Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

Hard rock pillar is one of the important structures in engineering design and excavation in underground mines. Accurate and convenient prediction of pillar stability is of great significance for underground space safety. This paper aims to develop hybrid support vector machine (SVM) models improved by three metaheuristic algorithms known as grey wolf optimizer (GWO), whale optimization algorithm (WOA) and sparrow search algorithm (SSA) for predicting the hard rock pillar stability. An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models. Five parameters including pillar height, pillar width, ratio of pillar width to height, uniaxial compressive strength and pillar stress were set as input parameters. Two global indices, three local indices and the receiver operating characteristic (ROC) curve with the area under the ROC curve (AUC) were utilized to evaluate all hybrid models’ performance. The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices. Nevertheless, the performance of the SSA-SVM model for predicting the unstable pillar (AUC: 0.899) is not as good as those for stable (AUC: 0.975) and failed pillars (AUC: 0.990). To verify the effectiveness of the proposed models, 5 field cases were investigated in a metal mine and other 5 cases were collected from several published works. The validation results indicated that the SSA-SVM model obtained a considerable accuracy, which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.