Citation

Kumar SDV, Kai MLY, Arumugam T, Karuppanan S. Materials (Basel) 2021; 14(20): e6135.

Copyright

(Copyright © 2021, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ma14206135

PMID

unavailable

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. Publisher: MDPI

Language: en

Keywords

Neural networks; Forecasting; Finite element method; Pipeline corrosion; Pipelines; Failure (mechanical)