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

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Title: Study and application of deeply optimized neural network in roof stability evaluation.
Authors: Yin, Huiyong1 (AUTHOR), Li, Shuo1 (AUTHOR), Xu, Guoliang2 (AUTHOR), Xie, Daolei1 (AUTHOR) 202082030047@sdust.edu.cn, Jiang, Cheng3 (AUTHOR), Dong, Fangying1 (AUTHOR), Wang, Houchen4 (AUTHOR), Wu, Bin5 (AUTHOR)
Source: Earth Science Informatics. Apr2024, Vol. 17 Issue 2, p1729-1744. 16p.
Subject Terms: *Coal mining, *Graphical projection, *Search algorithms, *Scientific method, *Map projection, *Geological research, *Hydrogeology
Abstract: 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. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 176080226
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PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Study and application of deeply optimized neural network in roof stability evaluation.
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  Data: <searchLink fieldCode="AR" term="%22Yin%2C+Huiyong%22">Yin, Huiyong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Shuo%22">Li, Shuo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Guoliang%22">Xu, Guoliang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Daolei%22">Xie, Daolei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 202082030047@sdust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Cheng%22">Jiang, Cheng</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Fangying%22">Dong, Fangying</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Houchen%22">Wang, Houchen</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Bin%22">Wu, Bin</searchLink><relatesTo>5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Earth+Science+Informatics%22">Earth Science Informatics</searchLink>. Apr2024, Vol. 17 Issue 2, p1729-1744. 16p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Coal+mining%22">Coal mining</searchLink><br />*<searchLink fieldCode="DE" term="%22Graphical+projection%22">Graphical projection</searchLink><br />*<searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Scientific+method%22">Scientific method</searchLink><br />*<searchLink fieldCode="DE" term="%22Map+projection%22">Map projection</searchLink><br />*<searchLink fieldCode="DE" term="%22Geological+research%22">Geological research</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrogeology%22">Hydrogeology</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s12145-023-01214-1
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 1729
    Subjects:
      – SubjectFull: Coal mining
        Type: general
      – SubjectFull: Graphical projection
        Type: general
      – SubjectFull: Search algorithms
        Type: general
      – SubjectFull: Scientific method
        Type: general
      – SubjectFull: Map projection
        Type: general
      – SubjectFull: Geological research
        Type: general
      – SubjectFull: Hydrogeology
        Type: general
    Titles:
      – TitleFull: Study and application of deeply optimized neural network in roof stability evaluation.
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            NameFull: Yin, Huiyong
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            NameFull: Li, Shuo
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            NameFull: Xu, Guoliang
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            NameFull: Xie, Daolei
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            NameFull: Jiang, Cheng
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            – D: 01
              M: 04
              Text: Apr2024
              Type: published
              Y: 2024
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              Value: 18650473
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              Value: 17
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            – TitleFull: Earth Science Informatics
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