A prediction model on rockburst intensity grade based on variable weight and matter-element extension
Rockburst is a common dynamic disaster in deep underground engineering. To accurately predict rockburst intensity grade, this study proposes a novel rockburst prediction model based on variable weight and matter-element extension theory. In the proposed model, variable weight theory is used to optim...
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| Veröffentlicht in: | PloS one 2019-06, Vol.14 (6), p.e0218525-e0218525 |
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| creator | Chen, Jianhong Chen, Yi Yang, Shan Zhong, Xudong Han, Xu |
| description | Rockburst is a common dynamic disaster in deep underground engineering. To accurately predict rockburst intensity grade, this study proposes a novel rockburst prediction model based on variable weight and matter-element extension theory. In the proposed model, variable weight theory is used to optimize the weights of prediction indexes. Matter-element extension theory and grade variable method are used to calculate the grade variable interval corresponding to the classification standard of rockburst intensity grade. The rockburst intensity grade of Engineering Rock Mass is predicted by rock burst intensity level variable and the interval. Finally, the model is tested by predicting the rockburst intensity grades of worldwide several projects. The prediction results are compared with the actual rockburst intensity grades and the prediction results of other models. The results indicate that, after using variable weight theory and grade variable method, the correct rate of prediction results of matter-element extension model is improved, and the safety of the prediction results is also enhanced. This study provides a new way to predict rock burst in underground engineering. |
| doi_str_mv | 10.1371/journal.pone.0218525 |
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To accurately predict rockburst intensity grade, this study proposes a novel rockburst prediction model based on variable weight and matter-element extension theory. In the proposed model, variable weight theory is used to optimize the weights of prediction indexes. Matter-element extension theory and grade variable method are used to calculate the grade variable interval corresponding to the classification standard of rockburst intensity grade. The rockburst intensity grade of Engineering Rock Mass is predicted by rock burst intensity level variable and the interval. Finally, the model is tested by predicting the rockburst intensity grades of worldwide several projects. The prediction results are compared with the actual rockburst intensity grades and the prediction results of other models. The results indicate that, after using variable weight theory and grade variable method, the correct rate of prediction results of matter-element extension model is improved, and the safety of the prediction results is also enhanced. This study provides a new way to predict rock burst in underground engineering.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0218525</identifier><identifier>PMID: 31242202</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Biology and Life Sciences ; Compressive Strength ; Computer and Information Sciences ; Discriminant analysis ; Energy ; Engineering ; Engineering and Technology ; Geological Phenomena ; Methods ; Mining ; Model testing ; Models, Theoretical ; Neural networks ; Novels ; People and Places ; Physical Sciences ; Prediction models ; Research and Analysis Methods ; Rock masses ; Rockbursts ; Rocks ; Science Policy ; Soil mechanics ; Weight</subject><ispartof>PloS one, 2019-06, Vol.14 (6), p.e0218525-e0218525</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Chen et al 2019 Chen et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a715t-253bf23e6c5fc633ef38568c469a3414cf612b4c602bd2b6cf5930295d15355e3</citedby><cites>FETCH-LOGICAL-a715t-253bf23e6c5fc633ef38568c469a3414cf612b4c602bd2b6cf5930295d15355e3</cites><orcidid>0000-0002-8249-0183</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6594615/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6594615/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,862,883,2098,2917,23853,27911,27912,53778,53780,79355,79356</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31242202$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Li, Yang</contributor><creatorcontrib>Chen, Jianhong</creatorcontrib><creatorcontrib>Chen, Yi</creatorcontrib><creatorcontrib>Yang, Shan</creatorcontrib><creatorcontrib>Zhong, Xudong</creatorcontrib><creatorcontrib>Han, Xu</creatorcontrib><title>A prediction model on rockburst intensity grade based on variable weight and matter-element extension</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Rockburst is a common dynamic disaster in deep underground engineering. To accurately predict rockburst intensity grade, this study proposes a novel rockburst prediction model based on variable weight and matter-element extension theory. In the proposed model, variable weight theory is used to optimize the weights of prediction indexes. Matter-element extension theory and grade variable method are used to calculate the grade variable interval corresponding to the classification standard of rockburst intensity grade. The rockburst intensity grade of Engineering Rock Mass is predicted by rock burst intensity level variable and the interval. Finally, the model is tested by predicting the rockburst intensity grades of worldwide several projects. The prediction results are compared with the actual rockburst intensity grades and the prediction results of other models. The results indicate that, after using variable weight theory and grade variable method, the correct rate of prediction results of matter-element extension model is improved, and the safety of the prediction results is also enhanced. This study provides a new way to predict rock burst in underground engineering.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Biology and Life Sciences</subject><subject>Compressive Strength</subject><subject>Computer and Information Sciences</subject><subject>Discriminant analysis</subject><subject>Energy</subject><subject>Engineering</subject><subject>Engineering and Technology</subject><subject>Geological Phenomena</subject><subject>Methods</subject><subject>Mining</subject><subject>Model testing</subject><subject>Models, Theoretical</subject><subject>Neural networks</subject><subject>Novels</subject><subject>People and Places</subject><subject>Physical Sciences</subject><subject>Prediction models</subject><subject>Research and Analysis Methods</subject><subject>Rock masses</subject><subject>Rockbursts</subject><subject>Rocks</subject><subject>Science Policy</subject><subject>Soil 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Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Jianhong</au><au>Chen, Yi</au><au>Yang, Shan</au><au>Zhong, Xudong</au><au>Han, Xu</au><au>Li, Yang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A prediction model on rockburst intensity grade based on variable weight and matter-element extension</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-06-26</date><risdate>2019</risdate><volume>14</volume><issue>6</issue><spage>e0218525</spage><epage>e0218525</epage><pages>e0218525-e0218525</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Rockburst is a common dynamic disaster in deep underground engineering. To accurately predict rockburst intensity grade, this study proposes a novel rockburst prediction model based on variable weight and matter-element extension theory. In the proposed model, variable weight theory is used to optimize the weights of prediction indexes. Matter-element extension theory and grade variable method are used to calculate the grade variable interval corresponding to the classification standard of rockburst intensity grade. The rockburst intensity grade of Engineering Rock Mass is predicted by rock burst intensity level variable and the interval. Finally, the model is tested by predicting the rockburst intensity grades of worldwide several projects. The prediction results are compared with the actual rockburst intensity grades and the prediction results of other models. The results indicate that, after using variable weight theory and grade variable method, the correct rate of prediction results of matter-element extension model is improved, and the safety of the prediction results is also enhanced. This study provides a new way to predict rock burst in underground engineering.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31242202</pmid><doi>10.1371/journal.pone.0218525</doi><tpages>e0218525</tpages><orcidid>https://orcid.org/0000-0002-8249-0183</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Biology and Life Sciences Compressive Strength Computer and Information Sciences Discriminant analysis Energy Engineering Engineering and Technology Geological Phenomena Methods Mining Model testing Models, Theoretical Neural networks Novels People and Places Physical Sciences Prediction models Research and Analysis Methods Rock masses Rockbursts Rocks Science Policy Soil mechanics Weight |
| title | A prediction model on rockburst intensity grade based on variable weight and matter-element extension |
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