eprintid: 16989
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/69/89
datestamp: 2023-12-19 03:23:27
lastmod: 2023-12-19 03:23:27
status_changed: 2023-12-19 03:07:15
type: article
metadata_visibility: show
creators_name: Kumar, S.D.V.
creators_name: Karuppanan, S.
creators_name: Ovinis, M.
title: Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings
ispublished: pub
keywords: Compressive stress; Defects; Failure (mechanical); Finite element method; Forecasting; Pipeline corrosion; Pipelines, Combined loading; Corroded pipe; Corrosion assessment; Corrosion assessment method; Corrosion defect; Failure pressure; Finite element analyse; Network-based; Pressure predictions; X-80 pipeline, Neural networks
note: cited By 10
abstract: Conventional pipeline corrosion assessment methods produce conservative failure pressure predictions for pipes under the influence of both internal pressure and longitudinal compressive stress. Numerical approaches, on the other hand, are computationally expensive. This work provides an assessment method (empirical) for the failure pressure prediction of a high toughness corroded pipe subjected to combined loading, which is currently unavailable in the industry. Additionally, a correlation between the corrosion defect geometry, as well as longitudinal compressive stress and the failure pressure of a pipe based on the developed method, is established. An artificial neural network (ANN) trained with failure pressure from FEA of an API 5L X80 pipe for varied defect spacings, depths, defect lengths, and longitudinal compressive loads were used to develop the equation. With a coefficient of determination (R2 ) of 0.99, the proposed model was proven to be capable of producing accurate predictions when tested against arbitrary finite element models. The effects of defect spacing, length, and depth, and longitudinal compressive stress on the failure pressure of a corroded pipe with circumferentially interacting defects, were then investigated using the suggested model in a parametric analysis. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127602135&doi=10.3390%2fma15062259&partnerID=40&md5=53e4fc4362757d4923fbc20e2ef5ab5c
id_number: 10.3390/ma15062259
full_text_status: none
publication: Materials
volume: 15
number: 6
refereed: TRUE
issn: 19961944
citation:   Kumar, S.D.V. and Karuppanan, S. and Ovinis, M.  (2022) Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings.  Materials, 15 (6).   ISSN 19961944