Predicting rock displacement in underground mines using improved machine learning-based models

Displacement of rock mass in tunnels and underground mines is considered one of the most hazardous phenomena that can cause the collapse of the structures. In this study, the rock properties, such as the depth of the tunnels (H), the angle of rock layers (alpha), anti-bending moment (Wc), the width of the tunnels (b), the tensile strength of rock layers (Rn), and monitoring distance (Lb), and observation time (t), were investigated to predict rock displacement in tunnels and underground mines. Two novel soft computing models, namely Harris Hawks optimization algorithm (HHOA)-based support vector machine (SVM) model (i.e., HHOA-SVM) and Grasshopper optimization algorithm (GOA)-based SVM model (i.e., GOA-SVM), were developed for this aim based on the field measurements. A total of 12 measurement stations and 63 observations of vertical rock mass displacement, rock properties, and observation time in some underground coal mines in the Donbas region (Ukraine) were compiled as the dataset for developing soft computing models. In addition, a constraint was also added to the proposed HHOA-SVM and GOA-SVM models to prevent the model from offering negative results in predicting rock displacement. The conventional models, such as SVM (without optimization) and artificial neural network (ANN), were also investigated to compare favorably with the two proposed HHOA-SVM and GOA-SVM models. Furthermore, linear and nonlinear equations were also established to predict rock displacement and compared to the soft computing models. The results showed that the novel HHOA-SVM and GOA-SVM models provided better performances than conventional SVM and ANN models. Besides, the sensitivity of the input variables was also analyzed to discover the certain characteristics of the rock displacement phenomenon through the properties of rock and observation time. The findings show that H, Lb, t, and alpha are the most influential parameters for predicting rock displacement in tunnels and underground mines. In contrast, the contribution of b in rock displacement is tiny, and Wc did not relate to the rock displacement in tunnels and underground mines.

Automated summary

The study focused on predicting rock displacement in tunnels and underground mines by analyzing various rock properties and developing two novel soft computing models, which outperformed conventional models. It was found that the depth of tunnels, monitoring distance, observation time, and angle of rock layers were the most influential parameters for predicting rock displacement.