Corrosion fatigue crack growth modelling for subsea pipeline steels

Cheng, Ankang; Chen, Nian-Zhong

doi:10.1016/j.oceaneng.2017.06.057

Cited by 49 publications

(16 citation statements)

References 28 publications

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“…, k, the covariant of the PCA transformed training input variables-x (np) and θ k , which is independent and identically distributed (iid) random vectors of the model parameter. The RF predictor uses unweighted averages over the collector (ĥ) shown in Equation (18) to generalize the prediction by minimizing the loss function and avoiding overfitting via the convergence of Equation (19) [40]:ĥ…”

Section: Pca-random Forest (Rf) Algorithmmentioning

confidence: 99%

“…They proposed a non-deterministic artificial intelligence method that estimated the corrosion at different sections of the pipeline. Many other investigations on the use of artificial intelligence in the estimation of the corrosion defect growth of carbon steel materials used for pipelines also abound in the literature [14][15][16][17][18][19][20][21][22][23]. Notable among these studied is the work on the fatigue crack growth where ANN was employed to investigate the corrosion-fatigue crack of a dual phase steel at different stress intensities inconsideration of the martensite content of 32-76% [23].…”

Section: Introductionmentioning

confidence: 99%

“…However, these authors concentrated on the length of crack and cyclic loading time of the pipeline during operation. Other research papers [14][15][16][17][18] also used machine learning strategies for establishing the corrosion defect growth of pipelines in varying conditions of operations.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Machine Learning Approach for Corrosion Risk Assessment—A Comparative Study

Ossai

2019

BDCC

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Understanding the corrosion risk of a pipeline is vital for maintaining health, safety and the environment. This study implemented a data-driven machine learning approach that relied on Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), Feed-Forward Artificial Neural Network (FFANN), Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) to estimate the corrosion defect depth growth of aged pipelines. By modifying the hyperparameters of the FFANN algorithm with PSO and using PCA to transform the operating variables of the pipelines, different Machine Learning (ML) models were developed and tested for the X52 grade of pipeline. A comparative analysis of the computational accuracy of the corrosion defect growth was estimated for the PCA transformed and non-transformed parametric values of the training data to know the influence of the PCA transformation on the accuracy of the models. The result of the analysis showed that the ML modelling with PCA transformed data has an accuracy that is 3.52 to 5.32 times better than those carried out without PCA transformation. Again, the PCA transformed GBM model was found to have the best modeling accuracy amongst the tested algorithms; hence, it was used for computing the future corrosion defect depth growth of the pipelines. This helped to compute the corrosion risks using the failure probabilities at different lifecycle phases of the asset. The excerpts from the results of this study indicate that my technique is vital for the prognostic health monitoring of pipelines because it will provide information for maintenance and inspection planning.

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Section: Pca-random Forest (Rf) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Machine Learning Approach for Corrosion Risk Assessment—A Comparative Study

Ossai

2019

BDCC

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“…Corrosion is considered a significant threat which can affect severely the pipeline's integrity. Regarding corrosion effects, several models are used by researchers to study it (Netto 2009;Vanaei et al 2017;Cheng and Chen 2017;Chiodo and Ruggieri 2009;Majid et al 2010Majid et al , 2012Zhou and Huang 2012;Zhu and Leis 2012;Ma et al 2013;Fekete and Varga 2012;Alamilla et al 2013;Abdalla Filho et al 2014;Khelif 2013, 2015) but the power model constitutes one of the most empirical models which is used to describe the corrosion evolution in time such as uniform corrosion, localized corrosion and corrosion fatigue, or even time-dependent for fracture toughness estimation (Romanov 1957;Li and Mahmoodian 2013;Katano et al 2003). Several works applied the power corrosion model as it is considered indispensable for understanding the corrosion behavior (CSA 2007;DNV 2004;Lee and Pyun 2002;Lee 2005).…”

Section: Corrosion Modelmentioning

confidence: 99%

Numerical model for estimating time-dependent reliability of a corroding pipeline over its lifetime

Nahal

Khelif

2018

Int J Adv Struct Eng

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This work aims to evaluate time-dependent reliability of a pipeline under corrosion impact over its lifetime. A finite element corrosion model was proposed, and an empirical power low model is also used and coupled with a probabilistic model for evaluating reliability index about a limit state function. The failure probability of structure was determinate for deferent corrosion rate (low, moderate and high rates), considering corrosion depth. Form method and Monte Carlo simulation are used for evaluating the structure reliability. The impact of applying both effect of corrosion and residual stress is shown which is appears a significant failure probability of the studied pipeline. The found results are analyzed and discussed.

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“…While at high pH medium, the hydrogen-based mechanism was dominant. Cheng et al [8] established a two-component model considering anode dissolution and hydrogen embrittlement based on the fracture mechanics theory, and they applied the model for API 5L X65 pipeline steel. The results showed that the model can control the shape of the simulated crack growth curve and the fatigue crack growth (FCG) rate.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Corrosive Fatigue Life of Submarine Pipelines of API 5L X56 Steel Materials

et al. 2019

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Corrosive fatigue failure of submarine pipelines is very common because the pipeline is immersed in a sea environment. In Bohai sea, many old pipelines are made of API 5L X56 steel materials, and it is necessary to provide an accurate method for predicting the residual life of these pipelines. As Paris law has been proven to be reliable in predicting the fatigue crack growth in metal materials, the two constants in Paris law for API 5L X56 steel materials are obtained by using a new proposed shape factor based on the analysis of experimental data measured from fatigue tests on compact tension specimens immersed in the water of Bohai sea. The results of the newly proposed shape factor show that, for a given stress intensity factor range (ΔK), the fatigue crack growth rate (da/dN) in seawater is 1.6 times of that that in air. With the increase of fatigue crack growth rate, the influence of seawater on corrosive fatigue decreases gradually. Thereafter, a finite element model for analyzing the stress intensity factor of fatigue crack in pipelines is built, and the corrosive fatigue life of a submarine pipeline is then predicted according to the Paris law. To verify the presented method, the fatigue crack growth (FCG) behavior of an API 5L X56 pipeline with an initial crack under cyclic load is tested. Comparison between the prediction and the tested result indicates that the presented method is effective in evaluating the corrosive fatigue life of API 5L X56 pipelines.

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Corrosion fatigue crack growth modelling for subsea pipeline steels

Abstract: This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence Newcastle University ePrints-eprint.ncl.ac.uk Cheng A, Chen NZ. Corrosion fatigue crack growth modelling for subsea pipeline steels.

Cited by 49 publications

References 28 publications

A Data-Driven Machine Learning Approach for Corrosion Risk Assessment—A Comparative Study

A Data-Driven Machine Learning Approach for Corrosion Risk Assessment—A Comparative Study

Numerical model for estimating time-dependent reliability of a corroding pipeline over its lifetime

Prediction of Corrosive Fatigue Life of Submarine Pipelines of API 5L X56 Steel Materials

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