A new approach for finite element based reliability evaluation of offshore corroded pipelines

Abyani, Mohsen; Bahaari, Mohammad Reza

doi:10.1016/j.ijpvp.2021.104449

Cited by 15 publications

(6 citation statements)

References 44 publications

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“…According to the Eqs. (5)(6)(7)(8)(9), the core point of the SVM algorithm is to find the appropriate penalty factor C and the appropriate radial basis kernel function parameters  .…”

Section:    ，，mentioning

confidence: 99%

“…Gao J [5] utilized the finite element method (FEM) to conduct a fullscale blasting test and obtained multiple sets of failure pressure data, suggesting that FEM is a viable approach for predicting the failure pressure of corroded pipelines. Abyani M [6] deployed FEM to assess the reliability of a single corrosion defect in a submarine pipeline. However, this method is intricate to construct the model and consumes a significant amount of time, indicating that it cannot be utilized for large-scale corrosion assessments.…”

Section: Introductionmentioning

confidence: 99%

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Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

Yuan,

Zhang,

Ren

2024

AJST

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In this study, the failure pressure of submarine oil and gas pipelines was predicted by using five commonly used machine learning models and derivative models. Firstly, an efficient local time-intensive finite element algorithm is constructed and used to generate a machine learning database. Secondly, in order to improve the accuracy of machine learning algorithm, the frontier optimization algorithm is improved to form a new predictive regression model IAEO-SVM model. To compare the accuracy of commonly used machine learning models and IAEO-SVM models, a comprehensive evaluation standard consisting of k-fold cross-validation and three statistical indicators was used. Finally, the influence of seawater depth and geometric factors of corrosion defects on the blasting pressure of submarine oil and gas pipelines was comprehensively analyzed through the high-dimensional surface built by the IAEO-SVM model. The IAEO-SVM model shows superior stability and accuracy compared to the comparison model, as demonstrated by its MSE’ of 0.0482, R2’ of 0.9982, MAE’ of 0.1295, and SD of 0.2198. The high-dimensional surface obtained through inversion shows a linear relationship between seawater depth and failure pressure. Meanwhile, the width of corrosion defects has a significant impact on failure pressure, accounting for up to 23% and thus cannot be overlooked.

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“…According to the Eqs. (5)(6)(7)(8)(9), the core point of the SVM algorithm is to find the appropriate penalty factor C and the appropriate radial basis kernel function parameters  .…”

Section:    ，，mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

Yuan,

Zhang,

Ren

2024

AJST

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“…Peng et al [14] presented a comprehensive review of pipeline corrosion assessment from the perspective of data analysis and proposed a corrosion growth model aimed at predicting future corrosion states to achieve effective prediction of corrosion rates. Abyani et al [15] proposed a new method for assessing the reliability of pipeline corrosion using finite element simulation, which achieves an effective prediction of corrosion rates. Deif et al [16] uses passive RFID sensors to collect field data and then integrates them into a building information modeling (BIM) system to enable effective prediction of corrosion rates.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm

Cui

Liu

et al. 2022

Axioms

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Pipeline corrosion prediction (PCP) is an important technology for pipeline maintenance and management. How to accurately predict pipeline corrosion is a challenging task. To address the drawback of the poor prediction accuracy of the grey model (GM(1,1)), this paper proposes a method named ETGM(1,1)-RABC. The proposed method consists of two parts. First, the exponentially transformed grey model (ETGM(1,1)) is an improvement of the GM(1,1), in which exponential transformation (ET) is used to preprocess the raw data. Next, dynamic coefficients, instead of background fixed coefficients, are optimized by the reformative artificial bee colony (RABC) algorithm, which is a variation of the artificial bee colony (ABC) algorithm. Experiments are performed on actual pipe corrosion data, and four different methods are included in the comparative study, including GM(1,1), ETGM(1,1), and three ETGM(1,1)-ABC variants. The results show that the proposed method proves to be superior for the PCP in terms of Taylor diagram and absolute error.

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“…Zelmati et al [30] proposed a probabilistic method to evaluate the corroded pipeline's remaining life, and FEM was used to calculate the pipeline's failure pressure. Based on FEM, Abyani et al [1] proposed a new method to evaluate the reliability of pipeline corrosion resistance. Then, the Latin hypercube sampling method was combined with simulated annealing to simulate the uncertainty of the random parameter.…”

Section: Introductionmentioning

confidence: 99%

“…This study is to evaluate buried pipeline's reliability by considering the coupling relationship among the aforementioned factors. The major contributions are as follows: (1) The effect of rockfall parameters on the pipeline's deformation is analyzed, including rockfall mass, impact velocity, and impact position. Meanwhile, the coupling relationship between the von Mises equivalent stress and rockfall parameters is quantitatively analyzed; (2) Sensitivity analyses are conducted for the rockfall parameters, and dynamic reliability model is established to update the pipeline's reliability; (3) The pipeline's dynamic reliability during and after the rockfall impact are studied.…”

Section: Introductionmentioning

confidence: 99%

Dynamic reliability evaluation of buried corroded pipeline under rockfall impact

Wang¹,

Xie²,

Su³

2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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Load impact, such as the rockfall, may bring significant threats to the integrity management of pipeline. This study is intended to evaluate the reliability of buried pipeline under rockfall impact, and so as to reduce the possible failure and unnecessary downtime. Firstly, the dynamic response of the buried pipeline under load is analyzed by Euler Bernoulli foundation beam. After that, the process of rockfall impact on buried corroded pipeline is simulated with nonlinear finite element method. Furthermore, the influence of rockfall’s parameters (including rockfall mass, impact velocity, impact position, etc.) on the pipeline’s equivalent stress is quantitatively analyzed. Eventually, a time-varying reliability model is established to calculate the failure probability. The results indicate that the mass and velocity of the rockfall have obvious influence on the pipeline’s failure probability, and the change of impact’s position has small influence. The proposed method can provide a theoretical reference for the design and maintenance of buried pipeline.

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A new approach for finite element based reliability evaluation of offshore corroded pipelines

Cited by 15 publications

References 44 publications

Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm

Dynamic reliability evaluation of buried corroded pipeline under rockfall impact

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