This study focuses on applying machine learning algorithms to predict the corrosion depth of facility station piping assets, as well as comparing the computational accuracy of the predicted corrosion depth based on various machine learning algorithms. Simulated corrosion testing data of facility piping was fit into the following machine learning algorithms: Gradient Boosting(GBM), Artificial Neural Network (ANN), and Random Forest (RF). K-fold cross validation was used to evaluate the models and grid search was applied for the models to refine and calibrate each model. The variable sensitivity analysis was conducted separately for the external and internal corrosion of station piping, and it assisted in limiting the number of independent variables included in machine learning models. This study compares the performance of corrosion depth prediction models for facility station piping and draws conclusions on model performance based on performance evaluation metrics.

Subject
Artificial neural networks, Piping, Corrosion rate, Corrosion prediction, Machinery, Pipelines, Algorithms, Variability, Corrosion growth, Corrosion modeling, Corrosion data, Computer based testing, Training
Keywords:
Facility Integrity, Station Piping, NDE, Machine Learning, Corrosion Depth Prediction
© 2023 Association for Materials Protection and Performance (AMPP). All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise) without the prior written permission of AMPP. Positions and opinions advanced in this work are those of the author(s) and not necessarily those of AMPP. Responsibility for the content of the work lies solely with the author(s).
2023
Association for Materials Protection and Performance (AMPP)
You do not currently have access to this content.