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

作     者：Chengyu Xie Hoang Nguyen Xuan-Nam Bui Van-Thieu Nguyen Jian Zhou Chengyu Xie;Hoang Nguyen;Xuan-Nam Bui;Van-Thieu Nguyen;Jian Zhou

作者机构：School of Environment and ResourcesXiangtan UniversityXiangtan411105China Mining FacultyHanoi University of Mining and GeologyHanoi100000Viet Nam Innovations for Sustainable and Responsible Mining(ISRM)GroupHanoi University of Mining and GeologyHanoi100000Viet Nam Artificial Intelligence Independent Research GroupHanoi100000Viet Nam School of Resources and Safety EngineeringCentral South UniversityChangsha410083China 

出 版 物：《Journal of Rock Mechanics and Geotechnical Engineering》 (岩石力学与岩土工程学报（英文版）)

年 卷 期：2021年第13卷第6期

页      面：1452-1465页

核心收录：

学科分类：12[管理学] 081901[工学-采矿工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0819[工学-矿业工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by the Natural Science Foundation of Hunan Province,China(Grant No.2021JJ30679) the Center for Mining,Electro-Mechanical Research,Hanoi University of Mining and Geology,Hanoi,Vietnam,for the kind supports 

主　　题：Roof displacement Longwall mining Underground mine Physics-based optimization Risk assessment Mining hazards 

摘      要：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.