PREDICTION OF CONCRETE COMPRESSIVE STRENGTH USING ULTRASONIC PULSE VELOCITY TEST AND ARTIFICIAL NEURAL NETWORK MODELING

Ultrasonic pulse velocity (UPV) test method is used in this study for evaluating the compressive strength of concrete. A series of UPV tests were performed to evaluate the 28-day compressive strength of concrete and examine the effect of concrete mixture parameters on the UPV of concrete. It was found that concrete with higher 28-day compressive strength gives higher UPV and that an exponential relationship exists between the UPV and 28-day compressive strength of concrete. The results showed that the aggregate size has a significant effect on the strength of concrete. Concrete with larger aggregate size was found to give lower UPV and compressive strength. UPV results also indicated that the UPV and compressive strength of concrete consistently decrease with increase in water-cement ratio of concrete. The effect of using microsilica (Silica fume) in concrete is also studied. It was found that as the microsilka to cement ratio increases in concrete, the UPV and compressive strength of concrete increase. The effects of the ingredient materials on UPV were analyzed and potential mechanisms were proposed. To make the results applicable, the artificial neural network (ANN) method was used to predict the compressive strength of concrete based on the evaluated concrete mix parameters and ultrasonic pulse velocity. The ANN analysis demonstrated high reliability in predicting the compressive strength values of concrete.