Technical Papers
Dec 22, 2023

Uncovering Critical Causes of Highway Work Zone Accidents Using Unsupervised Machine Learning and Social Network Analysis

Publication: Journal of Construction Engineering and Management
Volume 150, Issue 3

Abstract

Highway work zones are essential for the preservation and improvement of the national road system. Nevertheless, these areas are reported to be among the most hazardous workplaces. Thus, it is crucial to develop appropriate measures to effectively mitigate the safety risks, which require a good understanding of the critical causes of accidents. While there are many previous studies on critical causes of construction accidents, none of them was specifically focused on highway work zones. This type of construction workplace has its own characteristics (e.g., near-passing traffic), which can lead to a unique set of critical causes of accidents. This study used text mining to extract root causes from a large narrative data set of construction accidents at work zones obtained from the Occupational Safety and Health Administration (OSHA). The study applied latent Dirichlet allocation (LDA) modeling on the text corpus to extract 12 root causes, which were subsequently classified into five groups: management, human, unsafe behavior, environmental, and material factors. In addition, social network analysis (SNA) was conducted to gain further insights into the interrelations between the root causes to determine their criticality degree. As a result, four highly ranked causes were identified: supervision dereliction of duty, weak safety awareness, poor construction environment, and risk-taking behavior. The findings of this study offer a new understanding of critical factors that highway agencies and contractors should focus on when developing construction accident prevention strategies at work zones.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

