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Bridge Influence Line Identification Based on Regularized Least-Squares QR Decomposition Method
AbstractThe bridge influence line is an important tool to study the bridge response under moving loads and contains tremendous structural information. Structural deterioration during the bridge’s service life will induce variation in the bridge influence line, which makes it essential to identify th...
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Published in: | Journal of bridge engineering 2019-08, Vol.24 (8) |
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container_title | Journal of bridge engineering |
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creator | Zheng, Xu Yang, Dong-Hui Yi, Ting-Hua Li, Hong-Nan Chen, Zhi-Wei |
description | AbstractThe bridge influence line is an important tool to study the bridge response under moving loads and contains tremendous structural information. Structural deterioration during the bridge’s service life will induce variation in the bridge influence line, which makes it essential to identify the influence line exactly from the field measurement data under moving vehicle loads. This article establishes the underdetermined influence line identification model for a bridge under multiaxle vehicle excitation, in which the bridge influence line is identified based on the regularized least-squares QR decomposition method. Then an example of a four-span girder bridge is provided to prove the validity of the method, and the accuracy and computational complexity of different methods in this condition are compared. This study thus provides an effective method for the identification of the bridge influence line with very high computational efficiency and identification accuracy. |
doi_str_mv | 10.1061/(ASCE)BE.1943-5592.0001458 |
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Structural deterioration during the bridge’s service life will induce variation in the bridge influence line, which makes it essential to identify the influence line exactly from the field measurement data under moving vehicle loads. This article establishes the underdetermined influence line identification model for a bridge under multiaxle vehicle excitation, in which the bridge influence line is identified based on the regularized least-squares QR decomposition method. Then an example of a four-span girder bridge is provided to prove the validity of the method, and the accuracy and computational complexity of different methods in this condition are compared. 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Structural deterioration during the bridge’s service life will induce variation in the bridge influence line, which makes it essential to identify the influence line exactly from the field measurement data under moving vehicle loads. This article establishes the underdetermined influence line identification model for a bridge under multiaxle vehicle excitation, in which the bridge influence line is identified based on the regularized least-squares QR decomposition method. Then an example of a four-span girder bridge is provided to prove the validity of the method, and the accuracy and computational complexity of different methods in this condition are compared. This study thus provides an effective method for the identification of the bridge influence line with very high computational efficiency and identification accuracy.</description><subject>Accuracy</subject><subject>Bridge construction</subject><subject>Bridge loads</subject><subject>Civil engineering</subject><subject>Computer applications</subject><subject>Computing time</subject><subject>Decomposition</subject><subject>Girder bridges</subject><subject>Identification</subject><subject>Influence lines</subject><subject>Least squares</subject><subject>Loads (forces)</subject><subject>Moving loads</subject><subject>Service life</subject><subject>Technical Notes</subject><issn>1084-0702</issn><issn>1943-5592</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kMtOwzAQRS0EEqXwDxFsYJFix3mya0uASkGIFtbW2BkXV21S7GQBX09CC6xYzUP3zEiHkHNGR4zG7PpyvJjmV5N8xLKQ-1GUBSNKKQuj9IAMfneHXU_T0KcJDY7JiXOrPhNnfEDExJpyid6s0usWK4VeYapuLLFqjDYKGlNX3gQcll7XzHHZrsGaz24sEFzjL95bsOi857l3i6rebGtnvplHbN7q8pQcaVg7PNvXIXm9y1-mD37xdD-bjgsfeBI2vtSgFY_LAFCWMksVUEkx4QoQOYQyoSgziFgQaSYZp1ylOgRZahlFnAHjQ3Kxu7u19XuLrhGrurVV91IEQZCEYSeAd6mbXUrZ2jmLWmyt2YD9EIyKXqgQvVAxyUUvT_TyxF5oB8c7GJzCv_M_5P_gF9a4e5k</recordid><startdate>20190801</startdate><enddate>20190801</enddate><creator>Zheng, Xu</creator><creator>Yang, Dong-Hui</creator><creator>Yi, Ting-Hua</creator><creator>Li, Hong-Nan</creator><creator>Chen, Zhi-Wei</creator><general>American Society of Civil Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7TN</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope></search><sort><creationdate>20190801</creationdate><title>Bridge Influence Line Identification Based on Regularized Least-Squares QR Decomposition Method</title><author>Zheng, Xu ; Yang, Dong-Hui ; Yi, Ting-Hua ; Li, Hong-Nan ; Chen, Zhi-Wei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a374t-bfafc36d2aebdb98ca0b0e73caee3a4b70eb9a5125f1b1303c8f4abdfb5531a13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accuracy</topic><topic>Bridge construction</topic><topic>Bridge loads</topic><topic>Civil engineering</topic><topic>Computer applications</topic><topic>Computing time</topic><topic>Decomposition</topic><topic>Girder bridges</topic><topic>Identification</topic><topic>Influence lines</topic><topic>Least squares</topic><topic>Loads (forces)</topic><topic>Moving loads</topic><topic>Service life</topic><topic>Technical Notes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zheng, Xu</creatorcontrib><creatorcontrib>Yang, Dong-Hui</creatorcontrib><creatorcontrib>Yi, Ting-Hua</creatorcontrib><creatorcontrib>Li, Hong-Nan</creatorcontrib><creatorcontrib>Chen, Zhi-Wei</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Journal of bridge engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zheng, Xu</au><au>Yang, Dong-Hui</au><au>Yi, Ting-Hua</au><au>Li, Hong-Nan</au><au>Chen, Zhi-Wei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bridge Influence Line Identification Based on Regularized Least-Squares QR Decomposition Method</atitle><jtitle>Journal of bridge engineering</jtitle><date>2019-08-01</date><risdate>2019</risdate><volume>24</volume><issue>8</issue><issn>1084-0702</issn><eissn>1943-5592</eissn><abstract>AbstractThe bridge influence line is an important tool to study the bridge response under moving loads and contains tremendous structural information. Structural deterioration during the bridge’s service life will induce variation in the bridge influence line, which makes it essential to identify the influence line exactly from the field measurement data under moving vehicle loads. This article establishes the underdetermined influence line identification model for a bridge under multiaxle vehicle excitation, in which the bridge influence line is identified based on the regularized least-squares QR decomposition method. Then an example of a four-span girder bridge is provided to prove the validity of the method, and the accuracy and computational complexity of different methods in this condition are compared. This study thus provides an effective method for the identification of the bridge influence line with very high computational efficiency and identification accuracy.</abstract><cop>New York</cop><pub>American Society of Civil Engineers</pub><doi>10.1061/(ASCE)BE.1943-5592.0001458</doi></addata></record> |
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source | ASCE Library (civil engineering) |
subjects | Accuracy Bridge construction Bridge loads Civil engineering Computer applications Computing time Decomposition Girder bridges Identification Influence lines Least squares Loads (forces) Moving loads Service life Technical Notes |
title | Bridge Influence Line Identification Based on Regularized Least-Squares QR Decomposition Method |
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