Abstract
Corrosion phenomena is one of the main deterioration causes, which remarkably affects the behavior of structural reinforced concrete (RC) members in seismic regions. Researches on reducing rehabilitation cost, performance assessment, and accurate modelling of corrosion-affected RC structures are progressively becoming popular in recent years. Corrosion diminishes bond capacity between reinforcement and surrounding concrete, which induces reduction in strength and ductility of members. The aim of this investigation is to provide a prediction approach based on a large number of results from published researches related to corroded reinforcement in concrete members using artificial neural networks (ANN). The minimizing mean square error criterion and increasing regression value of predicted results are considered for evaluation of training performance of ANN models. The validity of proposed model is checked using collected experimental database. Results show that estimated model has acceptable agreement with experimented data.
Highlights
Based on the design codes and guidelines, strain in steel reinforcement of reinforced concrete (RC) elements should be the same as that in the adjacent concrete
The present study proposed a new approach to estimate the average value of bond capacity between corroded reinforcement and surrounding concrete by gathering wide range of experimental results using artificial neural networks (ANN) method
Some analytical, empirical, and numerical bond models are proposed for determining the residual bond strength between concrete and corroded reinforcement [20,21,22,23,24,25,26,27,28]
Summary
Based on the design codes and guidelines, strain in steel reinforcement of reinforced concrete (RC) elements should be the same as that in the adjacent concrete. Aggressive environment, as one of the main reasons of corrosion, may result in a large deterioration of RC structural elements and decreases their bond strength. Carbonation is the main source of uniform corrosion, which can cause concrete cover cracking, loss in bond strength and anchorage between concrete and reinforcements [6,7,8]. For the above-mentioned reasons, it is crucial to better understand and characterize the effects of reinforcement corrosion on the deterioration of interfacial bond capacity between reinforcement and adjacent concrete. The present study proposed a new approach to estimate the average value of bond capacity between corroded reinforcement and surrounding concrete by gathering wide range of experimental results using ANN method. The results of this study could utilize directly to improve modelling and assessing of existing RC structures with considering corrosion effects
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