relation: https://khub.utp.edu.my/scholars/19490/
title: Application of Artificial Neural Network for Failure Pressure Prediction of Pipeline with Circumferential Groove Corrosion Defect
creator: Devi Vijaya Kumar, S.
creator: Karuppanan, S.
creator: Ovinis, M.
description: This paper describes the application of artificial neural network (ANN) to develop a corrosion assessment equation for predicting the failure pressure of pipeline with circumferential groove corrosion defect.Finite element analysis (FEA) was utilised to obtain the failure pressure of pipeline for various defect depths and defect length.The FEA results were used to train the ANN model that consisted of three inputs that are true ultimate tensile strength, normalised defect depth, and normalised defect length while the output of the model was the normalised failure pressure of the pipeline.The weights and biases of the ANN model was used to develop a new equation to predict the failure pressure of a pipe with circumferential groove corrosion defect subjected to internal pressure only. © 2023, Institute of Technology PETRONAS Sdn Bhd.
date: 2023
type: Article
type: PeerReviewed
identifier:   Devi Vijaya Kumar, S. and Karuppanan, S. and Ovinis, M.  (2023) Application of Artificial Neural Network for Failure Pressure Prediction of Pipeline with Circumferential Groove Corrosion Defect.  Lecture Notes in Mechanical Engineering.  pp. 939-954.      
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140762943&doi=10.1007%2f978-981-19-1939-8_70&partnerID=40&md5=d9f963e6d666cd8048b3b8a962cdc57a
relation: 10.1007/978-981-19-1939-8₇₀
identifier: 10.1007/978-981-19-1939-8₇₀