Disaster impact assessment of the underground hazardous materials pipeline

Teng, Min-Cheng; Ke, Siao-Syun

doi:10.1016/j.jlp.2021.104486

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…The use of machine learning techniques in pipeline risk assessment has been a topic of increasing interest in recent years. Sohaib et al 5 proposed a method for detecting leaks in circular water storage tanks in the chemical sector using acoustic emissions. Support vector machines are used to locate the exact location of the crack or leakage.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As with computer hackers, some third parties may damage pipeline equipment or perform incorrect operations, causing the pipeline to malfunction. Natural disasters 5 like lightning, heavy rains, floods, weather-related events, and earth movements also affect the quality of pipelines. Due to the severity of the composite risk from all threats, pipeline sections may be prioritised for integrity assessment.…”

Section: Introductionmentioning

confidence: 99%

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

Vanitha,

Easwaramoorthy,

Krishna

et al. 2023

Sci Rep

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

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“…In a study conducted by Manshoori [32], pipeline damage in industrial areas and oil pipelines was evaluated following critical past earthquakes. Teng's study [33] implemented a seismic scenario analysis using a spatial grid to evaluate earthquake damage to underground pipelines in an urban area. Types of damage to underground pipelines were classified, pipeline disaster management procedures were discussed, and improvement measures such as establishing a geographic information platform and conducting disas-ter impact assessments for hazardous material pipelines were proposed.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Earthquake-Induced Pipe Damage in Liquefiable Soils

Dulger,

Kilic

2024

Applied Sciences

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Liquefaction occurs in saturated sandy and silty soils due to transient and repetitive seismic loads. The result is a loss of soil strength caused by increased pore pressure. In this study, the response of buried pipes in the Iskenderun region during the earthquakes centered in the subprovinces of Pazarcık and Elbistan in Kahramanmaraş, Turkey, on 6 February 2023, has been investigated utilizing numerical analyses using geological data from two different areas. The effects of shallow and deep rock layers, pipe diameter, burial depths, and boundary conditions have been evaluated. In the analyses, records from two stations located in Iskenderun during the Pazarcık, Kahramanmaraş earthquake have been utilized, taking into account records from shallow rock (station no. 3116) and thick soil layers (station no. 3115), as determined from shear wave velocities. Modeling conducted using station 3116 records has revealed the effect of shallow rock layers on pipe displacement, indicating less damage in areas where the rock layer is close to the surface. The pipe uplift risk is higher when the bedrock is deep, and the overlying soil layer is liquefiable (station no. 3115). It has been determined that depth to bedrock significantly influences upward movement of the pipe. In the areas where the bedrock is deep, expanding the boundary conditions has helped reduce the effects of settlements outside the pipe, preventing the occurrence of pipe uplift. Increasing the pipe diameter has increased the amount of uplift. The analysis results are consistent with field observations.

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“…For pipelines in areas with complex geological conditions such as mountainous regions, landslides have always been considered a dominant challenge for continuous pipeline service [1][2][3] . In this respect, landslides always impose additional stress on the pipeline, which may further lead to local deformation and displacement [4,5]. Once the additional stress generated exceeds the material strength or there are defects such as cracks and metal loss in the deformation area, it will seriously damage structural integrity of the pipeline [6], resulting in catastrophic accidents such as rupture and leakage.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards

et al. 2022

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Pipelines that cross complex geological terrains are inevitably threatened by natural hazards, among which landslide attracts extensive attention when pipelines cross mountainous areas. The landslides are typically associated with ground movements that would induce additional stress on the pipeline. Such stress state of pipelines under landslide interference seriously damage structural integrity of the pipeline. Up to the date, limited research has been done on the combined landslide hazard and pipeline stress state analysis. In this paper, a multi-parameter integrated monitoring system was developed for the pipeline stress-strain state and landslide deformation monitoring. Also, data-driven models for the pipeline additional stress prediction was established. The developed predictive models include individual and ensemble-based machine learning approaches. The implementation procedure of the predictive models integrates the field data measured by the monitoring system, with k-fold cross validation used for the generalization performance evaluation. The obtained results indicate that the XGBoost model has the highest performance in the prediction of the additional stress. Besides, the significance of the input variables is determined through sensitivity analyses by using feature importance criteria. Thus, the integrated monitoring system together with the XGBoost prediction method is beneficial to modeling the additional stress in oil and gas pipelines, which will further contribute to pipeline geohazards monitoring management.

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Disaster impact assessment of the underground hazardous materials pipeline

Cited by 12 publications

References 5 publications

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

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

Investigation of Earthquake-Induced Pipe Damage in Liquefiable Soils

A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards

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