Abstract:

To address the challenges in accurately describing uncertainty and multiple failure modes in the risk assessment of embankment slope failures in goaf areas, a risk assessment model based on an improved T-S fuzzy fault tree approach was proposed. Initially, Gaussian fuzzy numbers were introduced to characterize the failure states and occurrence probabilities of basic events, thereby addressing the over-reliance on precise probabilistic data in conventional fault tree analysis and the insufficient representation of intermediate event states. Subsequently, T-S fuzzy model was employed to replace traditional AND/OR relationships in logic gates, capturing the uncertainty and fuzzy characteristics among events, and thereby deriving the failure probability of slope collapse. Finally, an engineering case study was conducted for validation. The results demonstrate that the proposed approach simplifies the fault tree construction, identifies the key risk factors leading to slope failure, provides a ranking of their influence, and reveal the intrinsic relationships between failure events and various factors.

Key words: mining goafs, embankment slopes, collapse, Takagi-Sugeno (T-S) fuzzy fault tree, gaussian fuzzy number, risk assessment