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    Simultaneous Recovery Model for Missing Multiple-Source Structural Health Monitoring Data of a Quayside Container Crane

    Source: Journal of Performance of Constructed Facilities:;2024:;Volume ( 038 ):;issue: 006::page 04024044-1
    Author:
    Jiahui Liu
    ,
    Jian Zhao
    ,
    Dong Zhao
    ,
    Xianrong Qin
    DOI: 10.1061/JPCFEV.CFENG-4806
    Publisher: American Society of Civil Engineers
    Abstract: Structural health monitoring (SHM) data encompasses vital information that provides a comprehensive understanding of the structural health condition. However, data loss may occur due to faults in acquisition equipment or sensors, and it is essential to reconstruct missing data to ensure the integrity of the monitoring information. Although extensive researches have been conducted on the topic of data recovery, a suitable missing data recovery method that can effectively address the missing data for multiple-source monitoring variables has not been identified yet. In this study, we proposed a novel missing data recovery model based on a deep learning framework to recover the missing strain and acceleration data simultaneously for SHM of the quayside container crane (QCC). The framework combines dual-tree complex wave transform (DTCWT) and bidirectional long short-term memory with attention mechanism (Att-BiLSTM). The multiple-source monitoring data are decomposed into several subtime series using the dual-tree complex wavelet, then, the Att-BiLSTM network is used to assign different weights to each subsequence in order to capture valuable information from the complete data. The effectiveness of the proposed model is verified by case studies, and the comparison results of missing data recovery under different miss rates show that the proposed model simultaneously improves the accuracy of missing data recovery for multiple-source monitoring variables.
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      Simultaneous Recovery Model for Missing Multiple-Source Structural Health Monitoring Data of a Quayside Container Crane

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4298076
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    contributor authorJiahui Liu
    contributor authorJian Zhao
    contributor authorDong Zhao
    contributor authorXianrong Qin
    date accessioned2024-12-24T09:59:06Z
    date available2024-12-24T09:59:06Z
    date copyright12/1/2024 12:00:00 AM
    date issued2024
    identifier otherJPCFEV.CFENG-4806.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4298076
    description abstractStructural health monitoring (SHM) data encompasses vital information that provides a comprehensive understanding of the structural health condition. However, data loss may occur due to faults in acquisition equipment or sensors, and it is essential to reconstruct missing data to ensure the integrity of the monitoring information. Although extensive researches have been conducted on the topic of data recovery, a suitable missing data recovery method that can effectively address the missing data for multiple-source monitoring variables has not been identified yet. In this study, we proposed a novel missing data recovery model based on a deep learning framework to recover the missing strain and acceleration data simultaneously for SHM of the quayside container crane (QCC). The framework combines dual-tree complex wave transform (DTCWT) and bidirectional long short-term memory with attention mechanism (Att-BiLSTM). The multiple-source monitoring data are decomposed into several subtime series using the dual-tree complex wavelet, then, the Att-BiLSTM network is used to assign different weights to each subsequence in order to capture valuable information from the complete data. The effectiveness of the proposed model is verified by case studies, and the comparison results of missing data recovery under different miss rates show that the proposed model simultaneously improves the accuracy of missing data recovery for multiple-source monitoring variables.
    publisherAmerican Society of Civil Engineers
    titleSimultaneous Recovery Model for Missing Multiple-Source Structural Health Monitoring Data of a Quayside Container Crane
    typeJournal Article
    journal volume38
    journal issue6
    journal titleJournal of Performance of Constructed Facilities
    identifier doi10.1061/JPCFEV.CFENG-4806
    journal fristpage04024044-1
    journal lastpage04024044-13
    page13
    treeJournal of Performance of Constructed Facilities:;2024:;Volume ( 038 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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