Predicting bearing capacity of gently inclined bauxite pillar based on numerical simulation and machine learning

Pillar strength is significantly affected by inclination, making accurate prediction of inclined pillar strength crucial for the safety of underground quarries in inclined ore bodies. To address this, a pillar strength prediction model is established by integrating parametric modelling's flexible interactivity, the scalability of numerical simulation sample data and the data-driven advantages of machine learning methods. A parametric modelling program for gently inclined pillar was compiled based on the Grasshopper platform in Rhino, furthermore, the fracture production parameters of bauxite were incorporated into a 200-group Bonded Block Discrete Fracture Network (BBM-DFN) pillar numerical model. A coupled FLAC3D-3DEC simulation method was employed to conduct tests on the bearing characteristics of a gently inclined pillar, based on the rock mass and joint parameters that had been calibrated by the trial-and-error method, monitor and build a machine learning gently inclined pillar strength dataset and verify its reliability. Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Light Gradient Boosting Machine (LightGBM) were used to construct the model for predicting the strength of gently inclined pillars. Additionally, two optimization algorithms, Genetic Programming (GP) and Improved Quantum Particle Swarm Algorithm (IQPSO), were used to enhance model performance and establish a non-linear mapping relationship between the influencing factors and the strength of the gently inclined pillars. The study indicated that the orebodies inclination effect significantly impacts pillar strength. Specifically, pillar strength decreases markedly with increasing inclination for pillars of the same size, with variations depending on the width-to-height ratio. For w/h < 1, the sensitivity order of influencing factors on gently pillar strength was as follows: inclination > height > width. For w/h > 1, the sensitivity order of the influencing factors was as follows: width > inclination > height; SVM is the best model for the gently inclined pillar strength prediction (R2=0.921; REVS=0.926; RMAE=1.225; RMSE=2.367), and the model prediction performance is further improved after combining the optimizations of GP and IQPSO algorithms (R2=0.976; REVS=0.977; RMAE=0.465; RMSE=0.862). The expression for the strength of gently inclined bauxite pillars was obtained by symbolic regression based on GP. The accuracy of the model was tested against the classical theory of pillar strength, extending the idea of predicting the strength of inclined pillars.