Concrete Compressive Strength Prediction Using Neural Networks Based on Non-destructive Tests and a Self-calibrated Response Surface Methodology

Abstract

An artificial neural network (ANN) model and response surface methodology (RSM) were established to estimate the compressive strength of concrete by using the combination of three non-destructive tests (NDT); rebound number, pulse velocity tests and resistance surface. These techniques are utilized in an attempt to increase the reliability of the non-destructive tests in detecting the strength of concrete. These methods were trained using a set of different mixes and at different ages of concrete specimens. In this case, 180 experimental specimens were conducted and their data are published. Then, different neural network topologies and algorithms besides RSM were examined using the given data. The published models are for two combination including the combination of UPV and RN and the combination UPV, RN and SR. The results show that the accuracy of the published models are increased by aging. In addition, it is showed that RSM don’t need calibration process, while its accuracy is enough. Hence, RSM is a promising method to conduct on NDTs and compressive strength prediction, while ANN needs to perform many times to find the best accuracy.