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Enhancing construction safety: Machine learning-based classification of injury types
journal contributionposted on 2023-03-10, 00:27 authored by M Alkaissy, M Arashpour, EM Golafshani, M. Reza HosseiniM. Reza Hosseini, S Khanmohammadi, Y Bai, H Feng
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.
LocationAmsterdam, The Netherlands
Publication classificationC1 Refereed article in a scholarly journal
artificial intelligenceclassification and regressionconstruction managementleading and lagging indicatorsmachine learning algorithmsmodeling and simulationneural networksquantitative analysisrisk analysissafety engineeringPhysical Injury - Accidents and Adverse EffectsPatient SafetyInjuries and accidents3 Good Health and Well BeingEngineeringMedical and Health SciencesPsychology and Cognitive Sciences