Temperature Field Online Reconstruction for In-Service Concrete Arch Dam Based on Limited Temperature Observation Data Using AdaBoost-ANN Algorithm
Temperature is one of the factors affecting the safety operation of concrete arch dams. To accurately reconstruct the temperature field of the concrete arch dam online based on the temperature data of several typical dam sections, this paper proposes the AdaBoost-ANN algorithm. The algorithm uses ar...
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| Veröffentlicht in: | Mathematical problems in engineering 2021-07, Vol.2021, p.1-10 |
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| creator | Chen, Zhuoyan Zheng, Dongjian Li, Jiqiong Wu, Xin Qiu, Jianchun |
| description | Temperature is one of the factors affecting the safety operation of concrete arch dams. To accurately reconstruct the temperature field of the concrete arch dam online based on the temperature data of several typical dam sections, this paper proposes the AdaBoost-ANN algorithm. The algorithm uses artificial neural network (ANN) to establish a training set of the measured temperature data and the temperature field of the concrete arch dam obtained by the three-dimensional finite element model; these trained artificial neural networks are used as weak classifiers of the AdaBoost algorithm. Then, the AdaBoost-ANN algorithm is used to establish the mapping relationship between the measured temperature data and the temperature field, and the online reconstruction of the temperature field of the concrete arch dam is realized. The case study shows that the temperature field of the concrete arch dam can be accurately established by AdaBoost-ANN algorithm based on limited temperature observation data. The algorithm is more time-saving and labor-saving than the finite element method and is convenient for online reconstruction of the temperature field and assessment of the safety status of the concrete arch dam. |
| doi_str_mv | 10.1155/2021/9979994 |
| format | Article |
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To accurately reconstruct the temperature field of the concrete arch dam online based on the temperature data of several typical dam sections, this paper proposes the AdaBoost-ANN algorithm. The algorithm uses artificial neural network (ANN) to establish a training set of the measured temperature data and the temperature field of the concrete arch dam obtained by the three-dimensional finite element model; these trained artificial neural networks are used as weak classifiers of the AdaBoost algorithm. Then, the AdaBoost-ANN algorithm is used to establish the mapping relationship between the measured temperature data and the temperature field, and the online reconstruction of the temperature field of the concrete arch dam is realized. The case study shows that the temperature field of the concrete arch dam can be accurately established by AdaBoost-ANN algorithm based on limited temperature observation data. The algorithm is more time-saving and labor-saving than the finite element method and is convenient for online reconstruction of the temperature field and assessment of the safety status of the concrete arch dam.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2021/9979994</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Accuracy ; Algorithms ; Arch dams ; Artificial neural networks ; Boundary conditions ; Concrete dams ; Dam safety ; Dams ; Design specifications ; Finite element analysis ; Finite element method ; Heat ; Hydration ; Radiation ; Reconstruction ; Simulation ; Stress analysis ; Temperature distribution ; Three dimensional models</subject><ispartof>Mathematical problems in engineering, 2021-07, Vol.2021, p.1-10</ispartof><rights>Copyright © 2021 Zhuoyan Chen et al.</rights><rights>Copyright © 2021 Zhuoyan Chen et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c294t-89b3c801c241c498c31d9e5d0ad5f9653dbbaee4338c144818b2671923ea86da3</cites><orcidid>0000-0003-3753-4455 ; 0000-0003-0766-569X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,27911,27912</link.rule.ids></links><search><contributor>Nguyen Thanh, Nhon</contributor><contributor>Nhon Nguyen Thanh</contributor><creatorcontrib>Chen, Zhuoyan</creatorcontrib><creatorcontrib>Zheng, Dongjian</creatorcontrib><creatorcontrib>Li, Jiqiong</creatorcontrib><creatorcontrib>Wu, Xin</creatorcontrib><creatorcontrib>Qiu, Jianchun</creatorcontrib><title>Temperature Field Online Reconstruction for In-Service Concrete Arch Dam Based on Limited Temperature Observation Data Using AdaBoost-ANN Algorithm</title><title>Mathematical problems in engineering</title><description>Temperature is one of the factors affecting the safety operation of concrete arch dams. To accurately reconstruct the temperature field of the concrete arch dam online based on the temperature data of several typical dam sections, this paper proposes the AdaBoost-ANN algorithm. The algorithm uses artificial neural network (ANN) to establish a training set of the measured temperature data and the temperature field of the concrete arch dam obtained by the three-dimensional finite element model; these trained artificial neural networks are used as weak classifiers of the AdaBoost algorithm. Then, the AdaBoost-ANN algorithm is used to establish the mapping relationship between the measured temperature data and the temperature field, and the online reconstruction of the temperature field of the concrete arch dam is realized. The case study shows that the temperature field of the concrete arch dam can be accurately established by AdaBoost-ANN algorithm based on limited temperature observation data. The algorithm is more time-saving and labor-saving than the finite element method and is convenient for online reconstruction of the temperature field and assessment of the safety status of the concrete arch dam.