A Novel Predict Corrosion Rate Model Based on RBFNN

Mohammed Hliyil Hafiz

Abstract


Predication Corrosion rate is quantitative method by which the effectiveness of corrosion control and prevention techniques can be evaluated and provides the feedback to enable corrosion control and prevention methods to be optimized. In this paper, a novel Model to predict corrosion rate based on RBFNN was proposed. A model is produced from experimental work for one year and eighty four specimens were used through this work using anode with a high level of precision. Learning data was performed by using a 36 samples test with different Environment Resistivity (ER), Impressed Current (IC), Location of Anode (LA), Corrosion Current (CC) and Corrosion Rate (CR). The RBFNN model has five input nodes representing the (ER, IC, CC, LN, and SA), sixteen nodes at hidden layer and one output node representing corrosion rate (CR). Simulation test use 6 data samples taken from the experimental results to check the performance of the neural network on these data and shows the proposed model can be use successfully to predicate the corrosion rate.


Full Text: PDF

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Modern Applied Science   ISSN 1913-1844 (Print)   ISSN 1913-1852 (Online)

Copyright © Canadian Center of Science and Education

To make sure that you can receive messages from us, please add the 'ccsenet.org' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.