Nondestructive Testing (NDT) techniques are useful tools for analyzing reinforced concrete (RC) structures. The use of Ultrasonic Pulse Velocity (UPV) measurements enables monitoring changes in some critical characteristics of concrete over the service life of a structure. Nonetheless, the current techniques for UPV data analysis are largely based on the sensitivity of the professionals who apply these tests. For accurate diagnosis it is necessary to consider the different factors and conditions that can affect the results. In order to properly control and inspect RC facilities it is essential to develop appropriate strategies to make the task of data interpretation easier and more accurate. This study is based on the idea that using Artificial Neural Networks (ANNs) is a feasible way to generate workable estimation models correlating concrete characteristics, density and compressive strength. The study shows that this goal is achievable and indicates that neural models perform better than traditional statistical models.
artificial neural networks; nondestructive testing; concrete compressive strength estimate