TY  - JOUR
PY  - 2021//
TI  - A review of finite element analysis and artificial neural networks as failure pressure prediction tools for corroded pipelines
JO  - Materials (Basel, Switzerland)
A1  - Kumar, Suria Devi Vijaya
A1  - Kai, Michael Lo Yin
A1  - Arumugam, Thibankumar
A1  - Karuppanan, Saravanan
SP  - e6135
EP  - e6135
VL  - 14
IS  - 20
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. Publisher: MDPI<p />  <p>Language: en</p>
LA  - en
SN  - 1996-1944
UR  - http://dx.doi.org/10.3390/ma14206135
ID  - ref1
ER  -