TY  - JOUR
Y1  - 2023///
EP  - 954
UR  - 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
JF  - Lecture Notes in Mechanical Engineering
A1  - Devi Vijaya Kumar, S.
A1  - Karuppanan, S.
A1  - Ovinis, M.
AV  - none
KW  - Defects; Failure (mechanical); Forecasting; Pipeline corrosion; Pipelines; Tensile strength
KW  -  Artificial neural network modeling; Corrosion assessment; Corrosion defect; Defect depth; Defect length; Failure pressure; Finite element analyse; Internal pressures; Pressure predictions; Ultimate tensile strength
KW  -  Neural networks
SP  - 939
ID  - scholars19490
TI  - Application of Artificial Neural Network for Failure Pressure Prediction of Pipeline with Circumferential Groove Corrosion Defect
N2  - 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.
N1  - cited By 0; Conference of 7th International Conference on Production, Energy and Reliability, ICPER 2020 ; Conference Date: 14 July 2020 Through 16 July 2020; Conference Code:284729
ER  -