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    A stochastic gradient approach for the reliability maximization of passively controlled structures

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    Type
    Article
    Authors
    Carlon, A.G.
    Lopez, R.H.
    Espath, Luis cc
    Miguel, L.F.F.
    Beck, A.T.
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-02-10
    Online Publication Date
    2019-02-10
    Print Publication Date
    2019-05
    Permanent link to this record
    http://hdl.handle.net/10754/631665
    
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    Abstract
    This paper addresses the reduction of the computational burden of structural reliability maximization of passively controlled buildings subject to transient loading. Such a reduction is accomplished by means of Stochastic Gradient Descent (SGD) algorithms, which replace expensive multi-dimensional Monte Carlo integration by a singleton multi-iteration integration. In order to be able to apply SGD methods, the time-dependent structural reliability evaluation was constructed based on the out-crossing rate approach. Design of single and multi Friction Tuned Mass Dampers (FTMDs) are considered as design examples. The proposed SGD algorithm, the accelerated stochastic gradient descent (ASGD) algorithm, efficiently combines Nesterov acceleration, Polyak–Ruppert averaging and restart techniques. The main result drew from the numerical analysis is that SGD algorithms were able to maximize the structural reliability by providing more accurate results and requiring lower computational cost than well-known optimization methods. Finally, the limitation of the proposed optimization procedure is linked to the validity of the out-crossing rate to approximate the time-dependent structural reliability.
    Citation
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    Sponsors
    The authors gratefully acknowledge the financial support of CNPq (National Counsel of Technological and Scientific Development) and CAPES (Coordination of Superior Level Staff Improvement).
    Publisher
    Elsevier BV
    Journal
    Engineering Structures
    DOI
    10.1016/j.engstruct.2019.01.121
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0141029618328402
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.engstruct.2019.01.121
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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