Machine Learning–Based Decision Support Framework for Construction Injury Severity Prediction and Risk Mitigation
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 3
Abstract
Construction is a key pillar in the global economy, but it is also an industry that has one of the highest fatality rates. The goal of the current study is to employ machine learning in order to develop a framework based on which better-informed and interpretable injury-risk mitigation decisions can be made for construction sites. Central to the framework, generalizable glass-box and black-box models are developed and validated to predict injury severity levels based on the interdependent effects of identified key injury factors. To demonstrate the framework utility, a data set pertaining to construction site injury cases is utilized. By employing the developed models, safety managers can evaluate different construction site safety risk levels, and the potential high-risk zones can be flagged for devising targeted (i.e., site-specific) proactive risk mitigation strategies. Managers can also use the framework to explore complex relationships between interdependent factors and corresponding cause-and-effect of injury severity, which can further enhance their understanding of the underlying mechanisms that shape construction safety risks. Overall, the current study offers transparent, interpretable and generalizable decision-making insights for safety managers and workplace risk practitioners to better identify, understand, predict, and control the factors influencing construction site injuries and ultimately improve the safety level of their working environments by mitigating the risks of associated project disruptions.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors are grateful for the financial support of the Natural Science and Engineering Research Council of Canada (NSERC) CaNRisk-CREATE program, and the INTERFACE Institute and the INViSiONLab, both of McMaster University.
References
Abdel-Rahman, E. M., F. B. Ahmed, and R. Ismail. 2013. “Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data.” Int. J. Remote Sens. 34 (2): 712–728. https://doi.org/10.1080/01431161.2012.713142.
Abubakar, A. M., H. Karadal, S. W. Bayighomog, and E. Merdan. 2018. “Workplace injuries, safety climate and behaviors: Application of an artificial neural network.” Int. J. Occup. Saf. Ergon. 26 (4): 651–661. https://doi.org/10.1080/10803548.2018.1454635.
Aggarwal, C. C. 2015. Data mining: The textbook. New York: Springer.
Akhavian, R., and A. H. Behzadan. 2013. “Knowledge-based simulation modeling of construction fleet operations using multimodal-process data mining.” J. Constr. Eng. Manage. 139 (11): 04013021. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000775.
Alkaissy, M., M. Arashpour, B. Ashuri, Y. Bai, and R. Hosseini. 2020. “Safety management in construction: 20 years of risk modeling.” Saf. Sci. 129 (Sep): 104805. https://doi.org/10.1016/j.ssci.2020.104805.
Altmann, A., L. Toloşi, O. Sander, and T. Lengauer. 2010. “Permutation importance: A corrected feature importance measure.” Bioinformatics 26 (10): 1340–1347. https://doi.org/10.1093/bioinformatics/btq134.
Arlot, S., and A. Celisse. 2010. “A survey of cross-validation procedures for model selection.” Stat. Surv. 4 (Jan): 40–79. https://doi.org/10.1214/09-SS054.
AWCBC (Association of Workers’ Compensation Boards of Canada). 2021. “National work injury, disease and fatality statistics.” Accessed August 4, 2021. https://awcbc.org/en/statistics/.
Ayhan, B. U., and O. B. Tokdemir. 2020. “Accident analysis for construction safety using latent class clustering and artificial neural networks.” J. Constr. Eng. Manage. 146 (3): 04019114. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001762.
BEA (Bureau of Economic Analysis). 2021. “Employment by NAICS industry.” Accessed July 27, 2021. https://www.bea.gov/data/employment/employment-by-industry.
Behm, M. 2005. “Linking construction fatalities to the design for construction safety concept.” Saf. Sci. 43 (8): 589–611. https://doi.org/10.1016/j.ssci.2005.04.002.
Bergstra, J., and Y. Bengio. 2012. “Random search for hyper-parameter optimization.” J. Mach. Learn. Res. 13 (2): 281–305.
BLS (Bureau of Labor Statistics). 2021a. “Census of fatal occupational injuries (CFOI).” Accessed July 27, 2021. https://www.bls.gov/iif/oshcfoi1.htm.
BLS (Bureau of Labor Statistics). 2021b. “Survey of occupational injuries and illnesses data.” Accessed July 27, 2021. https://www.bls.gov/iif/soii-data.htm#dafw.
