Probabilistic models for shear strength of circular and square concrete-filled steel tubes
Concrete-filled steel tubular (CFST) columns are pivotal in modern structural design, leveraging the synergistic properties of their materials while addressing structural shortcomings. However, forecasting their shear capacity remains a formidable challenge, owing to the intricate load transfer mech...
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AIP Publishing LLC
2025-02-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0250829 |
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| author | Yong Yu Wenjun Wu Jia Yan Jianbo Zang |
| author_facet | Yong Yu Wenjun Wu Jia Yan Jianbo Zang |
| author_sort | Yong Yu |
| collection | DOAJ |
| description | Concrete-filled steel tubular (CFST) columns are pivotal in modern structural design, leveraging the synergistic properties of their materials while addressing structural shortcomings. However, forecasting their shear capacity remains a formidable challenge, owing to the intricate load transfer mechanisms involved. This study utilizes a Bayesian model updating technique, complemented by reliability analysis, drawing upon the most extensive shear test database to date for both circular and square CFST columns, encompassing 314 high-quality datasets. Through Bayesian updating, two explicit formulas for shear capacity estimation are derived and validated via reliability analysis, facilitating a comprehensive evaluation of load and resistance factor design. Key conclusions include the following: (a) Among existing empirical models, those of Mansouri and Lin demonstrate the highest predictive accuracy for shear capacity in circular and square columns, with R2 values of 0.920 and 0.964, although they exhibit relatively high coefficients of variation (COVs) of 0.162 and 0.172, respectively, in predicted-to-measured ratios. (b) Bayesian-derived formulas, incorporating both prior knowledge and empirical data, offer enhanced precision and physical relevance, achieving R2 values of 0.970 and 0.975, with lower COVs of 0.121 and 0.163 for circular and square columns, respectively. (c) The proposed models’ reliability indices exceed 3.7 under the resistance reduction factor method, meeting brittle failure safety criteria. In partial safety factor design, the square column formula is safe, while the circular column model slightly falls short. To meet safety standards, the study recommends increasing partial safety factors for concrete compressive strength and steel yield strength from 1.4 and 1.1 to 1.55 and 1.2, respectively. |
| format | Article |
| id | doaj-art-b9b276f4464749babc5115344f8d83b0 |
| institution | DOAJ |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | AIP Publishing LLC |
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| spelling | doaj-art-b9b276f4464749babc5115344f8d83b02025-08-20T03:00:21ZengAIP Publishing LLCAIP Advances2158-32262025-02-01152025210025210-1510.1063/5.0250829Probabilistic models for shear strength of circular and square concrete-filled steel tubesYong Yu0Wenjun Wu1Jia Yan2Jianbo Zang3School of Intelligent Transportation and Engineering, Guangzhou Maritime University, Guangzhou, ChinaSchool of Art and Design, Guangdong Vocational Academy of Art, Foshan, ChinaSchool of Environment and Civil Engineering, Dongguan University of Technology, Dongguan, ChinaState Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, ChinaConcrete-filled steel tubular (CFST) columns are pivotal in modern structural design, leveraging the synergistic properties of their materials while addressing structural shortcomings. However, forecasting their shear capacity remains a formidable challenge, owing to the intricate load transfer mechanisms involved. This study utilizes a Bayesian model updating technique, complemented by reliability analysis, drawing upon the most extensive shear test database to date for both circular and square CFST columns, encompassing 314 high-quality datasets. Through Bayesian updating, two explicit formulas for shear capacity estimation are derived and validated via reliability analysis, facilitating a comprehensive evaluation of load and resistance factor design. Key conclusions include the following: (a) Among existing empirical models, those of Mansouri and Lin demonstrate the highest predictive accuracy for shear capacity in circular and square columns, with R2 values of 0.920 and 0.964, although they exhibit relatively high coefficients of variation (COVs) of 0.162 and 0.172, respectively, in predicted-to-measured ratios. (b) Bayesian-derived formulas, incorporating both prior knowledge and empirical data, offer enhanced precision and physical relevance, achieving R2 values of 0.970 and 0.975, with lower COVs of 0.121 and 0.163 for circular and square columns, respectively. (c) The proposed models’ reliability indices exceed 3.7 under the resistance reduction factor method, meeting brittle failure safety criteria. In partial safety factor design, the square column formula is safe, while the circular column model slightly falls short. To meet safety standards, the study recommends increasing partial safety factors for concrete compressive strength and steel yield strength from 1.4 and 1.1 to 1.55 and 1.2, respectively.http://dx.doi.org/10.1063/5.0250829 |
| spellingShingle | Yong Yu Wenjun Wu Jia Yan Jianbo Zang Probabilistic models for shear strength of circular and square concrete-filled steel tubes AIP Advances |
| title | Probabilistic models for shear strength of circular and square concrete-filled steel tubes |
| title_full | Probabilistic models for shear strength of circular and square concrete-filled steel tubes |
| title_fullStr | Probabilistic models for shear strength of circular and square concrete-filled steel tubes |
| title_full_unstemmed | Probabilistic models for shear strength of circular and square concrete-filled steel tubes |
| title_short | Probabilistic models for shear strength of circular and square concrete-filled steel tubes |
| title_sort | probabilistic models for shear strength of circular and square concrete filled steel tubes |
| url | http://dx.doi.org/10.1063/5.0250829 |
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