Reliability assessment of subsea pipelines under the effect of spanning load and corrosion degradation

Seghier, Mohamed El Amine Ben; Mustaffa, Zahiraniza; Zayed, Tarek

doi:10.1016/j.jngse.2022.104569

Cited by 23 publications

(8 citation statements)

References 44 publications

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“…The First/Second Order Reliability Methods (FORM/SORM) are two analytical reliability methods that use the linear and quadratic approximations of the LSF to calculate the reliability index, which is then converted to failure probability. The efficiency of these approaches is determined by the complexity of the LSF (e.g., more applicable to less expensive and linear LSF) and the distribution of the random variables (e.g., FORM is applied when the variables follow a normal distribution) (Ben Seghier et al., 2020, 2022). Apart from FORM/SORM, First Order Second Moment Approximation (FOSM) is another approach that has been used to model the failure probability of water pipes.…”

Section: Physical Modelsmentioning

confidence: 99%

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Taiwo

Seghier

Zayed

2023

Water Resources Research

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Water distribution networks (WDNs) are undoubtedly a critical part of water utility assets, as they are responsible for water transmission (Atef et al., 2016;Berardi et al., 2014). It has been reported that, on average, 80% of water utility expenditures are spent on the management of WDNs (Poulakis et al., 2003). However, available evidence shows that the failure of WDNs is increasing at a fast rate, which negatively impacts individuals' social, economic, and health status (Steffelbauer et al., 2022;Yazdani & Jeffrey, 2012).In the USA and Canada, around 700 water pipes fail daily, thereby contributing to the loss of over 2 trillion gallons of clean water annually (Fan et al., 2022). In 2017, more than 2.2 billion m 3 of water was loss in China (Liao et al., 2021). The enormous financial commitment associated with the failure of water distribution networks cannot be overemphasized. According to the American Water Work Association (AWWA), the USA needs to invest around $1 trillion in replacing and repairing the deteriorating components of their WDNs (Fan et al., 2022). In Australia, the estimated cost of repairing and maintaining WDNs is about AUD 1.4 billion (Weeraddana et al., 2020). In South Korea, 52.5% of the water pipes will require rehabilitation by 2024, as indicated in a study by Seo et al. (2015). In the case of Colombia, a developing country, approximately 50% of water is lost due to water pipe failures (Giraldo-González & Rodríguez, 2020). From the aforementioned, the failure of WDNs, which mostly consist of water pipes, is a global issue that requires utmost attention. WDNs are founded underground; therefore, these infrastructures need to be designed to resist traffic, soil, internal, and overburden pressures, and other environment and operation related loads (Berardi et al., 2008). In essence, the reliability of WDNs must not be compromised during their service life; hence, catastrophic failure

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Section: Physical Modelsmentioning

confidence: 99%

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Taiwo

Seghier

Zayed

2023

Water Resources Research

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“…For example, the data measured by nondestructive testing have large errors and online monitoring can only monitor some local points. Different corrosion management models based on simulated and experimental data have been developed, but the models have uncertainties in judging the trends and expected timing of corrosion defect growth (Ben Seghier et al , 2022; Guo et al , 2012; Li et al , 2022). In recent years, researchers have turned to the use of AI as a viable tool for establishing pipeline corrosion life.…”

Section: Life Predictionmentioning

confidence: 99%

Application of artificial intelligence (AI) in the area of corrosion protection

Lin

Zhang

et al. 2023

ACMM

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Purpose As a common form of failure in industry, corrosion causes huge economic losses. At present, with the development of computational techniques, artificial intelligence (AI) is playing a more and more important role in the field of scientific research. This paper aims to review the application of AI in corrosion protection research. Design/methodology/approach In this paper, the role of AI in corrosion protection is systematically described in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction. Findings With efficient and in-depth data processing methods, AI can rapidly advance the research process in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction and save on costs. Originality/value This paper summarizes the application of AI in corrosion protection research and provides the basis for corrosion engineers to quickly and comprehensively understand the role of AI and improve production processes.

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“…Evaluating the reliability of tunnel structures is a key tool for assessing the safety level and their most probable failure state under possible uncertainties during their service life. According to the theory of structural reliability, the safety level of engineering structures can be denoted as a probabilistic model, and its mathematical formula is as follows [5,6]:…”

Section: Introductionmentioning

confidence: 99%

NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel

Xu,

Sun,

Rui

2024

Applied Sciences

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How to evaluate the reliability of deep soft rock tunnels under high stress is a very important problem to be solved. In this paper, we proposed a practical stochastic reliability method based on the third-generation non-dominated sorting genetic algorithm (NSGA–III) and eXtreme Gradient Boosting (XGBoost). The proposed method used the Latin hypercube sampling method to generate the dataset samples of geo-mechanical parameters and adopted XGBoost to establish the model of the nonlinear relationship between displacements and surrounding rock mechanical parameters. And NSGA–III was used to optimize the surrogate model hyper-parameters. Finally, the failure probability was computed by the optimized surrogate model. The proposed approach was firstly implemented in the analysis of a horseshoe-shaped highway tunnel to illustrate the efficiency of the approach. Then, in comparison to the support vector regression method and the back propagation neural network method, the feasibility, validity and advantages of XGBoost were demonstrated for practical problems. Using XGBoost to achieve Monte Carlo simulation, a surrogate solution can be provided for numerical simulation analysis to overcome the time-consuming reliability evaluation of initial support structures in soft rock tunnels. The proposed method can evaluate quickly the large deformation disaster risks of non-circular deep soft rock tunnels.

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Reliability assessment of subsea pipelines under the effect of spanning load and corrosion degradation

Cited by 23 publications

References 44 publications

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Application of artificial intelligence (AI) in the area of corrosion protection

NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel

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