Breiman, L. 1996. “Bagging predictors.” Mach. Learn. 24 (2): 123–140. https://doi.org/10.1007/BF00058655.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Vol. 432 of Classification and regression trees, 151–166. Belmont, CA: Wadsworth International Group.
Chen, Q., and R. Jin. 2013. “Multilevel safety culture and climate survey for assessing new safety program.” J. Constr. Eng. Manage. 139 (7): 805–817. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000659.
Chi, S., S.-J. Suk, Y. Kang, and S. P. Mulva. 2012. “Development of a data mining-based analysis framework for multi-attribute construction project information.” Adv. Eng. Inf. 26 (3): 574–581. https://doi.org/10.1016/j.aei.2012.03.005.
Chou, J.-S., and C. Lin. 2013. “Predicting disputes in public-private partnership projects: Classification and ensemble models.” J. Comput. Civ. Eng. 27 (1): 51–60. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000197.
Choudhry, R. M., D. Fang, and H. Lingard. 2009. “Measuring safety climate of a construction company.” J. Constr. Eng. Manage. 135 (9): 890–899. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000063.
Cooper, M. D., and R. A. Phillips. 2004. “Exploratory analysis of the safety climate and safety behavior relationship.” J. Saf. Res. 35 (5): 497–512. https://doi.org/10.1016/j.jsr.2004.08.004.
Dedobbeleer, N., and F. Béland. 1991. “A safety climate measure for construction sites.” J. Saf. Res. 22 (2): 97–103. https://doi.org/10.1016/0022-4375(91)90017-P.
Desai, V. S., and S. Joshi. 2010. “Application of decision tree technique to analyze construction project data.” In Proc., Int. Conf. on Information Systems, Technology and Management, 304–313. Berlin: Springer.
Fang, D., Y. Chen, and L. Wong. 2006. “Safety climate in construction industry: A case study in Hong Kong.” J. Constr. Eng. Manage. 132 (6): 573–584. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:6(573).
Feng, D.-C., W.-J. Wang, S. Mangalathu, and E. Taciroglu. 2021. “Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls.” J. Struct. Eng. 147 (11): 04021173. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003115.
Feng, Y., E. A. L. Teo, F. Y. Y. Ling, and S. P. Low. 2014. “Exploring the interactive effects of safety investments, safety culture and project hazard on safety performance: An empirical analysis.” Int. J. Project Manage. 32 (6): 932–943. https://doi.org/10.1016/j.ijproman.2013.10.016.
Fiore, A., G. Quaranta, G. C. Marano, and G. Monti. 2016. “Evolutionary polynomial regression–based statistical determination of the shear capacity equation for reinforced concrete beams without stirrups.” J. Comput. Civ. Eng. 30 (1): 04014111. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000450.
Friedman, J. H. 2001. “Greedy function approximation: A gradient boosting machine.” Ann. Stat. 29 (5): 1189–1232. https://doi.org/10.1214/aos/1013203451.
Gerassis, S., J. E. Martín, J. T. García, A. Saavedra, and J. Taboada. 2017. “Bayesian decision tool for the analysis of occupational accidents in the construction of embankments.” J. Constr. Eng. Manage. 143 (2): 04016093. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001225.
Glendon, A. I., and D. K. Litherland. 2001. “Safety climate factors, group differences and safety behaviour in road construction.” Saf. Sci. 39 (3): 157–188. https://doi.org/10.1016/S0925-7535(01)00006-6.
Goldstein, A., A. Kapelner, J. Bleich, and E. Pitkin. 2015. “Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation.” J. Comput. Graphical Stat. 24 (1): 44–65. https://doi.org/10.1080/10618600.2014.907095.
Gondia, A., M. Ezzeldin, and W. El-Dakhakhni. 2020. “Mechanics-guided genetic programming expression for shear-strength prediction of squat reinforced concrete walls with boundary elements.” J. Struct. Eng. 146 (11): 04020223. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002734.
Gondia, A., M. Ezzeldin, and W. El-Dakhakhni. 2022. “Dynamic networks for resilience-driven management of infrastructure projects.” Auto. Constr. 136: 104149. https://doi.org/10.1016/j.autcon.2022.104149.
Gondia, A., A. Siam, W. El-Dakhakhni, and A. H. Nassar. 2019. “Machine learning algorithms for construction projects delay risk prediction.” J. Constr. Eng. Manage. 146 (1): 04019085. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001736.
