Predictive models for concrete strength estimation of existing bridges in Brazil

Abstract This work presents a review of nondestructive techniques for concrete strength estimation. Also, five predictive models are proposed for existing bridges in Brazil, based on ultrasonic pulse velocity (UPV) measurements. The models were calibrated through empirical random regression, cross-validation procedure, bi-objective approach, Bayesian updating, and using a 95% confidence interval. The models were assessed within the database and against datasets found in the literature. The empirical regression, cross-validation, and bi-objective techniques provided the most accurate results for the present dataset. The 95% confidence interval avoids overestimating the concrete strength, but it results in poor model correlation. The Bayesian inference model was the one that best predicted the strength of datasets outside its scope of validation.