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A probabilistic framework to model bond strength between concrete and GFRP rebar using Bayesian linear regression by Amir Masoud Hassanzadeh; Hadi Nazarpour; Mehdi Dehestani is a Engineering article available to read on EtoBox.

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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 reinforcemen

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Author

Amir Masoud Hassanzadeh; Hadi Nazarpour; Mehdi Dehestani

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Engineering (Physical Sciences)