Gorissen, D., I. Couckuyt, P. Demeester, T. Dhaene, and K. Crombecq. 2010. “A surrogate modeling and adaptive sampling toolbox for computer based design.” J. Mach. Learn. Res. 11 (68): 2051–2055.
Hallowell, M. R., J. W. Hinze, K. C. Baud, and A. Wehle. 2013. “Proactive construction safety control: Measuring, monitoring, and responding to safety leading indicators.” J. Constr. Eng. Manage. 139 (10): 04013010. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000730.
Han, T., D. Jiang, Q. Zhao, L. Wang, and K. Yin. 2018. “Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery.” Trans. Inst. Meas. Control 40 (8): 2681–2693. https://doi.org/10.1177/0142331217708242.
Hong, H., P. Tsangaratos, I. Ilia, W. Chen, and C. Xu. 2017. “Comparing the performance of a logistic regression and a random forest model in landslide susceptibility assessments. The case of Wuyaun area, China.” In Proc., Workshop on World Landslide Forum, 1043–1050. Cham, Switzerland: Springer.
Hornik, K., C. Buchta, and A. Zeileis. 2009. “Open-source machine learning: R meets Weka.” Comput. Stat. 24 (2): 225–232. https://doi.org/10.1007/s00180-008-0119-7.
Huang, L., C. Wu, B. Wang, and Q. Ouyang. 2018. “Big-data-driven safety decision-making: A conceptual framework and its influencing factors.” Saf. Sci. 109 (Nov): 46–56. https://doi.org/10.1016/j.ssci.2018.05.012.
James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. Vol. 112 of An introduction to statistical learning, 3–7. New York: Springer.
Jin, R., P. X. Zou, P. Piroozfar, H. Wood, Y. Yang, L. Yan, and Y. Han. 2019. “A science mapping approach based review of construction safety research.” Saf. Sci. 113 (Mar): 285–297. https://doi.org/10.1016/j.ssci.2018.12.006.
Kakhki, F. D., S. A. Freeman, and G. A. Mosher. 2019. “Evaluating machine learning performance in predicting injury severity in agribusiness industries.” Saf. Sci. 117 (Aug): 257–262. https://doi.org/10.1016/j.ssci.2019.04.026.
Kang, K., and H. Ryu. 2019. “Predicting types of occupational accidents at construction sites in Korea using random forest model.” Saf. Sci. 120 (Dec): 226–236. https://doi.org/10.1016/j.ssci.2019.06.034.
Kohavi, R. 1995. “A study of cross-validation and bootstrap for accuracy estimation and model selection.” In Vol. 14 of Proc., IJCAI’95: Proc. of the 14th Int. Joint Conf. On Artificial Intelligence, 1137–1145. Menlo Park, CA: American Association for Artificial Intelligence.
Kuhn, M., and K. Johnson. 2013. “Measuring performance in classification models.” In Applied predictive modeling, 247–273. New York: Springer.
Li, Z., P. Liu, W. Wang, and C. Xu. 2012. “Using support vector machine models for crash injury severity analysis.” Accid. Anal. Prev. 45 (Mar): 478–486. https://doi.org/10.1016/j.aap.2011.08.016.
Liu, X., Y. Song, W. Yi, X. Wang, and J. Zhu. 2018. “Comparing the random forest with the generalized additive model to evaluate the impacts of outdoor ambient environmental factors on scaffolding construction productivity.” J. Constr. Eng. Manage. 144 (6): 04018037. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001495.
Lundberg, S. M., G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee. 2020. “From local explanations to global understanding with explainable AI for trees.” Nat. Mach. Intell. 2 (1): 56–67. https://doi.org/10.1038/s42256-019-0138-9.
Lundberg, S. M., and S.-I. Lee. 2017. “A unified approach to interpreting model predictions.” In Proc., 31st Int. Conf. on Neural Information Processing Systems, 4768–4777. Vancouver, BC, Canada: NeurIPS.
Makki, A. A., and I. Mosly. 2021. “Predicting the safety climate in construction sites of Saudi Arabia: A bootstrapped multiple ordinal logistic regression modeling approach.” Appl. Sci. 11 (4): 1474. https://doi.org/10.3390/app11041474.
