Reliability of pipelines with corrosion defects

Teixeira, A.P.; Soares, C. Guedes; Netto, Theodoro Antoun; Estefen, Segen F.

doi:10.1016/j.ijpvp.2007.09.002

Cited by 217 publications

(80 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As seen by (8), CV represents a relative magnitude of variability on the basis of mean. This makes it possible for CV by itself to explain the variability.…”

Section: Reliability Methodsmentioning

confidence: 99%

“…According to the paper, radial growth rate of corrosion is most significant to pipeline failures. Teixeira et al [8] used FORM and Monte Carlo simulation to predict explosion pressures and to conduct uncertainty analysis. Basic parameters such as yield strength, diameter, thickness, and operating pressure are considered in that study.…”

Section: Related Researchesmentioning

confidence: 99%

See 1 more Smart Citation

A Fuzzy Inference based Reliability Method for Underground Gas Pipelines in the Presence of Corrosion Defects

Kim¹,

Choe²,

Kim³

et al. 2016

Journal of Korean Institute of Intelligent Systems

View full text Add to dashboard Cite

Remaining lifetime prediction of the underground gas pipeline plays a key role in maintenance planning and public safety. One of main causes in the pipeline failure is metal corrosion. This paper deals with estimating the pipeline reliability in the presence of corrosion defects. Because a pipeline has uncertainty and variability in its operation, probabilistic approximation approaches such as first order second moment (FOSM), first order reliability method (FORM), second order reliability method (SORM), and Monte Carlo simulation (MCS) are widely employed for pipeline reliability predictions. This paper presents a fuzzy inference based reliability method (FIRM). Compared with existing methods, a distinction of our method is to incorporate a fuzzy inference into quantifying degrees of variability in corrosion defects. As metal corrosion depends on the service environment, this feature makes it easier to obtain practical predictions. Numerical experiments are conducted by using a field dataset. The result indicates that the proposed method works well and, in particular, it provides more advisory estimations of the remaining lifetime of the gas pipeline.

show abstract

“…As seen by (8), CV represents a relative magnitude of variability on the basis of mean. This makes it possible for CV by itself to explain the variability.…”

Section: Reliability Methodsmentioning

confidence: 99%

Section: Related Researchesmentioning

confidence: 99%

A Fuzzy Inference based Reliability Method for Underground Gas Pipelines in the Presence of Corrosion Defects

Kim¹,

Choe²,

Kim³

et al. 2016

Journal of Korean Institute of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Forecasting the failure of an element means creating a forecast of the moment at which the failure occurs and the probability of its existence. It makes it possible to assess the system regarding its specificity and the influence on its reliability [12,[16][17][18][19][20]22,23].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of failures in district heating systems

Babiarz

Chudy-Laskowska

2015

Engineering Failure Analysis

View full text Add to dashboard Cite

“…Corrosion was a major cause of 18% of significant incidents from 1988-2008(Fessler 2008. Structural engineers and naval architects are becoming increasingly interested in rate of loss of strength of steel and thus in loss of material for offshore and onshore pipe systems (Teixeira, Guedes Soares et al 2008). Due to the nature of transported material, it is extremely important to determine the structural reliability of a corroded pipe.…”

Section: Introductionmentioning

confidence: 99%

Determination of extension of life of corroded offshore pipelines using form and monte carlo structural reliability

Nizamani¹,

Mustaffa²,

Wen³

2015

Proceedings of the Second International Conference on Performance-Based and Life-Cycle Structural Engineering (PLSE 2015)

View full text Add to dashboard Cite

The evaluation of the structural strength of an offshore pipeline after 25 years of service is an important issue for extending its lifespan. This is an important environmental and economic issue, especially when the pipeline is related to the oil and gas industry. Remaining strength after corrosion effects are included in the performance equation and can be determined by using maximum operating pressure and capacity equations. The results are then compared from burst test results. In this study, Bayesian updating of probability of failure is used to evaluate the updated probability of failure. The performance equations from the two main codes on corrosion used in this study are B31G and DNV-RP-F101 and they are used validate the results. The sensitivity analysis of the variables such as defect depth and thickness is considered in the analysis. This method could be adopted for evaluating the service life extension and evaluation of pipelines working under extreme environments. FORM and Monte Carlo simulations will be used to determine the updated probability of failure. The method could be used for many engineering structures where either practical approaches are not feasible to determine the remaining life of the structure or the uncertainty of the expected results is too high. The evidence concluded in this study could be used by industry to enhance our understanding of the mechanisms for pipeline failure and processes necessary for its preservation.

show abstract

Reliability of pipelines with corrosion defects

Cited by 217 publications

References 14 publications

A Fuzzy Inference based Reliability Method for Underground Gas Pipelines in the Presence of Corrosion Defects

A Fuzzy Inference based Reliability Method for Underground Gas Pipelines in the Presence of Corrosion Defects

Forecasting of failures in district heating systems

Determination of extension of life of corroded offshore pipelines using form and monte carlo structural reliability

Contact Info

Product

Resources

About