Determination of coarse aggregate content of concrete specimens by wave propagation and Artificial Neural Network

Abstract

Abstract Nondestructive tests that assess the constitution or degradation of structures are of great interest in Civil Engineering. Among the non-destructive testing techniques, the Ultrasonic Pulse Velocity (UPV) test stands out; however, although its use is widespread, there are still no applications that employ this method to determine the constitution of concrete in situ. Therefore, this article addresses the identification of the coarse aggregate content in concrete specimens by an Artificial Neural Network (ANN) trained with a database of numerical tests that simulated UPV. In this paper, the coarse aggregate content will be described as a percentage of the total area of a two-dimensional concrete model. Three artificial neural network architectures were evaluated. The first two, trained with 13 or 22 paths, solved a classification problem for five aggregate contents, and the third, trained with 22 paths, solved a regression problem. Its performance was compared with those of other regression solutions, namely XGB Regressor, Random Forest, and OLS (Ordinary Least Squares), and showed superior, with -2.55% to +2.17% average deviations. Thus, this paper demonstrated that the use of ANN in combination with UPV test has the potential to identify the coarse aggregate content in concretes. The positive results suggest that this approach is promising and highlights the need for further experimental validation in future research.

Keywords

• nondestructive test
• ultrasonic pulse velocity
• concrete
• machine learning