Enhancing construction safety: machine learning-based classification of injury types

Maryam Alkaissy, Mehrdad Arashpour, Emadaldin Mohammadi Golafshani, M. Reza Hosseini, Sadegh Khanmohammadi, Yu Bai, Haibo Feng

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)


The construction industry is a hazardous industry with significant injuries and fatalities. Few studies have used data-driven analysis to investigate injuries due to construction accidents. This study aims to deploy machine learning (ML) models to predict four injury types (ITs): Upper limbs, lower limbs, head/neck, and back/trunk. A total of 16,878 construction accident records in Australia were collected and fed into several ML algorithms, including fine trees, ensemble of boosted trees, xgboost, random forest, two types of support vector machines, and logistic regression. Six performance metrics of precision, recall, accuracy, F1 score, the area under the receiver operating curve (AUROC), and the area under precision recall curve (AUPRC) were used to evaluate modeling outputs. Random forest showed superior performance in predicting injury types (accuracy 79.3%; recall 78.0%; F1 score 78.5%; precision 77.1%; AUROC 0.98; and AUPRC 0.78). The critical features of injury types were analyzed using the feature importance method and accident nature and mechanism had significant impacts. The study's findings contribute to safety enhancement by providing quantitative prediction models of injury types and subsequent development of safety controls in construction.

Original languageEnglish
Article number106102
Number of pages12
JournalSafety Science
Publication statusPublished - Jun 2023


  • Artificial intelligence
  • Classification and regression
  • Construction management
  • Leading and lagging indicators
  • Machine learning algorithms
  • Modeling and simulation
  • Neural networks
  • Quantitative analysis
  • Risk analysis
  • Safety engineering

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