Artificial Intelligence in Occupational Health and Safety Risk Management of Construction, Mining, and Oil and Gas Sectors: Advances and Prospects
Journal of Engineering Research and Reports · pp. 241–253 · Published 22 May 2024
10.9734/jerr/2024/v26i61177Abstract
Artificial intelligence (AI) has gained much popularity in various sectors and has found applications in multiple areas, including occupational health and safety (OHS) risk management of the high-risk construction, mining, and oil and gas sectors. OHS risk management centers on identifying, assessing and controlling occupational risks systematically to prevent work-related injuries, illnesses and deaths. This review presents the advances in AI applications for OHS risk management in these sectors and synthesizes their barriers for better application prospects. In the construction sector, AI can be employed in building information modeling during the design stage to identify and deal with the hazards of building models. AI can be deployed in construction sites through computer vision, sensor networks, knowledge-based systems, and machine learning to capture real-time site conditions, analyze the videos or pictures captured, and provide feedback to workers for appropriate responses. A similar setup involving the same components is also used for managing the OHS risks of surface or underground mining, particularly for monitoring the environmental conditions, detecting the presence of hazardous gases, and identifying hazards in locations that are remote and difficult to assess. Sensors can be attached to personal protective equipment and watches and the signals transmitted via Bluetooth to permit data collection for analysis and response by AI. In the oil and gas sector, sensors are extensively used to collect process safety data from wells, pipelines, valves, etc. for analytical and predictive Al. Al, especially, machine learning is used to create personalized training for workers based on their learning pace and characteristics. However, the major barriers identified are high cost, lack of support and skilled employees, ethical issues, and the uncertainty of AI.
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