ARTIFICIAL NEURAL NETWORK APPROACH TO PREDICT COMPRESSIVE STRENGTH OF CONCRETE THROUGH ULTRASONIC PULSE VELOCITY

作者：Bilgehan M^*; Turgut P

来源：Research in Nondestructive Evaluation, 2010, 21(1): 1-17.

DOI：10.1080/09349840903122042

• artificial neural networks
• concrete
• compressive strength
• ultrasonic pulse velocity
• non-destructive testing

Plenty of efforts to use ultrasonic pulse velocity (UPV) as a measure of concrete compressive strength have been implemented in the recent years due to obvious advantages of nondestructive testing methods. In this article, an artificial neural network (ANN) approach has been proposed for the evaluation of relationship between concrete compressive strength and UPV values by using the data obtained from many cores taken from different reinforced concrete structures having different ages and unknown ratios of concrete mixtures. The presented approach enables to practically find concrete strengths in the existing reinforced concrete structures, whose records of concrete mixture ratios are not available or present. Thus, researchers can easily evaluate the compressive strength of concrete specimens by using UPV values. The method can also be used in conditions such as too many numbers of the structures and examinations to be done in a restricted time. The comparison of the results clearly shows that the ANN approach can be used effectively to predict the compressive strength of concrete by using UPV values.

• 出版日期2010