Citation

Gangadhari RK, Rabiee M, Khanzode V, Murthy S, Kumar Tarei P. J. Saf. Res. 2024; 89: 91-104.

Copyright

(Copyright © 2024, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2024.02.006

PMID

38858066

Abstract

INTRODUCTION: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts' subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology.

METHOD: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights.

CONCLUSIONS AND PRACTICAL APPLICATIONS: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Language: en

Keywords

Humans; Risk Factors; Information processing; India; Consensus; Machine Learning; Text mining; Decision Support Techniques; Oil and Gas Industry; Decision Support System; *Accidents, Occupational/prevention & control; *Data Mining/methods; *Risk Management/methods; Best Worst Method; Group Decision Making; Occupational Risk Prioritization