Modified response surface method basis harmony search to predict the burst pressure of corroded pipelines

Keshtegar, Behrooz; Seghier, Mohamed El Amine Ben

doi:10.1016/j.engfailanal.2018.02.016

Cited by 102 publications

(22 citation statements)

References 51 publications

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“…These indicators include the mean bias error ( MBE ), the root mean square error ( RMSE ), the mean absolute error ( MAE ), the Nash–Sutcliffe efficiency ( NSE ), the Willmott index of agreement ( d ), confidence index ( CI ) and the coefficient of determination ( R 2 ). Equations 17–23 express the formulas of those indicators as follows 52,53 :

italicMBE = \frac{1}{n} \sum_{i = 1}^{n} {({italicSIF}_{i} - \overset{true^}{{SIF}_{i}})}^{2},

italicRMSE = \sqrt{\frac{1}{n} \sum_{i = 1}^{n} {({italicSIF}_{i} - \overset{true^}{{SIF}_{i}})}^{2},}

italicMAE = \frac{1}{n} \sum_{i = 1}^{n} (||, {SIF}_{i} - \overset{{italicSIF}_{i}}{true}),

italicNSE = 1 - \frac{\sum_{i = 1}^{n} {({italicSIF}_{i} - \overset{true^}{{SIF}_{i}})}^{2}}{\sum_{i = 1}^{n} {({italicSIF}_{i} - \overset{true¯}{{SIF}_{i}})}^{2}} - \infty \leq italicNSE \leq 1,

d = 1 - \frac{\sum_{i = 1}^{n} {({italicSIF}_{i} - \overset{true^}{{SIF}_{i}})}^{2}}{\sum_{i = 1}^{n} (||)}

…”

Section: Methodsmentioning

confidence: 99%

Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor

Seghier

Carvalho

Keshtegar

et al. 2020

Fatigue Fract Eng Mat Struct

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The aim of this study is to develop a new framework for the prediction of stress intensity factor (SIF) using newly developed hybrid artificial intelligence (AI) models. To do so, an adaptive neuro-fuzzy inference system optimized by two meta-heuristic algorithms as genetic algorithm (ANFIS-GA) and particle swarm optimization (ANFIS-PSO) is proposed. Moreover, a database composed of 150 SIF values obtained using the finite element method (FEM) calculations is used for training and validating the two proposed AI models. The efficiency and accuracy of the proposed AI models were investigated through several assessment criteria. Results showed the outperformance of the ANFIS-PSO model for accurate prediction of SIF values with R 2 = 0.9913, root mean square error (RMSE) = 23.6 and mean absolute error (MAE) = 18.07, whereas both AI models indicate a robust performance in the presence of input variability. Overall, the performed study provides a hybrid AI framework that can serve as an efficient numerical tool for SIF prediction and analysis.

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italicMBE = \frac{1}{n} \sum_{i = 1}^{n} {({italicSIF}_{i} - \overset{true^}{{SIF}_{i}})}^{2},

italicRMSE = \sqrt{\frac{1}{n} \sum_{i = 1}^{n} {({italicSIF}_{i} - \overset{true^}{{SIF}_{i}})}^{2},}

italicMAE = \frac{1}{n} \sum_{i = 1}^{n} (||, {SIF}_{i} - \overset{{italicSIF}_{i}}{true}),

italicNSE = 1 - \frac{\sum_{i = 1}^{n} {({italicSIF}_{i} - \overset{true^}{{SIF}_{i}})}^{2}}{\sum_{i = 1}^{n} {({italicSIF}_{i} - \overset{true¯}{{SIF}_{i}})}^{2}} - \infty \leq italicNSE \leq 1,

d = 1 - \frac{\sum_{i = 1}^{n} {({italicSIF}_{i} - \overset{true^}{{SIF}_{i}})}^{2}}{\sum_{i = 1}^{n} (||)}

…”

