International Concrete Abstracts Portal

Title: Probabilistic Prediction Model for Flexure-Shear Capacity of Reinforced Concrete Girder

Author(s): Xinmin Zhang, Chaoyuan Wu, Zengwei Guo, Fanxiang Xia, Xianhu Ruan

Publication: Structural Journal

Volume:

Issue:

Appears on pages(s):

Keywords: Bayesian estimate; MCMC; probability model; RC member; shear capacity

DOI: 10.14359/51745491

Date: 12/19/2024

Abstract:
It is well known that the estimates of most shear capacity prediction models for reinforced concrete (RC) components are of high dispersion, due to its elaborate failure mechanism and elusive. A probability prediction model is more appropriate for estimating the shear capacity of RC members than a deterministic prediction model. Therefore, this study proposed a probabilistic model to evaluate the shear capacity of RC T-beams and employed a Bayesian-Markov Chain Monte Carlo (MCMC) approach to determine the posterior parameter in the shear strength prediction model by Bayesian updating. The analysis results indicate that the probabilistic model achieves minimal variance, offering the most accurate predictions that closely match test data compared with other prediction models. The shear capacity of the T-beam increases with changes in flange width and flange height ratio but remains constant once beyond a certain level. The shear capacity varies rapidly when the shear-span ratio (λ) is less than 2.5 or larger than 4.0, due to a notable shift in the failure mechanism. Besides, the shear capacity raises linearly by increasing the characteristic value of stirrups (ρ_vf_yv).