Intelligent Autofeedback and Safety Early-Warning for Underground Cavern Engineering during Construction Based on BP Neural Network and FEM
The low efficiency of feedback analysis is one of the main shortcomings in the construction of underground cavern engineering. With this in mind, a method of intelligent autofeedback and safety early-warning for underground cavern is proposed based on BP neural network and FEM. The training sample p...
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| Veröffentlicht in: | Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-8 |
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| container_title | Mathematical problems in engineering |
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| creator | Lei, Xu Ren, Qingwen Zhang, Taijun |
| description | The low efficiency of feedback analysis is one of the main shortcomings in the construction of underground cavern engineering. With this in mind, a method of intelligent autofeedback and safety early-warning for underground cavern is proposed based on BP neural network and FEM. The training sample points are chosen by using uniform test design method, and the autogeneration of FEM calculation file for ABAQUS is realized by using the technique of file partition, information grouping, and orderly numbering. Then, intelligent autoinversion of mechanics parameters is realized, and the automatic connection of parameter inversion, subsequent prediction, and safety early-warning is achieved. The software of intelligent autofeedback and safety early-warning for underground cavern engineering during construction is developed. Finally, the applicability of the proposed method and the developed software is verified through an application example of underground cavern of a pumped-storage power station located in Southwest China. |
| doi_str_mv | 10.1155/2015/873823 |
| format | Article |
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With this in mind, a method of intelligent autofeedback and safety early-warning for underground cavern is proposed based on BP neural network and FEM. The training sample points are chosen by using uniform test design method, and the autogeneration of FEM calculation file for ABAQUS is realized by using the technique of file partition, information grouping, and orderly numbering. Then, intelligent autoinversion of mechanics parameters is realized, and the automatic connection of parameter inversion, subsequent prediction, and safety early-warning is achieved. The software of intelligent autofeedback and safety early-warning for underground cavern engineering during construction is developed. Finally, the applicability of the proposed method and the developed software is verified through an application example of underground cavern of a pumped-storage power station located in Southwest China.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2015/873823</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Artificial intelligence ; Automation ; Back propagation networks ; Caverns ; Civil engineering ; Computer programs ; Construction ; Construction engineering ; Data exchange ; Design techniques ; Efficiency ; Finite element method ; Hydroelectric power ; Mathematical analysis ; Mathematical models ; Mathematical problems ; Neural networks ; Parameters ; Power plants ; Pumped storage ; Safety ; Software ; Software engineering ; Underground caverns ; Underground construction ; Underground storage ; Warning</subject><ispartof>Mathematical problems in engineering, 2015-01, Vol.2015 (2015), p.1-8</ispartof><rights>Copyright © 2015 Lei Xu et al.</rights><rights>Copyright © 2015 Lei Xu et al. Lei Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-34976e629f328894a82a31080cb3904e02ff1efc4905ef1d23a327c70ae1c4933</citedby><cites>FETCH-LOGICAL-c389t-34976e629f328894a82a31080cb3904e02ff1efc4905ef1d23a327c70ae1c4933</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><contributor>Cen, Song</contributor><creatorcontrib>Lei, Xu</creatorcontrib><creatorcontrib>Ren, Qingwen</creatorcontrib><creatorcontrib>Zhang, Taijun</creatorcontrib><title>Intelligent Autofeedback and Safety Early-Warning for Underground Cavern Engineering during Construction Based on BP Neural Network and FEM</title><title>Mathematical problems in engineering</title><description>The low efficiency of feedback analysis is one of the main shortcomings in the construction of underground cavern engineering. With this in mind, a method of intelligent autofeedback and safety early-warning for underground cavern is proposed based on BP neural network and FEM. The training sample points are chosen by using uniform test design method, and the autogeneration of FEM calculation file for ABAQUS is realized by using the technique of file partition, information grouping, and orderly numbering. Then, intelligent autoinversion of mechanics parameters is realized, and the automatic connection of parameter inversion, subsequent prediction, and safety early-warning is achieved. The software of intelligent autofeedback and safety early-warning for underground cavern engineering during construction is developed. Finally, the applicability of the proposed method and the developed software is verified through an application example of underground cavern of a pumped-storage power station located in Southwest China.