Predictive safety risk management in an open-pit gold mine in Peru using Bird's pyramid,Bayesian inference and Markov chains

Abstract

Occupational safety in open-pit mining requires tools capable of anticipating risk changes before accidents occur. This study developed a predictive framework for safety risk management in an open-pit gold mine in Peru by integrating Bird's pyramid, Bayesian inference and Markov chains using daily reports of substandard acts and conditions. Events were classified by severity, risk probabilities were estimated from cause–consequence relationships, a daily risk index was constructed and temporal transitions were modelled through a first-order Markov chain. The results showed a structure dominated by precursor events, with 15,010 substandard acts and conditions, 42 incidents and 4 minor accidents. Bayesian inference indicated that medium risk was predominant, representing 57.56% of observations, while the combination ‘unauthorised spills + environmental impact’ reached a posterior high-risk probability of 1.000. The Markov matrix showed moderate persistence of the high-risk state, with a transition probability of 0.4144. Short-term forecasting identified the low-risk state as the most likely outcome, although the probability of high risk increased progressively. The proposed framework transforms daily preventive reports into probabilistic information to prioritise controls and strengthen preventive risk management.