Dynamic Fault Tree–Markov Model for Rockburst Risk Assessment in Phosphate Mining

Deep phosphate mining operations face complex, dynamic working conditions characterized by the superimposed disturbances of high temperature, high stress, and high strain. The occurrence of rockburst disasters demonstrates a clear pattern of dynamic evolution. Traditional rockburst risk assessment methods mostly adopt a static analysis approach, making it difficult to accurately grasp the dynamic characteristics of the entire process of a rockburst from inception and development to occurrence, and also making it hard to meet the practical work requirements of deep phosphate mining safety management. To address this engineering problem, this study constructs a superimposed analysis model for the risk of underground rockburst accidents in deep phosphate drilling based on a dynamic fault tree, and strives to tackle the complex dynamic issues in rockburst risk analysis and prediction. This model retains the technical advantages of traditional fault tree logical reasoning, integrates the time-series analysis function of dynamic fault trees, and organizes and describes various risk factors of deep phosphate rockbursts, as well as the concurrent, selective, and time-overlapping correlations among each factor. Finally, by introducing dynamic logic gates such as priority gates and standby gates, combined with the quantitative representation of rockburst risk stacking effects, it achieves dynamic risk assessment and accurate prediction of rockburst disasters. The model construction strictly follows the core processes of top event definition, hierarchical decomposition of risk factors, and dynamic logic structure construction, and organically integrates risk stacking theory with the dynamic fault tree method, forming an emergency rockburst risk prediction system that can provide technical support for reducing the probability of deep phosphate rockburst accidents. The rock fracture risk superposition model developed in this study aims to provide a tool for risk identification and spatial superposition analysis in deep phosphate mining, minimizing disturbances to the mine’s ecological environment, and offering theoretical support and technical methods for safe and green mining, sustainable development, and high-quality exploitation of deep phosphate resources.