eprintid: 14380
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/43/80
datestamp: 2023-11-10 03:28:57
lastmod: 2023-11-10 03:28:57
status_changed: 2023-11-10 01:56:45
type: article
metadata_visibility: show
creators_name: Kumar, S.D.V.
creators_name: Kai, M.L.Y.
creators_name: Arumugam, T.
creators_name: Karuppanan, S.
title: A review of finite element analysis and artificial neural networks as failure pressure prediction tools for corroded pipelines
ispublished: pub
keywords: Failure (mechanical); Forecasting; Neural networks; Pipeline corrosion; Pipelines, Corroded pipelines; Failure pressure; Failure pressure prediction; Finite element analyse; Prediction tools; Pressure predictions; Residual strength; Strength assessment, Finite element method
note: cited By 7
abstract: 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.
date: 2021
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117502977&doi=10.3390%2fma14206135&partnerID=40&md5=c564274e7c028ab7de66331188bce1c7
id_number: 10.3390/ma14206135
full_text_status: none
publication: Materials
volume: 14
number: 20
refereed: TRUE
issn: 19961944
citation:   Kumar, S.D.V. and Kai, M.L.Y. and Arumugam, T. and Karuppanan, S.  (2021) A review of finite element analysis and artificial neural networks as failure pressure prediction tools for corroded pipelines.  Materials, 14 (20).   ISSN 19961944