References

Abukhashabah, E., A. Summan, and M. Balkhyour. 2020. “Occupational accidents and injuries in construction industry in Jeddah city.” Saudi J. Biol. Sci. 27 (8): 1993–1998. https://doi.org/10.1016/j.sjbs.2020.06.033.
Alarcón, D. M., I. M. Alarcón, and L. F. Alarcón. 2013. “Social network analysis: A diagnostic tool for information flow in the AEC industry.” In Proc., 21st Annual Conf. of the Int. Group for Lean Construction 2013, 947–956. Fortaleza, Brazil: International Group for Lean Construction.
Aletras, N., and M. Stevenson. 2013. “Evaluating topic coherence using distributional semantics.” In Proc., 10th Int. Conf. Computational Semantics (IWCS 2013), 13–22. Stroudsburg, PA: Association for Computational Linguistics.
Al Hattab, M., and F. Hamzeh. 2015. “Using social network theory and simulation to compare traditional versus BIM-lean practice for design error management.” Autom. Constr. 52 (4): 59–69. https://doi.org/10.1016/j.autcon.2015.02.014.
Aljassmi, H., S. Han, and S. Davis. 2016. “Analysis of the complex mechanisms of defect generation in construction projects.” J. Constr. Eng. Manage. 142 (2): 04015063. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001042.
Arditi, D., D. Lee, and G. Polat. 2007. “Fatal accidents in nighttime vs. daytime highway construction work zones.” J. Saf. Res. 38 (4): 399–405. https://doi.org/10.1016/j.jsr.2007.04.001.
Assaad, R., and I. H. El-adaway. 2020. “Enhancing the knowledge of construction business failure: A social network analysis approach.” J. Constr. Eng. Manage. 146 (6): 04020052. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001831.
Assaad, R., and I. H. El-adaway. 2021. “Determining critical combinations of safety fatality causes using spectral clustering and computational data mining algorithms.” J. Constr. Eng. Manage. 147 (5): 04021035. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002040.
Bonacich, P. 1972. “Factoring and weighting approaches to status scores and clique identification.” J. Math. Sociol. 2 (1): 113–120. https://doi.org/10.1080/0022250X.1972.9989806.
Brin, S., and L. Page. 1998. “The anatomy of a large-scale hypertextual web search engine.” Comput. Networks ISDN Syst. 30 (1): 107–117. https://doi.org/10.1016/S0169-7552(98)00110-X.
Chen, X., Y. Zhang, and X. Hu. 2020. “Analysis of regional freeway traffic safety based on social network.” In Proc., 20th COTA Int. Conf. of Transportation Professionals CICTP 2020, 2610–2621. Reston, VA: ASCE.
Chi, F., C. C. Yang, and Z. L. Chen. 2009. “In-depth accident analysis of electrical fatalities in the construction industry.” Int. J. Ind. Ergon. 39 (4): 635–644. https://doi.org/10.1016/j.ergon.2007.12.003.
Chi, S., S. Han, and D. Y. Kim. 2013. “Relationship between unsafe working conditions and workers’ behavior and impact of working conditions on injury severity in US construction industry.” J. Constr. Eng. Manage. 139 (7): 826–838. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000657.
Chokor, A., H. Naganathan, W. K. Chong, and M. El Asmar. 2016. “Analyzing Arizona OSHA injury reports using unsupervised machine learning.” Procedia Eng. 145 (Jan): 1588–1593. https://doi.org/10.1016/j.proeng.2016.04.200.
Chowdhury, S., and J. Zhu. 2023. “Investigation of critical factors for future-proofed transportation infrastructure planning using topic modeling and association rule mining.” J. Comput. Civ. Eng. 37 (1): 04022044. https://doi.org/10.1061/(ASCE)CP.1943-5487.0001059.
Chung, H. M., O. K. Kwon, O. S. Han, and H.-J. Kim. 2020. “Evolving network characteristics of the Asian international aviation market: A weighted network approach.” Transp. Policy 99 (C): 299–313. https://doi.org/10.1016/j.tranpol.2020.09.002.
Du, J., D. Zhao, R. R. A. Issa, and N. Singh. 2020. “BIM for improved project communication networks: Empirical evidence from email logs.” J. Comput. Civ. Eng. 34 (5): 04020027. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000912.
ELCOSH (Electronic Library of Construction Occupational Safety and Health). 2022. “Roadway Safety Awareness Program.” Accessed November 29, 2022. https://www.elcosh.org/document/102/d000625/Roadway+Safety+Awareness+Program%253A+Trainee++Booklet.html?show_text=1.
El-Rayes, K., and K. Hyari. 2005. “Optimal lighting arrangements for nighttime highway construction projects.” J. Constr. Eng. Manage. 131 (12): 1292–1300. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:12(1292).
Fang, D., Y. Huang, H. Guo, and H. W. Lim. 2020. “LCB approach for construction safety.” Saf. Sci. 128 (Aug): 104761. https://doi.org/10.1016/j.ssci.2020.104761.
