The commonly used NDT methods to predict concrete compressive strength include the rebound hammer test and the Ultrasonic Pulse Velocity (UPV) test. The poor reliability of rebound hammer and ultrasonic pulse velocity due to different aspects could be partially contrasted by using both methods together, as proposed.in the SonReb method, developed by RILEM Technical Committees 7 NDT and TC-43 CND. There are three techniques that are commonly used to predict fc based on the SonReb measurements: computational modeling, artificial intelligence, and parametric multi-variable regression models. The aim of this study is to verify the accuracy of some reliable parametric multi-variable regression models and ANN approach comparing the estimated compressive strength based on NDT measured parameters with the effective compressive strength based on DT results on core drilled in adjacent locations. The comparisons show the best performance of ANN approach.

Parametric Regression Model and ANN (Artificial Neural Network) Approach in Predicting Concrete Compressive Strength by SonReb Method

NOBILE, LUCIO;BONAGURA, MARIO
2016

Abstract

The commonly used NDT methods to predict concrete compressive strength include the rebound hammer test and the Ultrasonic Pulse Velocity (UPV) test. The poor reliability of rebound hammer and ultrasonic pulse velocity due to different aspects could be partially contrasted by using both methods together, as proposed.in the SonReb method, developed by RILEM Technical Committees 7 NDT and TC-43 CND. There are three techniques that are commonly used to predict fc based on the SonReb measurements: computational modeling, artificial intelligence, and parametric multi-variable regression models. The aim of this study is to verify the accuracy of some reliable parametric multi-variable regression models and ANN approach comparing the estimated compressive strength based on NDT measured parameters with the effective compressive strength based on DT results on core drilled in adjacent locations. The comparisons show the best performance of ANN approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/576979
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