Probabilistic Prediction Model for Ultimate Conditions Under Compression of FRP-Wrapped Concrete Columns Based on Bayesian Inference

The compressive strength and ultimate strain of FRP-confined concrete cylinders are the key indicators for evaluating their mechanical properties. Accurate prediction of compressive strength and ultimate strain is essential for reliability analysis and design of such components. However, the existing ultimate condition under compression models lack sufficient prediction accuracy, and the results exhibit significant uncertainty. This study proposes a Bayesian model updating method based on Markov Chain Monte Carlo (MCMC) sampling to improve the prediction accuracy of the ultimate condition under compression for FRP-confined concrete cylinders and to quantify the uncertainty of the prediction results. First of all, 1016 sets of experimental data on the ultimate condition under compression of FRP-confined concrete cylinders from previous studies were collected. Subsequently, the probabilistic updating model and evaluation system were established based on Bayesian parameter estimation principle, MCMC sampling, WAIC, and DIC. Then, several representative empirical models for predicting the ultimate condition under compression are selected, and their prediction performance is evaluated using the experimental data. Finally, a Bayesian updating problem is established for typical ultimate condition under compression models, and the posterior distributions of model parameters are obtained using MCMC sampling to select the best model, and the prediction performance of the optimal model is assessed using the experimental data. The results show that, compared with existing empirical models, the Bayesian inference-based probabilistic calculation model provides predictions closer to the experimental values, while also reasonably quantifying the uncertainty of the ultimate condition under compression prediction.