Prediction of Corrosion Rates in Structural Steel Using Artificial Neural Networks
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 2
Abstract
A phenomenal outcome for the prediction of corrosion in steel was proposed with the learning ability of artificial neural network using MATLAB software. The prediction of corrosion rate has become an important challenge for the Indian steel industry as well as for the engineering community. This paper presents the studies carried out towards the prediction of corrosion rates by using artificial neural networks (ANN), in which training of 406 sets of data using Levenberg-Marquardt algorithm obtained from experimental data. The training sets have been developed for three levels of corrosion such as mild, moderate and severe through ANN and resulted in a trend of an incremental parabolic curve. The input parameters considered were equivalent to simulate corrosion of structural steel exposed to atmospheric, marine or chemical environment. The correlation statistics (R) in ANN has proved to be 90%. The test results have been validated to confirm the efficacy of developed ANN model for prediction of corrosion rate.
Authors and Affiliations
Sharon John, Umesha P. K, Cinitha A, M. G. Rajendran
Keywords
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- EP ID EP19585
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