Residual behaviour and damage assessment of UHPC-filled double-skin steel tubular columns after lateral impact

被引:1
|
作者
Wang, Weiqiang [1 ]
Xiong, Zhilong [1 ]
Yu, Yang [2 ]
Chen, Da [3 ]
Wu, Chengqing [4 ]
机构
[1] Hohai Univ, Coll Water Conservancy & Hydropower Engn, Nanjing 210098, Peoples R China
[2] Univ New South Wales, Ctr Infrastructure Engn & Safety, Sch Civil & Environm Engn, Sydney, NSW 2052, Australia
[3] Hohai Univ, Coll Harbour Coastal & Offshore Engn, Nanjing 210098, Peoples R China
[4] Univ Technol Sydney, Ctr Built Infrastruct Res, Sch Civil & Environm Engn, Sydney, NSW 2007, Australia
关键词
UHPC; CFDST columns; Impact height; Damage index; Machine learning; FIBER-REINFORCED CONCRETE; CFDST MEMBERS;
D O I
10.1016/j.tws.2024.112602
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Ultra-high performance concrete (UHPC)-filled double-skin steel tubular (DST) column has great potential to be used in the protective structures. Although its lateral impact behaviour has been well understood, the residual behaviour after lateral impact remains unexplored. As a result, this study extensively investigated the residual behaviour and damage assessment of UHPC-filled DST columns after lateral impact. Firstly, a set of six DST columns were designed and tested under lateral impact, followed by static axial compression. In addition, two intact columns were subjected to static axial compression for comparative analysis. Secondly, the refined finite element models were developed and validated using the current test data, and the impact resistant mechanism of UHPC-filled DST columns with different impact locations was analysed. Thirdly, the suitability of different damage indexes for the damage assessment of impacted UHPC-filled DST columns was evaluated. Two damage indexes, the ratio of mid-height deflection to column height (R1), and the ratio of local deflection to the column diameter (R2), were proposed for the DST columns. Finally, two types of machine learning-based models were developed to predict the impact damage of UHPC-filled DST columns. The prediction models were interpreted locally and globally using the additive feature attribution method Shapley Additive Explanation (SHAP). The machine learning-based prediction models can rapidly evaluate the damage extent of impacted UHPC-filled DST column, which hold great significance for the selection of strengthening and retrofitting schemes.
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页数:20
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