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    Deep Learning–Based Retrofitting and Seismic Risk Assessment of Road Networks

    Source: Journal of Computing in Civil Engineering:;2021:;Volume ( 036 ):;issue: 002::page 04021038
    Author:
    Rodrigo Silva-Lopez
    ,
    Jack W. Baker
    ,
    Alan Poulos
    DOI: 10.1061/(ASCE)CP.1943-5487.0001006
    Publisher: ASCE
    Abstract: Seismic risk assessment of road systems involves computationally expensive traffic simulations to evaluate the performance of the system. To accelerate this process, this paper develops a neural network surrogate model that allows rapid and accurate estimation of changes in traffic performance metrics due to bridge damage. Some of the methodological aspects explored when calibrating this neural network are defining sampling protocols, selecting hyperparameters, and evaluating practical considerations of the model. In addition to the neural network, a modified version of the local interpretable model-agnostic explanation (LIME) is proposed as a retrofitting strategy that minimizes earthquakes’ impact on the system. The modified version (LIME-TI) uses traffic impacts (TI) and rates of occurrence to aggregate the importance of individual damage realizations during the computation of variable importance. This study uses the San Francisco Bay Area road network as a testbed. As a conclusion of this study, the neural network accurately predicts the system’s performance while taking five orders of magnitude less time to compute traffic metrics, allowing decision-makers to evaluate the impact of retrofitting bridges in the system quickly. Moreover, the proposed LIME-TI metric is superior to others (such as traffic volume or vulnerability) in identifying bridges whose retrofit effectively improves network performance.
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      Deep Learning–Based Retrofitting and Seismic Risk Assessment of Road Networks

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4283116
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    contributor authorRodrigo Silva-Lopez
    contributor authorJack W. Baker
    contributor authorAlan Poulos
    date accessioned2022-05-07T20:57:26Z
    date available2022-05-07T20:57:26Z
    date issued2021-12-15
    identifier other(ASCE)CP.1943-5487.0001006.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4283116
    description abstractSeismic risk assessment of road systems involves computationally expensive traffic simulations to evaluate the performance of the system. To accelerate this process, this paper develops a neural network surrogate model that allows rapid and accurate estimation of changes in traffic performance metrics due to bridge damage. Some of the methodological aspects explored when calibrating this neural network are defining sampling protocols, selecting hyperparameters, and evaluating practical considerations of the model. In addition to the neural network, a modified version of the local interpretable model-agnostic explanation (LIME) is proposed as a retrofitting strategy that minimizes earthquakes’ impact on the system. The modified version (LIME-TI) uses traffic impacts (TI) and rates of occurrence to aggregate the importance of individual damage realizations during the computation of variable importance. This study uses the San Francisco Bay Area road network as a testbed. As a conclusion of this study, the neural network accurately predicts the system’s performance while taking five orders of magnitude less time to compute traffic metrics, allowing decision-makers to evaluate the impact of retrofitting bridges in the system quickly. Moreover, the proposed LIME-TI metric is superior to others (such as traffic volume or vulnerability) in identifying bridges whose retrofit effectively improves network performance.
    publisherASCE
    titleDeep Learning–Based Retrofitting and Seismic Risk Assessment of Road Networks
    typeJournal Paper
    journal volume36
    journal issue2
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0001006
    journal fristpage04021038
    journal lastpage04021038-12
    page12
    treeJournal of Computing in Civil Engineering:;2021:;Volume ( 036 ):;issue: 002
    contenttypeFulltext
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