AN IMPROVED PREDICTION MODEL FOR BOND STRENGTH OF DEFORMED BARS IN RC USING UPV TEST AND ARTIFICIAL NEURAL NETWORK

Abstract

The composite action of reinforcement in the surrounding concrete involve a complex and nonlinear mechanism. Inadequate understanding of the underlying interactions may lead to designs with
insufficient amount of bond resistance of reinforcing bars in concrete structures. To investigate the effects of
various parameters on the bond strength of steel bars in concrete, 54 cube samples with varying embedded
reinforcements and strengths were prepared. The samples were cured for 28 days and tested using ultrasonic
pulse velocity (UPV) test for sample homogeneity and single pull out test for bond strength. Data gathered in
the experiment were used in the development of bond strength model as a function of compressive strength,
concrete cover to rebar diameter ratio, embedment length, and UPV using artificial neural network (ANN). Of
all the bond strength models considered from various literatures, the neural network model provided the most
satisfactory prediction results in good agreement with the bond strength values obtained from the experiment.
The UPV parameter was found to be one of the most significant predictors in the neural network model having
a relative importance of 20.57%. This suggest that the robust prediction performance of the bond model was
attributed to this essential component of the model. The proposed model of this study can be used as baseline
information and rapid non-destructive assessment for zone wise strengthening in reinforced concrete.