A probabilistic framework to model bond strength between concrete and GFRP rebar using Bayesian linear regression

Abstract

This study was aimed at developing a probabilistic model using Bayesian linear regression approach to examine bond strength behavior between concrete and GFRP rebars. This probabilistic model presents a quantitative description of the epistemic uncertainties of model parameters and model error, which decrease as new information is received. By using the problem mechanics, variables were selected and explanatory functions were developed. Then, based on an observational database including 405 beam test specimens, 47 candidate models were selected based on different combinations of explanatory variables, and the fittest model was selected based on prediction quality, error non-normality, scattering non-homogeneity, residual correlation, and nonlinearity. Afterward, Bayesian regression was used to obtain the probability distribution of the model parameters, and model error was obtained using Bayesian regression. The paper continues by an in-depth explanation of the observation selection process, model development, model diagnostic, step-by-step model reduction process with the removal of ineffective parameters. The analysis results specified the bar position and transverse reinforcement ratio parameters as ineffective parameters. In addition, a comparison between deterministic and probabilistic models showed that contrary to the deterministic model, the probabilistic one could capture uncertainty in the model.