TY  - JOUR
AV  - none
IS  - 3
N2  - Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R2 of 0.9921, with the percentage error ranging from â??9.39 to 4.63, when compared with FEA results. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
N1  - cited By 18
TI  - Failure pressure prediction of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loadings using fem and ann
ID  - scholars15128
Y1  - 2021///
SN  - 20771312
PB  - MDPI AG
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103011178&doi=10.3390%2fjmse9030281&partnerID=40&md5=fad930a30d6a0a5bc71f0e9379143e9c
JF  - Journal of Marine Science and Engineering
A1  - Lo, M.
A1  - Karuppanan, S.
A1  - Ovinis, M.
VL  - 9
ER  -