DEM-ML investigation of bauxite pillar strength prediction method

The ultimate loading capacity of the pillar is closely related to the bauxite pillar size parameters, and scientific prediction of pillar strength is the key to safe and efficient mining by the airfield method. In order to accurately and efficiently predict the pillar strength, combining the model parameterisation and high scalability of sample data using Discrete Element Methods (DEM) with the data-driven benefits of Machine Learning (ML) methods, selection of pillar size parameters (length, width and height) as influencing factors, grasshopper parametric modelling battery pack developed, parametric construction of a Bond Block Model (BBM) for pillars with equal block densities achieved, combined with the measured results of the distribution characteristics of the pillar joints, used the 3DEC program to construct 300-group Bond Block Model-Discrete Fracture Network (BBM-DFN) discrete element numerical model of the pillars, carried out tests on the loading characteristics of the pillar, monitor and build a machine learning dataset and verify its reliability; Four algorithms, namely Support Vector Machine (SVM), BP neural network, Random Forest (RF) and Gaussian Process Regression (GPR), were used to construct the pillar strength prediction model. The selection of the best model was carried out based on the regression class model evaluation indicators (R-Square R², Explained Variance Score E_EVS, Mean Absolute Error E_MAE, Mean Squared Error E_MSE), further optimisation of the model in combination with the Improved Quantum Particle Swarm Intelligent Optimisation Algorithm (IQPSO), and use the model to establish a non-linear mapping relationship between the pillar influencing factors and strength. The study shows that: from the parametric simulation results of the pillar strength, it can be seen that the strength increases significantly with the increase of the width to height ratio of the pillar; when the height and cross-sectional area of the pillar are the same, the loading capacity of different cross-sectional pillars is in the following order: square more than rectangular; When the pillar width-to-height ratio is greater than 1, the order of sensitivity of the strength factors affecting the strength of a square pillar is: pillar width (length) more than pillar height; Comprehensive evaluation based on Machine Learning algorithm metrics, the SVM model is the best model for pillar strength prediction (R²=0.953, E_EVS=0.953, E_MAE=0.608, E_MSE=0.551), and the model prediction performance is further improved after combining with the IQPSO algorithm optimization (R²=0.985, E_EVS=0.986, E_MAE=0.373, E_MSE=0.239); The IQPSO-SVM pillar strength prediction values are discussed and analysed with three classic hard rock pillar strength formulae calculation results, and it was concluded that the Hedley’s formula is not applicable for bauxite strength calculations, Krauland’s formula is applicable for aspect ratios less than 4, and Esterhuizen’s formula can be adapted for more accurate strength calculations by adjusting the large discontinuity factor (F) value. The results of the research have provided a solution for predicting hard rock pillar strength and have extended the idea of assessing pillar (group) stability.