Application of machine learning technology for occupational accident severity prediction in the case of construction collapse accidents

Abstract

Machine learning algorithms are capable of handling complex non-linear problems related to the prediction domain, but further exploration is required for automated, semi-supervised outcome prediction of occupational accidents employing unstructured textual data. It has been demonstrated that the injury severity can be predicted from the equipment, scenario and environmental attributes in the workplace, so this paper aims to enhance text data pre-processing and optimize machine learning algorithms to create an attribute factor-based occupational accident severity prediction framework, mapping characteristic attributes to accident severity categories (i.e., casualties and property damage). The reliability validation of the prediction framework and analysis of critical attribute components are performed using the collapse accidents data in construction engineering as a case study, which is the third most serious occupational problem. The findings indicate that the dataset obtained after addressing the class imbalance issue and improving the text segmentation procedure can be utilized as a training sample to accurately predict injury severity. The accuracy of the prediction model is evaluated in three simulated scenarios, and it can reach 82%, confirming the robust performance of the prediction model based on RF machine learning. Additionally, the outcomes of the measured ranking of feature importance enable the identification of critical attributes that can credibly explain the causal relationships resulting in injury severity findings, and provide managers with accident prevention strategies to minimize occupational injuries and losses.