FHWA (Federal Highway Administration). 2019. Work zone mobility and safety program. Washington, DC: FHWA.
Golizadeh, H., C. K. H. Hon, R. Drogemuller, and M. R. Hosseini. 2018. “Digital engineering potential in addressing causes of construction accidents.” Autom. Constr. 95 (Sep): 284–295. https://doi.org/10.1016/j.autcon.2018.08.013.
Griffiths, T. L., and M. Steyvers. 2004. “Finding scientific topics.” Proc. Natl. Acad. Sci. U.S.A. 101 (1): 5228–5235. https://doi.org/10.1073/pnas.0307752101.
Hassan, F., T. Le, and X. Lv. 2021. “Addressing legal and contractual matters in construction using natural language processing: A critical review.” J. Constr. Eng. Manage. 147 (9): 03121004. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002122.
Herrera, R. F., C. Mourgues, L. F. Alarcón, and E. Pellicer. 2020. “Understanding interactions between design team members of construction projects using social network analysis.” J. Constr. Eng. Manage. 146 (6): 04020053. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001841.
Hickethier, G., I. D. Tommelein, and B. Lostuvali. 2013. “Social network analysis of information flow in an IPD-project design organization.” In Proc., 21st Annual Conf. of the Int. Group for Lean Construction 2013, 319–328. Fortaleza, Brazil: International Group for Lean Construction.
Hoffman, M., F. Bach, and D. Blei. 2010. “Online learning for latent Dirichlet allocation.” In Proc., 23rd Int. Conf. Neural Information Processing Systems, 856–864. Red Hook, NY: Curran Associates.
Hong, Y., H. Xie, G. Bhumbra, and I. Brilakis. 2021. “Comparing natural language processing methods to cluster construction schedules.” J. Constr. Eng. Manage. 147 (10): 04021136. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002165.
Hoppe, B., and C. Reinelt. 2010. “Social network analysis and the evaluation of leadership networks.” Leadersh. Q. 21 (4): 600–619. https://doi.org/10.1016/j.leaqua.2010.06.004.
Jallan, Y., E. Brogan, B. Ashuri, and C. M. Clevenger. 2019. “Application of natural language processing and text mining to identify patterns in construction-defect litigation cases.” J. Leg. Aff. Dispute Resolut. Eng. Constr. 11 (4): 04519024. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000308.
Jeon, J., S. Padhye, S. Yoon, H. Cai, and M. Hastak. 2021. “Identification of metrics for the Purdue index for construction using latent Dirichlet allocation.” J. Manage. Eng. 37 (6): 04021067. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000968.
Joodaki, M., M. B. Dowlatshahi, and N. Z. Joodaki. 2021. “An ensemble feature selection algorithm based on PageRank centrality and fuzzy logic.” Knowledge-Based Syst. 233 (Dec): 107538. https://doi.org/10.1016/j.knosys.2021.107538.
Jung, N., and G. Lee. 2019. “Automated classification of building information modeling (BIM) case studies by BIM use based on natural language processing (NLP) and unsupervised learning.” Adv. Eng. Inf. 41 (Aug): 100917. https://doi.org/10.1016/j.aei.2019.04.007.
Lee, J. Y., Y. G. Yoon, T. K. Oh, S. Park, and S. Il Ryu. 2020. “A study on data pre-processing and accident prediction modelling for occupational accident analysis in the construction industry.” Appl. Sci. 10 (21): 7949. https://doi.org/10.3390/app10217949.
Li, P., Y. He, and Z. Li. 2022. “Study on influencing factors of construction workers’ unsafe behavior based on text mining.” Front. Psychol. 13 (Apr): 886390. https://doi.org/10.3389/fpsyg.2022.886390.
Liu, Z., J. Zhou, and G. Reniers. 2023. “Association analysis of accident factors in petrochemical storage tank farms.” J. Loss Prev. Process Ind. 84 (Sep): 105124. https://doi.org/10.1016/j.jlp.2023.105124.
Manzoor, B., I. Othman, and M. Manzoor. 2021. “Evaluating the critical safety factors causing accidents in high-rise building projects.” Ain Shams Eng. J. 12 (3): 2485–2492. https://doi.org/10.1016/j.asej.2020.11.025.
Moreno, J. L. 1960. The sociometry reader. Glencoe, IL: The Free Press.
Nnaji, C., A. A. Karakhan, J. Gambatese, and H. W. Lee. 2020. “Case study to evaluate work-zone safety technologies in highway construction.” Pract. Period. Struct. Des. Constr. 25 (3): 05020004. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000498.
NYSDOT (New York State Department of Transportation). 2016. Construction program employee safety manual. Albany, NY: NYSDOT.
OSHA (The Occupational Safety and Health Administration). 2022. “Data & statistics.” Accessed November 19, 2022. https://www.osha.gov/data.
