Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks

Abstract

Ultrasonic pulse velocity technique is one of the most popular non-destructive techniques used in the assessment of concrete properties. However, it is very difficult to accurately evaluate the concrete compressive strength with this method since the ultrasonic pulse velocity values are affected by a number of factors, which do not necessarily influence the concrete compressive strength in the same way or to the same extent. This paper deals with the analysis of such factors on the velocity–strength relationship. The relationship between ultrasonic pulse velocity, static and dynamic Young’s modulus and shear modulus was also analyzed. The influence of aggregate, initial concrete temperature, type of cement, environmental temperature, and w/c ratio was determined by our own experiments. Based on the experimental results, a numerical model was established within the Matlab programming environment. The multi-layer feed-forward neural network was used for this purpose. The paper demonstrates that artificial neural networks can be successfully used in modelling the velocity–strength relationship. This model enables us to easily and reliably estimate the compressive strength of concrete by using only the ultrasonic pulse velocity value and some mix parameters of concrete.