Enhancing open stope stability prediction in mining engineering: Optimal configuration of an artificial neural network model

Ensuring the stability of underground excavations is a crucial concern in mining engineering, directly impacting operational safety and efficiency. Given the dynamic nature of the subsurface environment, innovative methods are essential to enhance stability in open stopes and mitigate potential risks. This study focuses on refining predictions of underground excavation stability in mining engineering through the optimization of an artificial neural network (ANN) model. Analyzing Potvin's database, which includes 175 historical cases, we examined the impact of various ANN model configurations. Our findings indicate that normalizing data using Standard Scaler and employing Swish as the activation function across all layers yielded the most accurate predictions for this specific scenario. Additionally, employing the SHAP (Shapley Additive exPlanations) tool enabled us to assess feature importance and identify the most influential factors. Our results underscore the significant impact of the shape factor on underground opening stability, closely followed by the Q value. This research contributes to enhancing predictive models for underground mining excavation stability, emphasizing the critical role of specific parameters affecting open stope stability.