Title :
Application of Artificial Neural Network in Prediction of Bond Property between Corroded Reinforcement and Concrete
Author :
Fan, Yingfang ; Hu, Zhiqiang
Author_Institution :
Dalian Maritime Univ., Dalian
Abstract :
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.
Keywords :
concrete; mechanical engineering computing; neural nets; artificial neural network; concrete; corroded reinforced concrete structure; corroded reinforcement; Artificial neural networks; Bonding; Concrete; Corrosion; Cyclic redundancy check; Laboratories; Mathematical model; Neural networks; Steel; Testing;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2874-8
DOI :
10.1109/FSKD.2007.177