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

引用

Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1452-1465页

作者： Chengyu Xie Hoang Nguyen Xuan-Nam Bui Van-Thieu Nguyen Jian Zhou School of Environment and Resources Xiangtan UniversityXiangtan411105China Mining Faculty Hanoi University of Mining and GeologyHanoi100000Viet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyHanoi100000Viet Nam Artificial Intelligence Independent Research Group Hanoi100000Viet Nam School of Resources and Safety Engineering Central South UniversityChangsha410083China

Due to the rapid industrialization and the development of the economy in each country,the demand for energy is increasing *** coal mines have to pace up the mining operations with large production to meet the energy *** 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 ***,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(***-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 HGSOANFIS ***,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 *** 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 *** 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.

关键词： roof displacement Longwall mining Underground mine Physics-based optimization Risk assessment Mining hazards

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