Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

Due to the rapid industrialization and the development of the economy in each country, the demand for energy is increasing rapidly. The coal mines have to pace up the mining operations with large production to meet the energy demand. This requirement has led underground coal mines to go deeper with more difficult conditions, especially the mining hazards, such as large deformations, rockburst, coal burst, roof collapse, to name a few. Therefore, this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method, using various novel intelligent techniques based on physics-based optimization algorithms (i.e. multi-verse optimizer (MVO), equilibrium optimizer (EO), simulated annealing (SA), and Henry gas solubility optimization (HGSO)) and adaptive neuro-fuzzy inference system (ANFIS), named as MVO-ANFIS, EO-ANFIS, SA-ANFIS and HGSO-ANFIS models. Accordingly, 162 roof displacement events were investigated based on the characteristics of surrounding rocks, such as cohesion, Young's modulus, density, shear strength, angle of internal friction, uniaxial compressive strength, quench durability index, rock mass rating, and tensile strength. The MVO-ANFIS, EO-ANFIS, SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground mines. The results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%-92%. Remarkably, the SA-ANFIS model yielded the most dominant accuracy (i.e. 92%). Based on the accurate predictions from the proposed techniques, the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal, especially in the underground coal mines exploited by the longwall mining. (C) 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V.