams, M. Y., El-Hafeez, T. A., El-Messery, T. M., Elsayed, S., Fodah, A. E. M., Abdelwahab, T. A. M., Ali, K. A. M., Osman, Y. K. O. T., Abdelshafie, M. F., Abd El-wahhab G. G., & Elwakeel, A. E. (2024). Deep regression analysis for enhanced thermal control in photovoltaic energy systems. Scientific Reports, 14(1), Article 30600. https://doi.org/10.1038/s41598-024-81101-x
Farghaly, H. M., Ali, A. A., & Abd El-Hafeez, T. (2020a). Building an effective and accurate associative classifier based on support vector machine. Sylwan, 164(3), 39–56.
Farghaly, H. M., Ali, A. A., & El-Hafeez, T. A. (2020b). Developing an efficient method for automatic threshold detection based on hybrid feature selection approach. In R. Silhavy (Ed.), Advances in intelligent systems and computing: Vol. 1225. Artificial intelligence and bioinspired computational methods (pp. 56–72). Springer, Cham. https://doi.org/10.1007/978-3-030-51971-1_5
Fatemeh, M., & Vedat, T. (2023). A data-driven recommendation system for construction safety risk assessment. Journal of Construction Engineering and Management, 149(12), Article 04023157. https://doi.org/10.1061/JCEMD4.COENG-13437
Fonseca, E. D., Lima, F. P. A., & Duarte, F. (2014). From construction site to design: The different accident prevention levels in the building industry. Safety Science, 70, 406–418. https://doi.org/10.1016/j.ssci.2014.07.006
Gregersen, N. P., Nyberg, A., & Berg, H.-Y. (2003). Accident involvement among learner drivers—An analysis of the consequences of supervised practice. Accident Analysis & Prevention, 35(5), 725–730. https://doi.org/10.1016/S0001-4575(02)00051-9
Hsiao, S.-W., & Liu, M. C. (2002). A morphing method for shape generation and image prediction in product design. Design Studies, 23(6), 533–556. https://doi.org/10.1016/S0142-694X(01)00028-X
Hsu, L.-C. (2003). Applying the Grey prediction model to the global integrated circuit industry. Technological Forecasting and Social Change, 70(6), 563–574. https://doi.org/10.1016/S0040-1625(02)00195-6
Hsu, C., & Wen, Y. (1998). Improved grey prediction models for the trans‐pacific air passenger market. Transportation Planning and Technology, 22(2), 87–107. https://doi.org/10.1080/03081069808717622
Ikpe, E., Hammon, F., & Oloke, D. (2012). Cost-benefit analysis for accident prevention in construction projects. Journal of Construction Engineering and Management, 138(8), 991–998. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000496
Jianhong, S., Shupeng, L., & Jing, Z. (2024). Using text mining and Bayesian network to identify key risk factors for safety accidents in metro construction. Journal of Construction Engineering and Management, 150(6), Article 04024052. https://doi.org/10.1061/JCEMD4.COENG-14114
Lao, T., Chen, X., & Zhu, J. (2021). The optimized multivariate Grey prediction model based on dynamic background value and its application. Complexity, 2021, Article 6663773. https://doi.org/10.1155/2021/6663773
Lei, M., & Wang, G. (2022). Analysis of regional financial risk identification and prediction under CVM-GM(1, N) algorithm. In Proceedings of the 2022 International Conference on Artificial Intelligence and Smart Systems (pp. 213–219). Atlantis Press. https://doi.org/10.2991/aebmr.k.220502.039
Li, Y., & Li, M. (2015). Prediction research of death number in construction accident based on unbiased Grey-Fuzzy-Markov chain method. In Proceedings of the 3rd International Conference on Advances in Energy and Environmental Science (pp. 560–566). Atlantis Press. https://doi.org/10.2991/ic3me-15.2015.111
Li, Q., & Zhang, X. (2024). Neural multivariate grey model and its applications. Applied Sciences, 14(3), Article 1219. https://doi.org/10.3390/app14031219
Li, X., Li, N., Ding, S., Cao, Y., & Li, Y. (2023). A novel data-driven seasonal multivariable Grey model for seasonal time series forecasting. Information Sciences, 642, Article 119165. https://doi.org/10.1016/j.ins.2023.119165
Lin, C.-T., & Yang, S.-Y. (2003). Forecast of the output value of Taiwan’s opto-electronics industry using the Grey forecasting model. Technological Forecasting and Social Change, 70(2), 177–186. https://doi.org/10.1016/S0040-1625(01)00191-3
Mao, M., & Chirwa, E. C. (2006). Application of Grey model GM(1,1) to vehicle fatality risk estimation. Technological Forecasting and Social Change, 73(5), 588–605. https://doi.org/10.1016/j.techfore.2004.08.004
Ministry of Housing and Urban-Rural Development of the People’s Republic of China. (2022). Announcement on the production safety accidents of housing and municipal engineering in 2020. https://m.lubanlebiao.com/newsinfo/60645.html
Nini, X., Qiuhao, X., G. M. A., Gui, Y., & Jingfeng, Y. (2020). Antecedents of safety behavior in construction: A literature review and an integrated conceptual framework. Accident Analysis and Prevention, 148, Article 105834. https://doi.org/10.1016/j.aap.2020.105834
Penghui, L., Limao, Z., & T. R. L. K. (2023). Multi-objective robust optimization for enhanced safety in large-diameter tunnel construction with interactive and explainable AI. Reliability Engineering and System Safety, 234, Article 109138. https://doi.org/10.1016/j.ress.2023.109172
Pinto, A., Nunes, I. L., & Ribeiro, R. A. (2011). Occupational risk assessment in construction industry – Overview and reflection. Safety Science, 49(5), 616–624. https://doi.org/10.1016/j.ssci.2011.01.003
Shanshan, Z., & Hazem, E. (2022). A hybrid Grey system theory–based subcontractor selection model for high-stakes construction projects. Journal of Construction Engineering and Management, 148(6), Article 04022055. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002302
Simard, M., & Marchand, A. (1994). The behaviour of first-line supervisors in accident prevention and effectiveness in occupational safety. Safety Science, 17(3), 169–185. https://doi.org/10.1016/0925-7535(94)90010-8
Song, S. (1992). The application of Grey system theory to earthquake prediction in Jiangsu area. The Journal of Grey System, 4, 359–367.