Marin, L. S., H. Lipscomb, M. Cifuentes, and L. Punnett. 2019. “Perceptions of safety climate across construction personnel: Associations with injury rates.” Saf. Sci. 118 (Oct): 487–496. https://doi.org/10.1016/j.ssci.2019.05.056.
Mattila, M., M. Hyttinen, and E. Rantanen. 1994. “Effective supervisory behaviour and safety at the building site.” Int. J. Ind. Ergon. 13 (2): 85–93. https://doi.org/10.1016/0169-8141(94)90075-2.
McDonald, N., S. Corrigan, C. Daly, and S. Cromie. 2000. “Safety management systems and safety culture in aircraft maintenance organisations.” Saf. Sci. 34 (1–3): 151–176. https://doi.org/10.1016/S0925-7535(00)00011-4.
Mearns, K., S. M. Whitaker, and R. Flin. 2003. “Safety climate, safety management practice and safety performance in offshore environments.” Saf. Sci. 41 (8): 641–680. https://doi.org/10.1016/S0925-7535(02)00011-5.
Mohamed, S. 2002. “Safety climate in construction site environments.” J. Constr. Eng. Manage. 128 (5): 375–384. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:5(375).
Mohammadi, A., M. Tavakolan, and Y. Khosravi. 2018. “Factors influencing safety performance on construction projects: A review.” Saf. Sci. 109 (Nov): 382–397. https://doi.org/10.1016/j.ssci.2018.06.017.
Molnar, C. 2020. “Interpretable machine learning: A guide for making black box models explainable.” Accessed January 15, 2021. https://christophm.github.io/interpretable-ml-book.
Naser, M. Z. 2021. “An engineer’s guide to eXplainable artificial intelligence and interpretable machine learning: Navigating causality, forced goodness, and the false perception of inference.” Autom. Constr. 129 (Sep): 103821. https://doi.org/10.1016/j.autcon.2021.103821.
Nyshadham, C., M. Rupp, B. Bekker, A. V. Shapeev, T. Mueller, C. W. Rosenbrock, G. Csányi, D. W. Wingate, and G. L. Hart. 2019. “Machine-learned multi-system surrogate models for materials prediction.” NPJ Comput. Mater. 5 (1): 1–6. https://doi.org/10.1038/s41524-019-0189-9.
OSHA (Occupational Safety and Health Administration). 2019. “Injuries, illnesses, and fatalities.” Accessed February 17, 2019. https://www.bls.gov/iif/oshoiics.htm.
OSHA (Occupational Safety and Health Administration). 2021. “Recommended practices for safety and health programs.” Accessed April 6, 2021. https://www.osha.gov/safety-management.
Patel, D. A., and K. N. Jha. 2015. “Neural network approach for safety climate prediction.” J. Manage. Eng. 31 (6): 05014027. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000348.
Pereira, E., S. Ahn, S. Han, and S. Abourizk. 2018. “Identification and association of high-priority safety management system factors and accident precursors for proactive safety assessment and control.” J. Manage. Eng. 34 (1): 04017041. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000562.
Pereira, E., S. Ahn, S. Han, and S. Abourizk. 2020. “Finding causal paths between safety management system factors and accident precursors.” J. Manage. Eng. 36 (2): 04019049. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000738.
Poh, C. Q. X., C. U. Ubeynarayana, and Y. M. Goh. 2018. “Safety leading indicators for construction sites: A machine learning approach.” Autom. Constr. 93 (Sep): 375–386. https://doi.org/10.1016/j.autcon.2018.03.022.
Ripley, B. 2018. “Classification and regression trees: R package version 1.0-39.” Accessed August 10, 2018. https://CRAN.R-project.org/package=tree.
Rodriguez-Galiano, V., M. P. Mendes, M. J. Garcia-Soldado, M. Chica-Olmo, and L. Ribeiro. 2014. “Predictive modeling of groundwater nitrate pollution using random forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (southern Spain).” Sci. Total Environ. 476 (Apr): 189–206. https://doi.org/10.1016/j.scitotenv.2014.01.001.
Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An assessment of the effectiveness of a random forest classifier for land-cover classification.” ISPRS J. Photogramm. Remote Sens. 67 (Jan): 93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002.
Sakhakarmi, S., J. Park, and C. Cho. 2019. “Enhanced machine learning classification accuracy for scaffolding safety using increased features.” J. Constr. Eng. Manage. 145 (2): 04018133. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001601.
