The assessment of the compressive strength and thickness of concrete structures using nondestructive testing and an artificial neural network

Abstract

This study focuses on the prediction of concrete compressive strength and the unknown thickness of concrete structures as a partial development of a concrete assessment system. The nondestructive tests (NDTs), impact-echo method and spectral analysis of surface wave method were applied to predict concrete compressive strength for the correlation between NDT results and cylinder tests results. The concrete strength prediction and the measurement of thickness were effectively achieved by using an artificial neural network technology. As actual problems were tested in the neural network system, good agreement between the results from the cylinder test and the results from the neural network run was achieved. The accuracy in measuring the thickness of the specimen was successfully achieved using the same technology. The result of this study is a basic algorithm for the automation of predicting the compressive strength and the thickness of concrete member. Automation of these results can contribute to predict the accurate form removal time and unknown slab thickness in building construction practice.