Abstract
The construction sector is widely recognized as having the most hazardous working environment among the various business sectors, and many research studies have focused on injury prevention strategies for use on construction sites. The risk-based theory emphasizes the analysis of accident causes extracted from accident reports to understand, predict, and prevent the occurrence of construction accidents. The first step in the analysis is to classify the incidents from a massive number of reports into different cause categories, a task which is usually performed on a manual basis by domain experts. The research described in this paper proposes a convolutional bidirectional long short-term memory (C-BiLSTM)-based method to automatically classify construction accident reports. The proposed approach was applied on a dataset of construction accident narratives obtained from the Occupational Safety and Health Administration website, and the results indicate that this model performs better than some of the classic machine learning models commonly used in classification tasks, including support vector machine (SVM), naïve Bayes (NB), and logistic regression (LR). The results of this study can help safety managers to develop risk management strategies.
Highlights
Workplace health and safety is a significant concern in all countries [1] because there are more than 2.78 million deaths caused by occupational accidents every year according to the InternationalLabour Organization [2]
The original data of construction accident narratives were downloaded free of charge from the Occupational Safety and Health Administration (OSHA) website which contains more than 16,000 construction accident reports from 1983 to the present
It is worth noting that compared with the support vector machine (SVM) model, a single Convolutional Neural Network (CNN) does not significantly improve the text classification effect, while the bidirectional long short-term memory (BiLSTM) model improves the accuracy of classification results more
Summary
Workplace health and safety is a significant concern in all countries [1] because there are more than 2.78 million deaths caused by occupational accidents every year according to the InternationalLabour Organization [2]. In the United States, construction accounts for approximately one-sixth of fatal accidents while only employing 7% of the national workforce, and there are four recorded injuries per. Construction accidents usually result in both health/safety issues and financial loss [4], and there has been abundant research motivated by the alarming injury and fatality rates. Research on construction safety is mainly conducted from two perspectives: it is either management-driven or technology-driven [5]. It is assumed that enhanced construction safety management can effectively improve on-site safety performance and reduce the number of accidents. Research from the management perspective usually includes either safety management processes such as safety education and training or focuses on individual/organizational characteristics such as workers’ attitudes towards safety. The effect of traditional strategies for preventing injuries was limited due to their
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