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Arch dams</subject><subject>Artificial neural networks</subject><subject>Boundary conditions</subject><subject>Concrete dams</subject><subject>Dam safety</subject><subject>Dams</subject><subject>Design specifications</subject><subject>Finite element analysis</subject><subject>Finite element method</subject><subject>Heat</subject><subject>Hydration</subject><subject>Radiation</subject><subject>Reconstruction</subject><subject>Simulation</subject><subject>Stress analysis</subject><subject>Temperature distribution</subject><subject>Three dimensional models</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</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>eNp90LlOAzEQBuAVAomz4wEsUcLCjo_ELpeES4qIxCHRrbz2hBhl7WA7IJ6DF2YhFFRUM8U3_0h_URxCdQogxBmtKJwpNVRK8Y1iB8SAlQL4cLPfK8pLoOxpu9hN6aXqpQC5U3w-YLfEqPMqIrl0uLBk6hfOI7lDE3zKcWWyC57MQiQ3vrzH-OYMklHwJmJGUkczJ2PdkXOd0JJeTlzncr_-TZ62qT_UP0ljnTV5TM4_k9rq8xBSLuvbW1IvnkN0ed7tF1szvUh48Dv3isfLi4fRdTmZXt2M6klpqOK5lKplRlZgKAfDlTQMrEJhK23FTA0Es22rETlj0gDnEmRLB0NQlKGWA6vZXnG0zl3G8LrClJuXsIq-f9lQIYbApOLQq5O1MjGkFHHWLKPrdPxooGq-a2--a29-a-_58ZrPnbf63f2vvwCk2ILt</recordid><startdate>20210723</startdate><enddate>20210723</enddate><creator>Chen, Zhuoyan</creator><creator>Zheng, Dongjian</creator><creator>Li, Jiqiong</creator><creator>Wu, Xin</creator><creator>Qiu, Jianchun</creator><general>Hindawi</general><general>Hindawi Limited</general><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><orcidid>https://orcid.org/0000-0003-3753-4455</orcidid><orcidid>https://orcid.org/0000-0003-0766-569X</orcidid></search><sort><creationdate>20210723</creationdate><title>Temperature Field Online Reconstruction for In-Service Concrete Arch Dam Based on Limited Temperature Observation Data Using AdaBoost-ANN Algorithm</title><author>Chen, Zhuoyan ; Zheng, Dongjian ; Li, Jiqiong ; Wu, Xin ; Qiu, Jianchun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c294t-89b3c801c241c498c31d9e5d0ad5f9653dbbaee4338c144818b2671923ea86da3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Arch dams</topic><topic>Artificial neural networks</topic><topic>Boundary conditions</topic><topic>Concrete dams</topic><topic>Dam safety</topic><topic>Dams</topic><topic>Design specifications</topic><topic>Finite element analysis</topic><topic>Finite element method</topic><topic>Heat</topic><topic>Hydration</topic><topic>Radiation</topic><topic>Reconstruction</topic><topic>Simulation</topic><topic>Stress analysis</topic><topic>Temperature distribution</topic><topic>Three dimensional models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Zhuoyan</creatorcontrib><creatorcontrib>Zheng, Dongjian</creatorcontrib><creatorcontrib>Li, Jiqiong</creatorcontrib><creatorcontrib>Wu, Xin</creatorcontrib><creatorcontrib>Qiu, Jianchun</creatorcontrib><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 (ProQuest)</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><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Zhuoyan</au><au>Zheng, Dongjian</au><au>Li, Jiqiong</au><au>Wu, Xin</au><au>Qiu, Jianchun</au><au>Nguyen Thanh, Nhon</au><au>Nhon Nguyen Thanh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Temperature Field Online Reconstruction for In-Service Concrete Arch Dam Based on Limited Temperature Observation Data Using AdaBoost-ANN Algorithm</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2021-07-23</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>Temperature is one of the factors affecting the safety operation of concrete arch dams. To accurately reconstruct the temperature field of the concrete arch dam online based on the temperature data of several typical dam sections, this paper proposes the AdaBoost-ANN algorithm. The algorithm uses artificial neural network (ANN) to establish a training set of the measured temperature data and the temperature field of the concrete arch dam obtained by the three-dimensional finite element model; these trained artificial neural networks are used as weak classifiers of the AdaBoost algorithm. Then, the AdaBoost-ANN algorithm is used to establish the mapping relationship between the measured temperature data and the temperature field, and the online reconstruction of the temperature field of the concrete arch dam is realized. The case study shows that the temperature field of the concrete arch dam can be accurately established by AdaBoost-ANN algorithm based on limited temperature observation data. 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| subjects | Accuracy Algorithms Arch dams Artificial neural networks Boundary conditions Concrete dams Dam safety Dams Design specifications Finite element analysis Finite element method Heat Hydration Radiation Reconstruction Simulation Stress analysis Temperature distribution Three dimensional models |
| title | Temperature Field Online Reconstruction for In-Service Concrete Arch Dam Based on Limited Temperature Observation Data Using AdaBoost-ANN Algorithm |
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