Bond property between the reinforcing steel and concrete is influenced by many factors (such as corrosion ratio of reinforcing steel, concrete strength, depth of concrete cover, reinforcement diameter, reinforcement type, etc.), which make it difficult to build any calculation model. Based on the test results, artificial neural network (ANN) was introduced into the prediction of bond property between the corroded reinforcement and concrete in this paper. Considering single factor and multi-factors, BP neural network (BPNN) models were established respectively. It is shown that satisfactory results can be achieved by the given BP models, whereas need no mathematical model. Therefore, ANN will break a new approach for the study on mechanical property of corroded reinforced concrete structure, which will provide a simple calculation method for engineering in practice as well.
['Yingfang Fan', 'Zhiqiang Hu']
Application of Artificial Neural Network in Prediction of Bond Property between Corroded Reinforcement and Concrete