Open Access

ARTICLE

Artificial Neural Network (ANN) Approach for Predicting Concrete Compressive Strength by SonReb

Mario Bonagura, Lucio Nobile^*

LADS Laboratory, University of Bologna, Via dell’Università 50, Cesena, 47521, Italy

* Corresponding Author: Lucio Nobile. Email:

Structural Durability & Health Monitoring 2021, 15(2), 125-137. https://doi.org/10.32604/sdhm.2021.015644

Received 01 January 2021; Accepted 01 April 2021; Issue published 03 June 2021

Abstract

The compressive strength of concrete is one of most important mechanical parameters in the performance assessment of existing reinforced concrete structures. According to various international codes, core samples are drilled and tested to obtain the concrete compressive strengths. Non-destructive testing is an important alternative when destructive testing is not feasible without damaging the structure. The commonly used non-destructive testing (NDT) methods to estimate the in-situ values include the Rebound hammer test and the Ultrasonic Pulse Velocity test. The poor reliability of these tests due to different aspects could be partially contrasted by using both methods together, as proposed.in the SonReb method. There are three techniques that are commonly used to predict the compressive strength of concrete based on the SonReb measurements: computational modeling, artificial intelligence, and parametric multi-variable regression models. In a previous study the accuracy of the correlation formulas deduced from the last technique has been investigated in comparison with the effective compressive strengths based on destructive test results on core drilled in adjacent locations. The aim of this study is to verify the accuracy of Artificial Neural Approach comparing the estimated compressive strengths based on NDT measured parameters with the same effective compressive strengths. The comparisons show the best performance of ANN approach.

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