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    Graph Neural Network–Based Spatiotemporal Structural Response Modeling in Buildings

    Source: Journal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 002::page 04025006-1
    Author:
    Fangyu Liu
    ,
    Yongjia Xu
    ,
    Junlin Li
    ,
    Linbing Wang
    DOI: 10.1061/JCCEE5.CPENG-6229
    Publisher: American Society of Civil Engineers
    Abstract: Modeling structural responses is vital in building structural health monitoring. This study proposed the graph network–based structure simulator (GNSS), a method employing graph neural networks, for spatiotemporal structural response modeling in buildings. GNSS considered both the spatial positions and connections of structural components and the temporal correlations of time-series structural data. The entire 6-story building was represented as a graph, with nodes representing mass and edges representing columns and beams. These nodes and edges captured time-series data about structural information, responses, and ground motion. GNSS included three components: encoder, processor, and decoder. Four GNSS model variations were explored (GNSS-NE, GNSS-N2E, GNSS-NUEU, and GNSS-Full), each investigating different feature integrations and graph network architectures. To assess GNSS’s predictive performance for structural responses (displacement and acceleration) under varying test conditions, three case studies were conducted: One-Step, Rollout, and Rollout&Calibration. Among the four model variations, GNSS-NE demonstrated superior performance in predicting both displacement and acceleration across all three case studies, except for displacement prediction in the Rollout scenario. Overall, GNSS models performed best in the One-Step case study, followed by Rollout&Calibration, with the lowest performance observed in the Rollout case study. These results highlight the significant potential of GNSS for extensive application in structural response modeling by effectively integrating spatial and temporal information.
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      Graph Neural Network–Based Spatiotemporal Structural Response Modeling in Buildings

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    contributor authorFangyu Liu
    contributor authorYongjia Xu
    contributor authorJunlin Li
    contributor authorLinbing Wang
    date accessioned2025-04-20T10:25:35Z
    date available2025-04-20T10:25:35Z
    date copyright1/11/2025 12:00:00 AM
    date issued2025
    identifier otherJCCEE5.CPENG-6229.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304697
    description abstractModeling structural responses is vital in building structural health monitoring. This study proposed the graph network–based structure simulator (GNSS), a method employing graph neural networks, for spatiotemporal structural response modeling in buildings. GNSS considered both the spatial positions and connections of structural components and the temporal correlations of time-series structural data. The entire 6-story building was represented as a graph, with nodes representing mass and edges representing columns and beams. These nodes and edges captured time-series data about structural information, responses, and ground motion. GNSS included three components: encoder, processor, and decoder. Four GNSS model variations were explored (GNSS-NE, GNSS-N2E, GNSS-NUEU, and GNSS-Full), each investigating different feature integrations and graph network architectures. To assess GNSS’s predictive performance for structural responses (displacement and acceleration) under varying test conditions, three case studies were conducted: One-Step, Rollout, and Rollout&Calibration. Among the four model variations, GNSS-NE demonstrated superior performance in predicting both displacement and acceleration across all three case studies, except for displacement prediction in the Rollout scenario. Overall, GNSS models performed best in the One-Step case study, followed by Rollout&Calibration, with the lowest performance observed in the Rollout case study. These results highlight the significant potential of GNSS for extensive application in structural response modeling by effectively integrating spatial and temporal information.
    publisherAmerican Society of Civil Engineers
    titleGraph Neural Network–Based Spatiotemporal Structural Response Modeling in Buildings
    typeJournal Article
    journal volume39
    journal issue2
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/JCCEE5.CPENG-6229
    journal fristpage04025006-1
    journal lastpage04025006-16
    page16
    treeJournal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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