Počet záznamů: 1  

Assessment of RC frame capacity subjected to a loss of corner column

  1. 1.
    0559946 - ÚTAM 2023 RIV US eng J - Článek v odborném periodiku
    Guo, M. - Huang, H. - Zhang, Wei - Xue, C. - Huang, M.
    Assessment of RC frame capacity subjected to a loss of corner column.
    Journal of Structural Engineering-Asce. Roč. 148, č. 9 (2022), č. článku 04022122. ISSN 0733-9445. E-ISSN 1943-541X
    Institucionální podpora: RVO:68378297
    Klíčová slova: progressive collapse * corner column * reinforced concrete (RC) structure * machine learning * peak resistance capacity * shapely additive explanations (SHAP) values
    Obor OECD: Civil engineering
    Impakt faktor: 4.1, rok: 2022
    Způsob publikování: Omezený přístup
    https://doi.org/10.1061/(ASCE)ST.1943-541X.0003423

    In this paper, three one-third scale reinforced concrete (RC) beam-column-slab structure specimen tests were conducted to investigate the collapse mechanisms under a loss of the corner column, including a frame with slab (S-COR), a frame with slab and secondary beams (SS-COR), and a frame without slab (NS-COR). The slab and secondary beam’s contributions were investigated by comparing the SS-COR and NS-COR, SS-COR, and S-COR specimens. The results show that the RC slab significantly enhanced the load resistance. Only a slight increase in peak resistance capacity of the SS-COR specimen was observed, while the ductility improved obviously due to the existence of secondary beams. The failure mode of the SS-COR frame is different from that of the S-COR frame: No concrete failure line occurs on the slab bottom, and the cracks develop entirely on the slab top. Moreover, based on the test results, finite element models (FE) were updated by adapting the OpenSeespy, which shows a good fit between the test curves and simulation results. Finally, 1,000 samples considering the uncertainty parameters were generated using Monte Carlo sampling to better understand the effect of uncertainty on the structure response. Data-driven models based on machine learning were used to predict the peak resistance capacity of the RC structures with slab and secondary beams.
    Trvalý link: https://hdl.handle.net/11104/0333066

     
     
Počet záznamů: 1  

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