Failure assessment of corrosion affected pipeline networks with limited failure data availability

Aryai, Vahid; Baji, Hassan; Mahmoodian, Mojtaba

doi:10.1016/j.psep.2021.11.024

Cited by 12 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This either shows that minimal efforts were paid to this domain in the last century, or the problem was not significant as the pipes were not aged. This could be attributed to the limited robust technologies and tools for developing predictive models in this era (Aryai et al., 2022). Another reason may be the lack of sophisticated digital tools or platforms to showcase the conducted research in this era (i.e., before 2000).…”

Section: Bibliometric Analysismentioning

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

View full text Add to dashboard Cite

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

show abstract

Section: Bibliometric Analysismentioning

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

View full text Add to dashboard Cite

show abstract

“…The spatial occurrence of the corrosion can be correlated, and corrosion is a time-varying process where the rate of corrosion occurrence changes over time. Failure estimation of the pipeline due to corrosion can be predicted spatio-temporally even in the case of limited historical data of previous failures [14]. The spatio-temporal reliability analysis in the works of [14] has shown that it can predict the future condition of the buried pipeline where the location and the time of future failure can be predicted with good accuracy.…”

Section: Natural Environment Risksmentioning

confidence: 99%

A review on the GIS usage in spatio-temporal risk assessment in asset management

Syafiq,

Azri

2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Risk assessment for asset management is a process to analyse the potential risk that could affect the efficiency of operating assets. An effective risk assessment strategy depends on high-quality data and effective data analysis. To do so, risk assessment should take into consideration the spatial and temporal aspects of risks of physical assets in asset management for a data-driven decision-making. Failure to incorporate the spatial and temporal aspects in risk, when the risk possess spatio-temporal correlation, could lead to a defective estimation of assets risks. GIS can be used to analyse multidimensional information which includes 3D GIS, and spatio-temporal data (2D+t and 3D+t). Due to its superiority in handling multidimensional data, GIS is often a preferable tool to collect, manage, manipulate, and visualize spatio-temporal data to allow an accurate and informed analysis. The aim of this study is to uncover the use of GIS for risk assessment in asset management when spatial and temporal data are concerned. This study conduct a thorough review of previous research to look into the current development of asset management that utilizes a spatio-temporal approach for data collection, analysis, and 3D visualization for risk assessment. At the end of this study, we found that physical assets are affected by risk that possess spatio-temporal correlation. Several GIS techniques for data collection, analysis and visualization are found to provide a more effective analysis for assessing spatio-temporal risks. This paper concludes by providing a workflow that can be used by asset managers to assess risk spatio-temporally based on the findings of this study.

show abstract

“…Pipelines are the best-grounded and cheapest mode for transporting oil, fluids, and natural gas. Pipeline networks 2 are large and complicated; they consume time and are often intensive for inquiring about a lot of pipelines. PIM is executed to reduce the possibility of remissness caused by the debasement and to maintain the programmability and security of pipelines.…”

Section: Related Terminologiesmentioning

confidence: 99%

“…Pipelines pose risks such as jet fire, unconfined vapour cloud explosion, flash fire, delay ignition, pool fire, and toxicity. There are many integrity management tools, like cathodic protection, inline inspection, hydro testing, surveillance, direct assessment and evaluations, pipeline equipment health monitoring, and thickness assessment 2 , 3 . The pipeline threats are categorised as fixed, non-stationary, and stationary.…”

Section: Introductionmentioning

confidence: 99%

Efficient qualitative risk assessment of pipelines using relative risk score based on machine learning

Vanitha,

Easwaramoorthy,

Krishna

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Pipelines are observed one of the economic modes of transport for transporting oil, gas, and water between various locations. Most of the countries in the world transport petroleum and other flammable products through underground pipelines. The underground and aboveground pipelines are facing various damages due to corrosion, dents, and ruptures due to the environment and operational fluid conditions. The danger of leaks and accidents increases as a result of these damages. Pipelines must be evaluated on a regular basis to make sure they are fit for transmission. By evaluating the effects of damages and the possibility of catastrophic failures using a variety of techniques, pipeline integrity is controlled. Applying the relative risk scoring (RRS) technique, pipeline failures are predicted. One of the probabilistic techniques used to forecast risk based on an impartial assessment is machine learning. With different parameters like corrosion, leakage, materials, atmosphere, surface, earth-movements, above-ground and underground facilities, etc., the RRS method provides an accuracy of 97.5% in identifying the risk and gives a precise classification of risk, whether the pipeline has a high, medium, or low risk without any delay on the prediction compared with Naive Bayes, decision tree, support vector machine, and graph convolutional network.

show abstract

Failure assessment of corrosion affected pipeline networks with limited failure data availability

Cited by 12 publications

References 31 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

A review on the GIS usage in spatio-temporal risk assessment in asset management

Efficient qualitative risk assessment of pipelines using relative risk score based on machine learning

Contact Info

Product

Resources

About