Investigation of the effects of corrosion on bond strength of steel in concrete using neural network

Corrosion of steel reinforcement due to hostile environments is regarded as one vital structural health concerns in concrete structures. Specifically, the development of corrosion affects the necessary bond strength of rebar in concrete contributing to the loss of resilience and possible structural failures. It is thus essential to understand the effects of corrosion on bond strength so that remedial measures can be done on existing and deteriorating RC structures. Hence, this study investigated through laboratory experiments and Artificial Neural Network (ANN) modeling the effects of corrosion on bond strength. Experimental results showed that at small amounts of corrosion less than 0.27%, the bond strength was observed to increase. At these levels, the amounts of corrosion products were sufficient enough to expand freely through the permeable structure of concrete and occupy the pore spaces. Beyond this level, however, the bond strength of concrete deteriorated significantly. There was an observed average decrease of 1.391 MPa in the bond strength values for every percent increase in the amount of corrosion. The expansive and progressive internal radial stress due to corrosion resulted to the development of internal and surface cracks in concrete. In the parametric investigation of the derived ANN model, the bond strength was also observed to decline continuously with the growth of corrosion derivatives as represented by the relative magnitudes of the ultrasonic pulse velocity (UPV). The prediction results of the model can be utilized as basis for design and select appropriate mitigating measures to prolong the service life of concrete structures.