Otte, E., and R. Rousseau. 2002. “Social network analysis: A powerful strategy, also for the information sciences.” J. Inf. Sci. 28 (6): 441–453. https://doi.org/10.1177/016555150202800601.
Patterson, S., and Y. W. Teh. 2013. “Stochastic gradient Riemannian Langevin dynamics on the probability simplex.” In Proc., 26th Int. Conf. Neural Information Processing Systems, 3102–3110. Red Hook, NY: Curran Associates.
Pavlinek, M., and V. Podgorelec. 2017. “Text classification method based on self-training and LDA topic models.” Expert Syst. Appl. 80 (Sep): 83–93. https://doi.org/10.1016/j.eswa.2017.03.020.
Sabeti, S., O. Shoghli, M. Baharani, and H. Tabkhi. 2021. “Toward AI-enabled augmented reality to enhance the safety of highway work zones: Feasibility, requirements, and challenges.” Adv. Eng. Inf. 50 (Oct): 101429. https://doi.org/10.1016/j.aei.2021.101429.
Sievert, C., and K. Shirley. 2014. “LDAvis: A method for visualizing and interpreting topics.” In Proc., Workshop on Interactive Language Learning, Visualization, and Interfaces, 63–70. Cedarville, OH: Association for Computational Linguistics.
Suh, Y. 2021. “Sectoral patterns of accident process for occupational safety using narrative texts of OSHA database.” Saf. Sci. 142 (Oct): 105363. https://doi.org/10.1016/j.ssci.2021.105363.
Tadesse, S., and D. Israel. 2016. “Occupational injuries among building construction workers in Addis Ababa, Ethiopia.” J. Occup. Med. Toxicol. 11 (1): 1–6. https://doi.org/10.1186/s12995-016-0107-8.
Tong, R., H. Zhao, N. Zhang, H. Li, X. Wang, and H. Yang. 2020. “Modified accident causation model for highway construction accidents (ACM-HC).” Eng. Constr. Archit. Manage. 28 (9): 2592–2609. https://doi.org/10.1108/ECAM-07-2020-0530.
Ward, J. H. 1963. “Hierarchical grouping to optimize an objective function.” J. Am. Stat. Assoc. 58 (301): 236–244. https://doi.org/10.1080/01621459.1963.10500845.
Wasserman, S., and K. Faust. 1994. Social network analysis: Methods and applications. Cambridge, UK: Cambridge University Press.
Xu, H., Y. Liu, C.-M. Shu, M. Bai, M. Motalifu, Z. He, S. Wu, P. Zhou, and B. Li. 2022. “Cause analysis of hot work accidents based on text mining and deep learning.” J. Loss Prev. Process Ind. 76 (May): 104747. https://doi.org/10.1016/j.jlp.2022.104747.
Yang, J., et al. 2023a. “Analysis on causes of chemical industry accident from 2015 to 2020 in Chinese mainland: A complex network theory approach.” J. Loss Prev. Process Ind. 83 (Jul): 105061. https://doi.org/10.1016/j.jlp.2023.105061. https://doi.org/10.1016/j.jlp.2022.104747.
Yang, X., Y. Yang, and X. Zheng. 2023b. “Classifying urban functional zones by integrating POIs, Place2vec, and LDA.” J. Urban Plann. Dev. 149 (4): 4023034. https://doi.org/10.1061/JUPDDM.UPENG-4541.
Zeng, J., Z. Liu, and X. Cao. 2012. A new approach to speeding up topic modeling. Ithaca, NY: Cornell Univ.
Zhang, W., S. Zhu, X. Zhang, and T. Zhao. 2020. “Identification of critical causes of construction accidents in China using a model based on system thinking and case analysis.” Saf. Sci. 121 (1): 606–618. https://doi.org/10.1016/j.ssci.2019.04.038.
Zhong, B., X. Pan, P. E. D. Love, J. Sun, and C. Tao. 2020. “Hazard analysis: A deep learning and text mining framework for accident prevention.” Adv. Eng. Inf. 46 (Oct): 101152. https://doi.org/10.1016/j.aei.2020.101152.
Zhou, Z., X. Zhou, and L. Qian. 2021. “Online public opinion analysis on infrastructure megaprojects: Toward an analytical framework.” J. Manage. Eng. 37 (1): 04020105. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000874.

Information & Authors

Information

Published In

Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 150Issue 3March 2024

History

Received: Apr 30, 2023
Accepted: Oct 27, 2023
Published online: Dec 22, 2023
Published in print: Mar 1, 2024
Discussion open until: May 22, 2024

Permissions

Request permissions for this article.

ASCE Technical Topics:

Authors

Affiliations

Ph.D. Student, Glenn Dept. of Civil Engineering, Clemson Univ., Clemson, SC 29634. ORCID: https://orcid.org/0000-0001-6361-2985. Email: [email protected]
Assistant Professor, Glenn Dept. of Civil Engineering, Clemson Univ., Clemson, SC 29634 (corresponding author). ORCID: https://orcid.org/0000-0002-8606-9214. Email: [email protected]
Chau Le, A.M.ASCE [email protected]
Assistant Professor, Dept. of Civil, Construction and Environmental Engineering, North Dakota State Univ., Fargo, ND 58102. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share