TY  - JOUR
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117502977&doi=10.3390%2fma14206135&partnerID=40&md5=c564274e7c028ab7de66331188bce1c7
N1  - cited By 7
A1  - Kumar, S.D.V.
A1  - Kai, M.L.Y.
A1  - Arumugam, T.
A1  - Karuppanan, S.
ID  - scholars14380
Y1  - 2021///
TI  - A review of finite element analysis and artificial neural networks as failure pressure prediction tools for corroded pipelines
IS  - 20
KW  - Failure (mechanical); Forecasting; Neural networks; Pipeline corrosion; Pipelines
KW  -  Corroded pipelines; Failure pressure; Failure pressure prediction; Finite element analyse; Prediction tools; Pressure predictions; Residual strength; Strength assessment
KW  -  Finite element method
N2  - This paper discusses the capabilities of artificial neural networks (ANNs) when integrated with the finite element method (FEM) and utilized as prediction tools to predict the failure pressure of corroded pipelines. The use of conventional residual strength assessment methods has proven to produce predictions that are conservative, and this, in turn, costs companies by leading to premature maintenance and replacement. ANNs and FEM have proven to be strong failure pressure prediction tools, and they are being utilized to replace the time-consuming methods and conventional codes. FEM is widely used to evaluate the structural integrity of corroded pipelines, and the integration of ANNs into this process greatly reduces the time taken to obtain accurate results. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
SN  - 19961944
VL  - 14
JF  - Materials
AV  - none
PB  - MDPI
ER  -