Song, L., He, X., & Li, C. (2011). Longitudinal relationship between economic development and occupational accidents in China. Accident Analysis & Prevention, 43(1), 82–86. https://doi.org/10.1016/j.aap.2010.07.014
Sun, L., & Liu, G. (2011). Apply Gray-Markov Model to predict construction disaster death toll. In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC) (pp. 4969–4972). IEEE. https://doi.org/10.1109/AIMSEC.2011.6011412
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
Tien, T.-L. (2012). A research on the grey prediction model GM(1, n). Applied Mathematics and Computation, 218(9), 4903–4916. https://doi.org/10.1016/j.amc.2011.10.055
Toole, T. M., & Gambatese, J. (2008). The trajectories of prevention through design in construction. Journal of Safety Research, 39(2), 225–230. https://doi.org/10.1016/j.jsr.2008.02.026
Trivedi, H. V., & Singh, J. K. (2005). Application of grey system theory in the development of a runoff prediction model. Biosystems Engineering, 92(4), 521–526. https://doi.org/10.1016/j.biosystemseng.2005.09.005
Wang, H., & Wang, L. (2020). Logistics forecast of Malacca Strait Port using Grey GM (1, N) model. Journal of Coastal Research, 103(Supplement 1), 634–638. https://doi.org/10.2112/SI103-129.1
Wang, Q., & Song, X. (2019). Forecasting China’s oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM. Energy, 183, 160–171. https://doi.org/10.1016/j.energy.2019.06.139
Wenli, L., Yixiao, S., Chen, L., Chengqian, L., & Zehao, J. (2023). Development of a non-Gaussian copula Bayesian network for safety assessment of metro tunnel maintenance. Reliability Engineering & System Safety, 238, Article 109316. https://doi.org/10.1016/j.ress.2023.109316
Wilde, G. J. S. (1982). The theory of risk homeostasis: Implications for safety and health. Risk Analysis, 2(4), 209–225. https://doi.org/10.1111/j.1539-6924.1982.tb01384.x
Wu, W.-Y., & Chen, S.-P. (2005). A prediction method using the grey model GMC(1,n) combined with the grey relational analysis: A case study on Internet access population forecast. Applied Mathematics and Computation, 169(1), 198–217. https://doi.org/10.1016/j.amc.2004.10.087
Xiong, P., Xiao, L., Liu, Y., Yang, Z., Zhou, Y., & Cao, S. (2021). Forecasting carbon emissions using a multi-variable GM (1,N) model based on linear time-varying parameters. Journal of Intelligent & Fuzzy Systems, 41(6), 6137–6148. https://doi.org/10.3233/JIFS-202711
Ye, J., Li, Y., Meng, F., & Geng, S. (2024). A novel multivariate time-lag discrete grey model based on action time and intensities for predicting the productions in food industry. Expert Systems with Applications, 238, Article 121627. https://doi.org/10.1016/j.eswa.2023.121627
Yi, J., Kim, Y., Kim, K., & Koo, B. (2012). A suggested color scheme for reducing perception-related accidents on construction work sites. Accident Analysis & Prevention, 48, 185–192. https://doi.org/10.1016/j.aap.2011.04.022
Zeng, L. (2018). Analysing the high-tech industry with a multivariable grey forecasting model based on fractional order accumulation. Kybernetes, 48(6), 1158–1174. https://doi.org/10.1108/K-02-2018-0078
Zhou, Z., Goh, Y. M., & Li, Q. (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
Zhu, R., Hu, X., Hou, J., & Li, X. (2021). Application of machine learning techniques for predicting the consequences of construction accidents in China. Process Safety and Environmental Protection, 145, 293–302. https://doi.org/10.1016/j.psep.2020.08.006
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.