Machine learning-based literature review on the concept of safety barriers against hazardous events

The identification and implementation of effective safety barriers represent the core aim of any safety management process. Safety barriers, whether physical and/or non-physical, serve to prevent or mitigate hazardous events. The interest towards this topic has stimulated a wide spectrum of research activities that is difficult to investigate and summarise by only human analysts. In such a context, data-driven methods could assist the screening and review of the extensive number of contributions on safety barriers. However, to the best of our knowledge, this has not been addressed yet. For this reason, we propose a systematic literature review of the concept of safety barriers by employing an automated unsupervised Machine Learning-based clustering technique. To ensure the effectiveness of the automated clustering, we also performed a manual cleansing. As a result, 769 articles published until 2023 were retrieved from the Scopus database, and were grouped into 21 relevant cluster. Such clusters characterise the main research streams on the safety barriers, including risk assessment approaches, quantitative methods estimating event probabilities, design and management principles, and applications of the barriers in critical and industrial domains. This paper thus advocates for adopting a different perspective to investigate the safety barrier knowledge, by leveraging on the potentialities offered by the scientometric analysis and mapping in the safety science.