Research on the Collaborative Safety Optimization of Underground Mine Workings and Surface Roads Based on Machine Learning

Managing surface subsidence caused by underground mining beneath critical infrastructure requires highly efficient and robust optimization models. This study presents a data-driven, multi-objective framework for the collaborative safety optimization of mine stopes and overlying roads, applied to a quartzite mine beneath a secondary highway in Yunnan Province. Based on 88 FLAC3D simulation samples, an XGBoost surrogate model was developed to predict geomechanical responses (Z-displacement and extraction volume), while a Bayesian-optimized Long Short-Term Memory (BO-LSTM) network was employed to forecast ore price signals. To ensure model generalizability and mitigate overfitting in the limited stope dataset, a 5-fold cross-validation (5-fold CV protocol was systematically incorporated into the model training process. Critically, the CV-supported predictive models underwent stress testing under varying training data sizes (40–90%), Gaussian noise intensities (1–13%), and outlier distributions (5–20% proportion; 10–50%amplitude) to define the boundaries of algorithmic reliability. The NSGA-II algorithm was used to map the Pareto-optimal frontier, which was deployed via a Tkinter graphical user interface for dynamic stope geometry adjustments driven by price fluctuations. Secondary FLAC3D validation of the recommended parameters (21.44 m pillar, 1.60 m sidewall) yielded minimal relative errors of 5.07% for displacement and 2.38% for extraction volume. This validated framework demonstrates numerical robustness while balancing geotechnical safety and dynamic market rewards.