Prediction models for bond strength of steel reinforcement with consideration of corrosion

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. (c) 2021 Elsevier Ltd. All rights reserved. Second International Conference on Aspects of Materials Science and Engineering (ICAMSE 2021). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

Corrosion significantly impacts the behavior of structural reinforced concrete (RC) members in seismic regions. Research on reducing rehabilitation costs and accurately modeling corrosion-affected RC structures has gained popularity. Artificial neural networks (ANN) show promising results in predicting the performance of corroded reinforcement in concrete members.