</description><subject>Artificial intelligence</subject><subject>Automation</subject><subject>Back propagation networks</subject><subject>Caverns</subject><subject>Civil engineering</subject><subject>Computer programs</subject><subject>Construction</subject><subject>Construction engineering</subject><subject>Data exchange</subject><subject>Design techniques</subject><subject>Efficiency</subject><subject>Finite element method</subject><subject>Hydroelectric power</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Mathematical problems</subject><subject>Neural networks</subject><subject>Parameters</subject><subject>Power plants</subject><subject>Pumped storage</subject><subject>Safety</subject><subject>Software</subject><subject>Software engineering</subject><subject>Underground caverns</subject><subject>Underground construction</subject><subject>Underground storage</subject><subject>Warning</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0U1P3DAQBuAItRKU9sS9ssSlKgp4PE5iH-lqoUh8VGpRuUUmGW9Dg01tp2h_Q_90vYRD1Qund2Q9sq15i2IP-CFAVR0JDtWRalAJ3Cp2oKqxrEA2r_LMhSxB4M128SbGO84FVKB2ij9nLtE4DityiR1PyVui_tZ0P5lxPftqLKU1W5owrsvvJrjBrZj1gV27nsIq-CmjhflNwbGlWw2OKGxIPz3FwruYwtSlwTv2yUTq2Wb4wi5pCmbMkR59mJ86WV68LV5bM0Z695y7xfXJ8tvic3l-dXq2OD4vO1Q6lSh1U1MttEWhlJZGCYPAFe9uUXNJXFgLZDupeUUWeoEGRdM13BDkQ8Td4sN870PwvyaKqb0fYpe3YBz5KbbQcOCItRaZ7v9H7_wUXP5dC7WSErUQPKuDWXXBxxjItg9huDdh3QJvN8W0m2LauZisP876x-B68zi8gN_PmDIha_7BjdSyxr9Mv5aE</recordid><startdate>20150101</startdate><enddate>20150101</enddate><creator>Lei, Xu</creator><creator>Ren, Qingwen</creator><creator>Zhang, Taijun</creator><general>Hindawi Publishing Corporation</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7SC</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20150101</creationdate><title>Intelligent Autofeedback and Safety Early-Warning for Underground Cavern Engineering during Construction Based on BP Neural Network and FEM</title><author>Lei, Xu ; Ren, Qingwen ; Zhang, Taijun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-34976e629f328894a82a31080cb3904e02ff1efc4905ef1d23a327c70ae1c4933</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Artificial intelligence</topic><topic>Automation</topic><topic>Back propagation networks</topic><topic>Caverns</topic><topic>Civil engineering</topic><topic>Computer programs</topic><topic>Construction</topic><topic>Construction engineering</topic><topic>Data exchange</topic><topic>Design techniques</topic><topic>Efficiency</topic><topic>Finite element method</topic><topic>Hydroelectric power</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Mathematical problems</topic><topic>Neural networks</topic><topic>Parameters</topic><topic>Power plants</topic><topic>Pumped storage</topic><topic>Safety</topic><topic>Software</topic><topic>Software engineering</topic><topic>Underground caverns</topic><topic>Underground construction</topic><topic>Underground storage</topic><topic>Warning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lei, Xu</creatorcontrib><creatorcontrib>Ren, Qingwen</creatorcontrib><creatorcontrib>Zhang, Taijun</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Computer and Information Systems Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lei, Xu</au><au>Ren, Qingwen</au><au>Zhang, Taijun</au><au>Cen, Song</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intelligent Autofeedback and Safety Early-Warning for Underground Cavern Engineering during Construction Based on BP Neural Network and FEM</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2015-01-01</date><risdate>2015</risdate><volume>2015</volume><issue>2015</issue><spage>1</spage><epage>8</epage><pages>1-8</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>The low efficiency of feedback analysis is one of the main shortcomings in the construction of underground cavern engineering. With this in mind, a method of intelligent autofeedback and safety early-warning for underground cavern is proposed based on BP neural network and FEM. The training sample points are chosen by using uniform test design method, and the autogeneration of FEM calculation file for ABAQUS is realized by using the technique of file partition, information grouping, and orderly numbering. Then, intelligent autoinversion of mechanics parameters is realized, and the automatic connection of parameter inversion, subsequent prediction, and safety early-warning is achieved. The software of intelligent autofeedback and safety early-warning for underground cavern engineering during construction is developed. Finally, the applicability of the proposed method and the developed software is verified through an application example of underground cavern of a pumped-storage power station located in Southwest China.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2015/873823</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| source | Wiley-Blackwell Open Access Titles; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Artificial intelligence Automation Back propagation networks Caverns Civil engineering Computer programs Construction Construction engineering Data exchange Design techniques Efficiency Finite element method Hydroelectric power Mathematical analysis Mathematical models Mathematical problems Neural networks Parameters Power plants Pumped storage Safety Software Software engineering Underground caverns Underground construction Underground storage Warning |
| title | Intelligent Autofeedback and Safety Early-Warning for Underground Cavern Engineering during Construction Based on BP Neural Network and FEM |
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