Impact action of submarine slides on pipelines: A review of the state-of-the-art since 2008

Fan, Ning; Jiang, Jianxiong; Nian, Tingkai; Dong, Yun; Guo, Lin; Fu, C. L.; Tian, Zhuangcai; Guo, Xingsen

doi:10.1016/j.oceaneng.2023.115532

Cited by 12 publications

(4 citation statements)

References 89 publications

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“…Marine clayey sediment is extensively distributed worldwide [1][2][3][4]. The unfavorable behavior of marine clay deposits has been exposed over the past few years during the building of marine engineering indicates which has a detrimental impact on the dependability of engineering projects by causing issues such as sliding and splitting [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

Cui,

Zhou,

Gao

et al. 2024

Water

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In this study, a method was introduced to validate the presence of a Representative Elementary Volume (REV) within marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure. Physical testing provided the basis for this verification. Once the existence of the REV for such sediment was confirmed, we established a machine-learning predictive model. This model utilizes a hybrid algorithm combining Particle Swarm Optimization (PSO) with a Support Vector Machine (SVM). The model was trained using a database generated from the aforementioned physical tests. The machine-learning model demonstrates favorable predictive performance based on several statistical metrics, including the coefficient of determination (R2), mean residual error (MSE), mean relative residual error (MRSE), and the correlation coefficient R during the verification process. Utilizing the established machine-learning predictive model, one can effortlessly obtain the permeability tensor of marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure by inputting the relevant stress condition parameters. The original research cannot estimate the permeability tensor under similar loading and unloading conditions through REV. In this study, the physical model test was used to determine the REV of marine cohesive sediments with cracks by cyclic-constrained pressure loading and unloading. Referring to the results of physical tests, we developed a machine-learning prediction model that can easily estimate the permeability tensor of marine cohesive sediments with cracks under cyclic loading and constrained pressure unloading conditions. This method greatly saves time and computation and provides a direct method for engineering and technical personnel to predict the permeability tensor in this case.

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Section: Introductionmentioning

confidence: 99%

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

Cui,

Zhou,

Gao

et al. 2024

Water

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“…The distribution of soft soils, which have physical and mechanical properties such as high water content, high thixotropy, low strength, and low permeability, is very common in deep-sea areas, according to survey and analysis results (Nian et al, 2018;Huang et al, 2021). Deep-sea soft soils are highly susceptible to destabilization and sliding due to external disturbances (Ren et al, 2018a;Fan et al, 2023;Guo et al, 2023b;Guo et al, 2024), especially for cyclic loading disturbances with high frequencies and load levels (usually caused by frequent seaquakes or earthquakes; for example, Figure 1 shows the seismic distribution situation around the Pacific Ocean). The low permeability of deep-sea soft soils results in the inability of water to drain out of the soils (i.e., in the undrained state) under cyclic loading, which generates excess pore water pressure and reduces the strength and bearing capacity of deep-sea seabed soils, threatening the safety and stability of deep-sea engineering structures (Andersen, 2009).…”

Section: Introductionmentioning

confidence: 99%

An undrained dynamic strain-pore pressure model for deep-water soft clays from the South China Sea

Jiao,

Guo,

Fan

et al. 2024

Front. Mar. Sci.

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With the increasing use of oceans for engineering purposes, such as the installation of suction anchors and pipelines, the stability of seabed structures has become a pivotal concern and is intricately linked to the characteristics of seabed soils. This study focuses specifically on deep-sea soft clay, a predominant seabed soil type distinguished by its high water content, thixotropy, and low permeability. These clays are vulnerable to destabilization and damage when disturbed, thereby posing threats to seabed installations. While the existing literature extensively examines the cyclic behavior of clay, considering factors such as the pore pressure response and strain and deformation characteristics, there is a notable gap in research addressing the behavior of deep-sea soft clay under comprehensive stress levels and prolonged cyclic loading. In this study, cyclic shear tests of the natural marine clay of the South China Sea were conducted, and the cyclic stress ratio (CSR), overpressure consolidation ratio (OCR), consolidation ratio (Kc), and loading frequency were varied. It was found that the CSR, OCR, and Kc significantly impact the cumulative dynamic strain in deep-sea soft clay during undrained cyclic dynamic tests. Higher CSR values lead to increased dynamic strain and structural failure risk. Subsequently, a dynamic strain-dynamic pore pressure development model was proposed. This model effectively captures the cumulative plastic deformation and dynamic pore pressure development, showing correlations with the CSR, OCR, and Kc, thus providing insights into the deformation and pore pressure trends in deep-sea clay under high cyclic dynamic loading conditions. This research not only furnishes essential background information but also addresses a critical gap in understanding the behavior of deep-sea soft clay under cyclic loading, thereby enhancing the safety and stability of seabed structures.

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“…TTBTs can offer the However, corresponding to this, the maintenance cost of subsea pipelines is also extremely high [25]. Subsea pipelines may suffer from deformation defects under various damaging factors (such as corrosion, alternating sea currents, geological deformation, and human activities) [26][27][28]. Severe interplay among these factors can exacerbate each other and may even lead to pipeline rupture, causing major safety accidents, and inestimable damage to the global environment and regional economies [29,30].…”

Section: Introductionmentioning

confidence: 99%

In-Line Inspection (ILI) Techniques for Subsea Pipelines: State-of-the-Art

Zhu,

Chen,

Lin

et al. 2024

JMSE

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Offshore oil and gas resources play a crucial role in supplementing the energy needs of human society. The crisscrossing subsea pipeline network, which serves as vital infrastructure for the storage and transportation of offshore oil and gas, requires regular inspection and maintenance to ensure safe operation and prevent ecological pollution. In-line inspection (ILI) techniques have been widely used in the detection and inspection of potential hazards within the pipeline network. This paper offers an overview of ILI techniques used in subsea pipelines, examining their advantages, limitations, applicable scenarios, and performance. It aims to provide valuable insights for the selection of ILI technologies in engineering and may be beneficial for those involved in pipeline integrity management and planning.

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Impact action of submarine slides on pipelines: A review of the state-of-the-art since 2008

Cited by 12 publications

References 89 publications

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

An undrained dynamic strain-pore pressure model for deep-water soft clays from the South China Sea

In-Line Inspection (ILI) Techniques for Subsea Pipelines: State-of-the-Art

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