YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Engineering Mechanics
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Engineering Mechanics
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Physics-Informed Neural Networks for System Identification of Structural Systems with a Multiphysics Damping Model

    Source: Journal of Engineering Mechanics:;2023:;Volume ( 149 ):;issue: 010::page 04023079-1
    Author:
    Tong Liu
    ,
    Hadi Meidani
    DOI: 10.1061/JENMDT.EMENG-7060
    Publisher: ASCE
    Abstract: Structural system identification is critical in resilience assessments and structural health monitoring, especially following natural hazards. Among the nonlinear structural behaviors, structural damping is a complex behavior that can be modeled as a multiphysics system wherein the structure interacts with an external thermal bath and undergoes thermalization. In this paper, we propose a novel physics-informed neural network approach for nonlinear structural system identification and demonstrate its application in multiphysics cases where the damping term is governed by a separated dynamics equation. The proposed approach, called PIDynNet, improves the estimation of the parameters of nonlinear structural systems by integrating auxiliary physics-based loss terms, one for the structural dynamics and one for the thermal transfer. These physics-based loss terms form the overall loss function in addition to a supervised data-based loss term. To ensure effective learning during the identification process, subsampling and early stopping strategies are developed. The proposed framework also has the generalization capability to predict nonlinear responses for unseen ground excitations. Two numerical experiments of nonlinear systems are conducted to demonstrate the comparative performance of PIDynNet.
    • Download: (2.252Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Physics-Informed Neural Networks for System Identification of Structural Systems with a Multiphysics Damping Model

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4293505
    Collections
    • Journal of Engineering Mechanics

    Show full item record

    contributor authorTong Liu
    contributor authorHadi Meidani
    date accessioned2023-11-27T23:22:10Z
    date available2023-11-27T23:22:10Z
    date issued8/2/2023 12:00:00 AM
    date issued2023-08-02
    identifier otherJENMDT.EMENG-7060.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4293505
    description abstractStructural system identification is critical in resilience assessments and structural health monitoring, especially following natural hazards. Among the nonlinear structural behaviors, structural damping is a complex behavior that can be modeled as a multiphysics system wherein the structure interacts with an external thermal bath and undergoes thermalization. In this paper, we propose a novel physics-informed neural network approach for nonlinear structural system identification and demonstrate its application in multiphysics cases where the damping term is governed by a separated dynamics equation. The proposed approach, called PIDynNet, improves the estimation of the parameters of nonlinear structural systems by integrating auxiliary physics-based loss terms, one for the structural dynamics and one for the thermal transfer. These physics-based loss terms form the overall loss function in addition to a supervised data-based loss term. To ensure effective learning during the identification process, subsampling and early stopping strategies are developed. The proposed framework also has the generalization capability to predict nonlinear responses for unseen ground excitations. Two numerical experiments of nonlinear systems are conducted to demonstrate the comparative performance of PIDynNet.
    publisherASCE
    titlePhysics-Informed Neural Networks for System Identification of Structural Systems with a Multiphysics Damping Model
    typeJournal Article
    journal volume149
    journal issue10
    journal titleJournal of Engineering Mechanics
    identifier doi10.1061/JENMDT.EMENG-7060
    journal fristpage04023079-1
    journal lastpage04023079-12
    page12
    treeJournal of Engineering Mechanics:;2023:;Volume ( 149 ):;issue: 010
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian