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  • Open Access

    ARTICLE

    Estimation of Aleatory Randomness by Sa(T1)-Based Intensity Measures in Fragility Analysis of Reinforced Concrete Frame Structures

    Yantai Zhang1,*, Yongan Shi2, Baoyin Sun3, Zheng Wang4

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 73-96, 2022, DOI:10.32604/cmes.2022.016857 - 29 November 2021

    Abstract Based on the multiple stripes analysis method, an investigation of the estimation of aleatory randomness by Sa(T1)-based intensity measures (IMs) in the fragility analysis is carried out for two typical low- and medium-rise reinforced concrete (RC) frame structures with 4 and 8 stories, respectively. The sensitivity of the aleatory randomness estimated in fragility curves to various Sa(T1)-based IMs is analyzed at three damage limit states, i.e., immediate occupancy, life safety, and collapse prevention. In addition, the effect of characterization methods of bidirectional ground motion intensity on the record-to-record variability is investigated. It is found that the… More >

  • Open Access

    ARTICLE

    Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames

    Baoyin Sun1, 2, Yantai Zhang3, Caigui Huang4, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 755-776, 2020, DOI:10.32604/cmes.2020.09632 - 12 October 2020

    Abstract Steel frames equipped with buckling restrained braces (BRBs) have been increasingly applied in earthquake-prone areas given their excellent capacity for resisting lateral forces. Therefore, special attention has been paid to the seismic risk assessment (SRA) of such structures, e.g., seismic fragility analysis. Conventional approaches, e.g., nonlinear finite element simulation (NFES), are computationally inefficient for SRA analysis particularly for large-scale steel BRB frame structures. In this study, a machine learning (ML)- based seismic fragility analysis framework is established to effectively assess the risk to structures under seismic loading conditions. An optimal artificial neural network model can More >

  • Open Access

    ARTICLE

    Seismic Fragility Analysis of Long-Span Bridge System with Durability Degradation

    Yan Liang1,*, Jialei Yan1, Zhanqi Cheng1,*, Huai Chen1, Ruimin Mao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.1, pp. 177-214, 2019, DOI:10.32604/cmes.2019.07141

    Abstract An offshore long-span continuous rigid-frame bridge is taken as an example to study the effect of degradation of bond-slip behavior on the seismic performance of bridges in an offshore environment during a service period. On the basis of a numerical simulation analysis using the OpenSeeS platform, the influence of durability degradation of concrete carbonization, steel corrosion, and degradation of bond-slip performance is considered collectively using incremental dynamic analysis method to examine the time-varying seismic fragility of the offshore bridge. Results show that when bond slip is considered, the exceedance probability of the bridge components and the… More >

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