Automatic frequency estimation of contributory factors for confined space accidents

Although the dangers of working in confined spaces have been known for many years, fatal accidents related to working in confined spaces still frequently occur. Considerable research has been conducted to identify potential contributory factors of confined space incidents through analyzing accident reports. However, accident databases are usually read and interpreted manually by human experts. The process of analyzing confined space accident reports can be time-consuming and labor-intensive. As the number of accident records increases, it is difficult for the experts to manually review all the reports. Moreover, different individuals may reach various conclusions from the same accident report. Some analysts may fail to capture all the meaningful and relevant causal factors. Automatic information extraction using special rules and ontology-based approaches can be used to mine reports of confined space accidents. However, such approaches tend to suffer from the problem of weak generalization. To overcome this limitation and improve the performance of contributory factors analysis, an improved deep learning based framework is proposed in this paper to automatically extract and classify contributory factors from confined space accident reports using BERT-BiLSTM-CRF and CNN models. Research results suggested that the proposed framework can be used as a feasible method to qualitatively and quantitatively explore the contributory factors of confined space accidents. By analyzing a large quantity of confined space accident reports, the frequency of contributory factors can be estimated automatically. This outcome is helpful to significantly improve the risk assessment quality of confined space works.