Section: Methodsmentioning

confidence: 99%

Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor

Seghier

Carvalho

Keshtegar

et al. 2020

Fatigue Fract Eng Mat Struct

Self Cite

View full text Add to dashboard Cite

show abstract

“…robabilistic methods are widely used when evaluating the structural performance of pipeline due to theirs advantages [13,14]. Comparing to deterministic method or assessment again target reliability using calibration safely factor, it gives many advantages.…”

Section: Reliability Methodsmentioning

confidence: 99%

Effect of shape factor on structural reliability analysis of a surface cracked pipeline-parametric study

Guillal

Abdelbaki

Bensghier

et al. 2019

Frattura Ed Integrità Strutturale

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The variation of crack shape factor (a/c) during propagation has an important role on calculation of the stress intensity factor (SIF), where imprecise estimation of this latter can lead us to an inaccurate prediction of failure probabilities. For the case of external cracked surface pipeline carried oil and gas products, such estimation can results undesired fault decisions like excessive repairs action or inspection planning. In this paper, the structural integrity analysis of pipeline with semi elliptical crack on the external surface is evaluated. Reliability calculations expressed in term of reliability index β are carried out based on Monte Carlo simulation and First Order reliability Method (FORM). The crack shape factors are varied to cover a variety of geometries from shallow to deep cracks while fatigue crack growth is assumed in depth direction. For SIF estimation, surface and deep point are considered. Results of the analysis indicate that for a constant crack depth a/t, the reliability index is strongly affected by a/c ratio and the trend is not similar in deep and surface point for different crack shapes.

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“…To the authors' knowledge, academic and industry literature use almost surely the aforementioned approaches to predict burst pressures. Other approaches that were not included in this review because they are specific for girth welds (see the review in [21]), they combine external forces [87] or they require data training [21]. For instance, Keshtegar & el Amine Ben Seghier recently proposed a model using modified response surface methodology based on harmony search [21].…”

Section: Burst Model Comparisonmentioning

confidence: 99%

“…Other approaches that were not included in this review because they are specific for girth welds (see the review in [21]), they combine external forces [87] or they require data training [21]. For instance, Keshtegar & el Amine Ben Seghier recently proposed a model using modified response surface methodology based on harmony search [21]. This approach predicts the burst pressure by implementing non-linear polynomial basic functions, which minimize the mean square errors from records of a test dataset.…”

Section: Burst Model Comparisonmentioning

confidence: 99%

“…However, additional approaches to describe plastic deformation are also discussed. In comparison to other relevant reviews [17][18][19][20][21][22][23], this paper aims to evaluate the main available approaches concerning their level of conservatism subjected to the assumptions of each model; therefore, the models will be described in detail with a homogeneous nomenclature. The document is structured as follows: Section 2 describes an interesting yield criterion based on the longitudinal and circumferential stresses and the Von Mises criterion.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reliability assessments of corroded pipelines based on internal pressure – A review

Amaya-Gómez

Sánchez-Silva

Bastidas‐Arteaga

et al. 2019

Engineering Failure Analysis

144

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Corrosion is one of the most significant threats for onshore pipelines that may lead to a Loss of Containment (LOC). A LOC poses significant consequences over the surrounding people and environment because of the hazardousness of the transporting fluids, so different efforts have been raised to predict pipe failures, which are commonly based on reliability assessments with limit state functions. These functions are gathered in serviceability, leakage, and ultimate conditions, out of which the last two approaches contemplate a LOC. This paper reviews recognized limit state functions for corroded pipelines, and it discusses their assumptions and applicability. Specifically, this paper focuses on burst limit pressures considering the relevance in the academic literature and Oil & Gas standards. Therefore, a thorough comparison is presented based on failure criteria, acceptable defect dimensions, failure probability, and error prediction based on experimental and numerical burst tests. The objective is to evaluate the level of conservatism of each simplified model depending on the material toughness and the corrosion rate. This review aims to support a reliability model selection in corroded pipelines for future intervention strategies.

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Modified response surface method basis harmony search to predict the burst pressure of corroded pipelines

Cited by 102 publications

References 51 publications

Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor

Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor

Effect of shape factor on structural reliability analysis of a surface cracked pipeline-parametric study

Reliability assessments of corroded pipelines based on internal pressure – A review

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