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Study and application of deeply optimized neural network in roof stability evaluation

作     者:Yin, Huiyong Li, Shuo Xu, Guoliang Xie, Daolei Jiang, Cheng Dong, Fangying Wang, Houchen Wu, Bin 

作者机构:Shandong Univ Sci & Technol Coll Earth Sci & Engn Shandong Prov Key Lab Deposit Mineralizat & Sedime Deposit Mineralizat & Sedimentary Minerals Qingdao 266590 Shandong Peoples R China Geophys Prospecting & Surveying Shandong Coalfield Jinan 250104 Shandong Peoples R China Inner Mongolia Datang Int Jungar Min Co Ltd Hohhot 010090 Inner Mongolia Peoples R China Xiezhuang Coal Mine Xinwen Min Grp Co Ltd Xintai 271200 Peoples R China Qinxin Coal Ind Co Ltd Changzhi 046500 Shanxi Peoples R China 

出 版 物:《EARTH SCIENCE INFORMATICS》 (地球科学信息学)

年 卷 期:2024年第17卷第2期

页      面:1729-1744页

核心收录:

学科分类:07[理学] 0708[理学-地球物理学] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

基  金:National Natural Science Foundation of Shandong Province 

主  题:Stability of coal seam roof Genetic algorithm Sparrow search algorithm BP neural network Fuzzy comprehensive evaluation Evaluation and prediction 

摘      要:Deep coal seam mining causes instability and collapse of coal seam roof frequently, which seriously affects the safety production and threatens the personal safety of underground personnel. In order to evaluate the stability of coal roof accurately, this paper select 6th coal seam in Kongduigou Coalfield of Jungar Coalfieldas research object, analyzes the geological and hydrogeological data, and study the lithology, rock combination, sandstone thickness, fault, fold, seam inclination, rock quality index, and rock compressive strength on the influence of the roof stability, drawing the main control factor 3D mapping projection surface maps. Select 58 borehole data points as the input samples (50 training sets and 8 test sets), use genetic algorithm (GA) to optimize the network random initial weights and threshold initial and sparrow search algorithm (SSA) for secondary optimization for the BP neural network training and learning, establishing GA-BP neural network based on SSA optimization (SSA-GA-BP neural network) coal roof stability evaluation model, which is used to predict and evaluate the 6th coal roof stability of the research area after the training error accuracy reached the requirements. The fuzzy comprehensive evaluation method, BP neural network, GA-BP neural network and SSA-GA-BP neural network are also used to predict and evaluate the 6th coal roof stability. Compare the evaluation results of each model with the actual value. The results show that the error of coal seam roof stability evaluation of SSA-GA-BP neural network is smallest, with the accuracy 88%, and the model is successfully applied to predict the roof stability of the 6th coal seam in Kongduigou Coalfield, which provides a scientific evaluation method and theoretical basis for the evaluation of coal seam roof stability.

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