{"title":"Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate in underground coal mines","authors":"","doi":"10.1007/s40789-023-00630-4","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining. Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations. As a result, a reliable roof fall prediction model is essential to tackle such challenges. Different parameters that substantially impact roof falls are ill-defined and intangible, making this an uncertain and challenging research issue. The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls. Data acquired for 37 mines is limited due to several restrictions, which increased the likelihood of incompleteness. Fuzzy logic is a technique for coping with ambiguity, incompleteness, and uncertainty. Therefore, In this paper, the fuzzy inference method is presented, which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters. The performance of the deployed model is evaluated using statistical measures such as the <em>Root-Mean-Square Error </em>, <em>Mean-Absolute-Error</em>, and <em>coefficient of determination </em>(<span> <span>\\(R_2\\)</span> </span>). Based on these criteria, the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.</p>","PeriodicalId":53469,"journal":{"name":"International Journal of Coal Science & Technology","volume":null,"pages":null},"PeriodicalIF":6.9000,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Coal Science & Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40789-023-00630-4","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining. Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations. As a result, a reliable roof fall prediction model is essential to tackle such challenges. Different parameters that substantially impact roof falls are ill-defined and intangible, making this an uncertain and challenging research issue. The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls. Data acquired for 37 mines is limited due to several restrictions, which increased the likelihood of incompleteness. Fuzzy logic is a technique for coping with ambiguity, incompleteness, and uncertainty. Therefore, In this paper, the fuzzy inference method is presented, which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters. The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error , Mean-Absolute-Error, and coefficient of determination (\(R_2\)). Based on these criteria, the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.
期刊介绍：
The International Journal of Coal Science & Technology is a peer-reviewed open access journal that focuses on key topics of coal scientific research and mining development. It serves as a forum for scientists to present research findings and discuss challenging issues in the field.
The journal covers a range of topics including coal geology, geochemistry, geophysics, mineralogy, and petrology. It also covers coal mining theory, technology, and engineering, as well as coal processing, utilization, and conversion. Additionally, the journal explores coal mining environment and reclamation, along with related aspects.
The International Journal of Coal Science & Technology is published with China Coal Society, who also cover the publication costs. This means that authors do not need to pay an article-processing charge.
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