Probabilistic models for shear strength of circular and square concrete-filled steel tubes
Year: 2025
Type: article
Abstract: 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.
Source: AIP Advances
DOI: 10.1063/5.0250829
Authors: Yong Yu, Wenjun Wu, Jia Yan, Jianbo Zang
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,350
Awards: G3992439383, G7537904114
OpenAlex ID: W4407185385
