Title:
Probabilistic Evaluation for Yield Displacement of Reinforced Concrete Columns with Flexural Failure (Prepublished)
Author(s):
Bo Yu, Pengfei Zhang, and Shaonan Li
Publication:
Structural Journal
Volume:
Issue:
Appears on pages(s):
Keywords:
Bayesian theory; confidence intervals; probabilistic evaluation; reinforced concrete columns; yield displacement
DOI:
10.14359/51749098
Date:
7/31/2025
Abstract:
To evaluate the calculation accuracy of traditional yield displacement models and to describe the probabilistic characteristics of yield displacement, a probabilistic model for yield displacement of reinforced concrete (RC) columns with flexural failure was developed based on the Bayesian theory and the Markov Chain Monte Carlo (MCMC) method. The analytical expression for the yield displacement of RC columns was established by applying the plane-section assumption and cross-section analysis first. Then, the probabilistic model for yield displacement of RC columns with flexural failure was developed by replacing the empirical coefficients in the analytical expression with probabilistic coefficients. Moreover, the posterior information of the probabilistic coefficients was determined based on the prior information from experimental data and the MCMC method. Finally, the calculation accuracy of deterministic models for yield displacement was evaluated based on the experimental data, probability density functions, and confidence intervals. Analysis results demonstrate that the proposed probabilistic model provides an alternative approach to evaluate the calculation accuracy of deterministic models for yield displacement of RC columns with flexural failure. Priestley's model, JTD's model, and Cui's model tend to underestimate the yield displacement of RC columns, while Fardis's model and Billah's model often overestimate the yield displacement of RC columns.