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<p>Construction site accidents pose serious risks to workers and organizations, necessitating proactive measures to mitigate fatalities and severe injuries. Identifying key contributing factors and developing predictive models are essential. Whereas earlier studies relied on questionnaires or basic statistics and often focused on a single outcome, this study develops machine learning (ML) models that jointly address both the nature of incidents (NOI) and the severity of incidents (SOI). A dataset of 203 incidents from Saudi Arabia (2018–2024), comprising 14 explanatory variables, was used. Six ML algorithms, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) were employed. Results show that all models achieved over 60% accuracy, with XGB performing best, reaching 89% accuracy in SOI prediction. Incorporating NOI as an explanatory feature further improved SOI prediction, highlighting the interdependence of incident characteristics and severity. SHAP analysis provided interpretable insights, revealing that NOI, precipitation, date of incident, and workforce size were the most influential predictors. Factors such as location, safety training, and PPE compliance contributed additional explanatory power. These findings provide practical insights for construction firms, supporting the development of targeted emergency response strategies and the efficient deployment of first-aid resources on site.</p>

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Machine learning applications for predicting safety incidents in construction industry

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月10日

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  • Saleh Alsulamy,
  • Mohamed Alshayeb,
  • Inamullah Inam,
  • Anwar Ahmed

摘要

Construction site accidents pose serious risks to workers and organizations, necessitating proactive measures to mitigate fatalities and severe injuries. Identifying key contributing factors and developing predictive models are essential. Whereas earlier studies relied on questionnaires or basic statistics and often focused on a single outcome, this study develops machine learning (ML) models that jointly address both the nature of incidents (NOI) and the severity of incidents (SOI). A dataset of 203 incidents from Saudi Arabia (2018–2024), comprising 14 explanatory variables, was used. Six ML algorithms, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) were employed. Results show that all models achieved over 60% accuracy, with XGB performing best, reaching 89% accuracy in SOI prediction. Incorporating NOI as an explanatory feature further improved SOI prediction, highlighting the interdependence of incident characteristics and severity. SHAP analysis provided interpretable insights, revealing that NOI, precipitation, date of incident, and workforce size were the most influential predictors. Factors such as location, safety training, and PPE compliance contributed additional explanatory power. These findings provide practical insights for construction firms, supporting the development of targeted emergency response strategies and the efficient deployment of first-aid resources on site.