Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP)

Abstract

The occurrence of premature rockbolt failure in underground mines has remained one of the most serious challenges facing the industry over the years. Considering the complex mechanism of rockbolts’ failure and the large number of influencing factors, the prediction of rockbolts’ failure from laboratory testing may often be unreliable. It is therefore essential to develop new models capable of predicting rockbolts’ failure with high accuracy. Beyond the predictive accuracy, there is also the need to understand the decisions made by these models in order to convey trust and ensure safety, reliability, and accountability. In this regard, this study proposes an explainable risk assessment of rockbolts’ failure in an underground coal mine using the categorical gradient boosting (Catboost) algorithm and SHapley Additive exPlanations (SHAP). A dataset (including geotechnical and environmental features) from a complex underground mining environment was used. The outcomes of this study indicated that the proposed Catboost algorithm gave an excellent prediction of the risk of rockbolts’ failure. Additionally, the SHAP interpretation revealed that the “length of roadway” was the main contributing factor to rockbolts’ failure. However, conditions influencing rockbolts’ failure varied at different locations in the mine. Overall, this study provides insights into the complex relationship between rockbolts’ failure and the influence of geotechnical and environmental variables. The transparency and explainability of the proposed approach have the potential to facilitate the adoption of explainable machine learning for rockbolt risk assessment in underground mines.