Seong, H., H. Son, and C. Kim. 2018. “A comparative study of machine learning classification for color-based safety vest detection on construction-site images.” KSCE J. Civ. Eng. 22 (11): 4254–4262. https://doi.org/10.1007/s12205-017-1730-3.
Shannon, C. E. 1948. “A mathematical theory of communication, Part I, Part II.” Bell Syst. Tech. J. 27 (3): 623–656. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x.
Siam, A., M. Ezzeldin, and W. El-Dakhakhni. 2019. “Machine learning algorithms for structural performance classifications and predictions: Application to reinforced masonry shear walls.” Structures 22 (Dec): 252–265. https://doi.org/10.1016/j.istruc.2019.06.017.
Son, H., C. Kim, N. Hwang, C. Kim, and Y. Kang. 2014. “Classification of major construction materials in construction environments using ensemble classifiers.” Adv. Eng. Inf. 28 (1): 1–10. https://doi.org/10.1016/j.aei.2013.10.001.
Statistics Canada. 2021. “Labour force characteristics by industry annual (×1,000).” Accessed August 4, 2021. https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1410002301.
Štrumbelj, E., and I. Kononenko. 2014. “Explaining prediction models and individual predictions with feature contributions.” Knowl. Inf. Syst. 41 (3): 647–665. https://doi.org/10.1007/s10115-013-0679-x.
Sutton, C. D. 2005. “Classification and regression trees, bagging, and boosting.” In Vol. 24 of Handbook of statistics, 303–329.
Therneau, T., and B. Atkinson. 2018. “Recursive partitioning and regression trees: R package version 4.1-13.” Accessed August 10, 2018. https://CRAN.R-project.org/package=rpart.
Tixier, A. J.-P., M. R. Hallowell, B. Rajagopalan, and D. Bowman. 2016. “Application of machine learning to construction injury prediction.” Autom. Constr. 69 (Sep): 102–114. https://doi.org/10.1016/j.autcon.2016.05.016.
Wang, W., X. Jiang, S. Xia, and Q. Cao. 2010. “Incident tree model and incident tree analysis method for quantified risk assessment: An in-depth accident study in traffic operation.” Saf. Sci. 48 (10): 1248–1262. https://doi.org/10.1016/j.ssci.2010.04.002.
Zhang, Y., A. Javanmardi, Y. Liu, S. Yang, X. Yu, S. M. Hsiang, Z. Jiang, and M. Liu. 2020. “How does experience with delay shape managers’ making-do decision: Random forest approach.” J. Manage. Eng. 36 (4): 04020030. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000776.
Zhou, J., X. Li, and H. S. Mitri. 2016. “Classification of rockburst in underground projects: Comparison of ten supervised learning methods.” J. Comput. Civ. Eng. 30 (5): 04016003. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000553.
Zhou, Y., S. Li, C. Zhou, and H. Luo. 2019. “Intelligent approach based on random forest for safety risk prediction of deep foundation pit in subway stations.” J. Comput. Civ. Eng. 33 (1): 05018004. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000796.
Zhou, Y., W. Su, L. Ding, H. Luo, and P. E. Love. 2017. “Predicting safety risks in deep foundation pits in subway infrastructure projects: Support vector machine approach.” J. Comput. Civ. Eng. 31 (5): 04017052. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000700.
Zohar, D. 1980. “Safety climate in industrial organizations: Theoretical and applied implications.” J. Appl. Psychol. 65 (1): 96. https://doi.org/10.1037/0021-9010.65.1.96.
Zohar, D. 2002. “The effects of leadership dimensions, safety climate, and assigned priorities on minor injuries in work groups.” J. Organizational Behav. 23 (1): 75–92. https://doi.org/10.1002/job.130.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8 • Issue 3 • September 2022
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This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.
History
Received: Sep 9, 2021
Accepted: Feb 10, 2022
Published online: May 6, 2022
Published in print: Sep 1, 2022
Discussion open until: Oct 6, 2022
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction equipment
- Construction management
- Construction sites
- Disaster risk management
- Engineering fundamentals
- Equipment and machinery
- Managers
- Mitigation and remediation
- Models (by type)
- Occupational safety
- Optimization models
- Personnel (type)
- Personnel management
- Practice and Profession
- Public administration
- Public health and safety
- Risk management
- Safety
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