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    Seismic Drift Demand Estimation for Steel Moment Frame Buildings: From Mechanics-Based to Data-Driven Models

    Source: Journal of Structural Engineering:;2021:;Volume ( 147 ):;issue: 006::page 04021058-1
    Author:
    Xingquan Guan
    ,
    Henry Burton
    ,
    Mehrdad Shokrabadi
    ,
    Zhengxiang Yi
    DOI: 10.1061/(ASCE)ST.1943-541X.0003004
    Publisher: ASCE
    Abstract: A spectrum of simplified methods for estimating building seismic drift demands is conceptualized. On one extreme are mechanics-based approaches that are derived solely from fundamental engineering principles. On the other end are purely data-driven models that are developed using parametric data sets generated from nonlinear response history analyses. Between these two extremes, there are models that combine elements of basic engineering principles and statistical learning (hybrid models). First, the benefits and drawbacks of four existing simplified seismic response estimation methodologies that fall within this spectrum of approaches are critically examined. Subsequently, a generalized framework for developing and validating hybrid and/or purely data-driven seismic demand estimation models is proposed. Using this framework, two new machine learning–based models are developed and rigorously evaluated. Finally, a comparative assessment of the existing and newly developed models is conducted while focusing on their predictive performance and the level of effort needed to implement them.
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      Seismic Drift Demand Estimation for Steel Moment Frame Buildings: From Mechanics-Based to Data-Driven Models

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4270375
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    contributor authorXingquan Guan
    contributor authorHenry Burton
    contributor authorMehrdad Shokrabadi
    contributor authorZhengxiang Yi
    date accessioned2022-01-31T23:47:59Z
    date available2022-01-31T23:47:59Z
    date issued6/1/2021
    identifier other%28ASCE%29ST.1943-541X.0003004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4270375
    description abstractA spectrum of simplified methods for estimating building seismic drift demands is conceptualized. On one extreme are mechanics-based approaches that are derived solely from fundamental engineering principles. On the other end are purely data-driven models that are developed using parametric data sets generated from nonlinear response history analyses. Between these two extremes, there are models that combine elements of basic engineering principles and statistical learning (hybrid models). First, the benefits and drawbacks of four existing simplified seismic response estimation methodologies that fall within this spectrum of approaches are critically examined. Subsequently, a generalized framework for developing and validating hybrid and/or purely data-driven seismic demand estimation models is proposed. Using this framework, two new machine learning–based models are developed and rigorously evaluated. Finally, a comparative assessment of the existing and newly developed models is conducted while focusing on their predictive performance and the level of effort needed to implement them.
    publisherASCE
    titleSeismic Drift Demand Estimation for Steel Moment Frame Buildings: From Mechanics-Based to Data-Driven Models
    typeJournal Paper
    journal volume147
    journal issue6
    journal titleJournal of Structural Engineering
    identifier doi10.1061/(ASCE)ST.1943-541X.0003004
    journal fristpage04021058-1
    journal lastpage04021058-17
    page17
    treeJournal of Structural Engineering:;2021:;Volume ( 147 ):;issue: 006
    contenttypeFulltext
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