Reliability of the conventional approach for stress/fatigue analysis of pitting corroded pipelines – Development of a safer approach

Traffic loads have an important impact on long-term performance of buried urban pipelines due to generation of cyclic stress and fatigue damage. As a key task for pipeline fatigue assessment, the investigation of traffic-induced pipe stress and fatigue load spectrums is always a big challenge. Focusing on the metal pipelines buried under urban intersection, an integrated framework is proposed for fatigue assessment of the pipelines under traffic loads using video monitoring data acquired by the widespread surveillance cameras in cities. The computer vision algorithms and a quarter vehicle model are first employed to identify traffic loads. Analytical pipe-soil models are then developed to calculate pipeline stress and establish the traffic-induced fatigue load spectrums of buried pipelines, based on which the Monte-Carlo simulations are finally conducted to evaluate cumulative fatigue damage and fatigue reliability. A case study on a DN500 steel pipeline buried under an urban intersection demonstrates and validates the fatigue assessment procedure. The results can provide a basis for decision support regarding maintenance and replacement of urban pipelines.

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“…According to the work reported by Mokhtari and Melchers 41 the SCF can be calculated by using l , w and d :…”

Section: Methodsmentioning

confidence: 99%

Fatigue assessment of buried metal pipelines under traffic loads using video monitoring data

Peng

Wang

et al. 2023

Structural Health Monitoring

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“…In the water pipe failure analysis methods [11], accurately quantifying RWT at the low range (e.g. less than 50 percent of the nominal thickness) is paramount importance.…”

Section: A Extreme Value Marginal Distributionmentioning

confidence: 99%

“…Hence, some methods such as the fracture mechanics and loss-of-section [6]- [11] have been proposed for computing residual strength or likely failure of the pipe. In contrast to the fracture mechanics approach [8], [10], the loss-of-section technique [11] requires remaining thickness of a pipe wall to be known. In equivalent words, remaining wall thickness (RWT) of a water pipe is required to be measured before its failure analysis can be conducted.…”

Section: Introductionmentioning

confidence: 99%

Efficient Evaluation of Remaining Wall Thickness in Corroded Water Pipes Using Pulsed Eddy Current Data

Nguyen

Miró

2020

IEEE Sensors J.

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In order to analyse failures of an ageing water pipe, some methods such as the loss-of-section require remaining wall thickness (RWT) along the pipe to be fully known, which can be measured by the magnetism based non-destructive evaluation sensors though they are practically slow due to the magnetic penetrating process. That is, fully measuring RWT at every location in a water pipe is not really practical if RWT inspection causes disruption of water supply to customers. Thus, this paper proposes a new data prediction approach that can increase amount of RWT data of a corroded water pipe collected in a given period of time by only measuring RWT on a part (e.g. 20%) of the total pipe surface area and then employing the measurements to predict RWT at unmeasured area. It is proposed to utilize a marginal distribution to convert the non-Gaussian RWT measurements to the standard normally distributed data, which can then be input into a 3-dimensional Gaussian process model for efficiently predicting RWT at unmeasured locations on the pipe. The proposed approach was implemented in two real-life in-service pipes, and the obtained results demonstrate its practicality.

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“…Since pipelines operate under high pressure and the transport medium is flammable and explosive, once an accident occurs, it is easy to cause heavy losses. Therefore, it is very important to monitor the safety state of pipelines [2,3].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

The Optimization of a Pipeline Temperature Monitoring Method Based on Non-Local Means with the Black Widow Optimization Algorithm

Lou,

Wang,

Sima

et al. 2023

Energies

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The accuracy of pipeline temperature monitoring using the Brillouin Optical Time Domain Analysis system depends on the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. The Non-Local Means noise reduction algorithm, due to its ability to use the data patterns available within the two-dimensional measurement data space, has been used to improve the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. This paper studies a new Non-Local Means algorithm optimized through the Black Widow Optimization Algorithm, in view of the unreasonable selection of smoothing parameters in other Non-Local Means algorithms. The field test demonstrates that, the new algorithm, when compared to other Non-Local Means methods, excels in preserving the detailed information within the Brillouin Gain Spectrum. It successfully restores the fundamental shape and essential characteristics of the Brillouin Gain Spectrum. Notably, at the 25 km fiber end, it achieves a 3 dB higher Signal-to-Noise Ratio compared to other Non-Local Means noise reduction algorithms. Furthermore, the Brillouin Gain Spectrum values exhibit increases of 9.4% in Root Mean Square Error, 12.5% in Sum of Squares Error, and 10% in Full Width at Half Maximum. The improved method has a better denoising effect and broad application prospects in pipeline safety.

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Reliability of the conventional approach for stress/fatigue analysis of pitting corroded pipelines – Development of a safer approach

Cited by 16 publications

References 49 publications

Fatigue assessment of buried metal pipelines under traffic loads using video monitoring data

Fatigue assessment of buried metal pipelines under traffic loads using video monitoring data

Efficient Evaluation of Remaining Wall Thickness in Corroded Water Pipes Using Pulsed Eddy Current Data

The Optimization of a Pipeline Temperature Monitoring Method Based on Non-Local Means with the Black Widow Optimization Algorithm

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