Analytical Modeling of Elastic Moduli Dispersion and Poromechanical Responses of a Dual-Porosity Dual-Permeability Porous Cylinder Under Dynamic Forced Deformation Test

Liu, Chao; Phan, Dung

doi:10.1007/s00603-022-03165-3

Cited by 4 publications

(1 citation statement)

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“…After the upstream and downstream pressures are stabilized, the constant pulse pressure is applied to the upstream end of the sample. The permeability of the rock is calculated by recording the upstream and downstream pressure changes of the sample using the following equation [18]. where, K is the rock permeability (permeability coefficient), μ is the gas dynamic viscosity, β is the fluid compression coefficient, L is the sample length, V u and V d are the upstream and downstream gas chamber volumes, whose value is 2.199 × 10 -5 m −3 and 2.102 × 10 -5 m −3 , respectively, A is the sample's cross-sectional area, α is the attenuation coefficient, which can be calculated by upstream and downstream pressure difference [19], and its calculation formula is expressed as…”

Section: Permeabilitymentioning

confidence: 99%

Reservoir Characteristics

Zhao

Zhang

2023

Hydraulic Fracturing and Rock Mechanics

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Section: Permeabilitymentioning

confidence: 99%

Reservoir Characteristics

Zhao

Zhang

2023

Hydraulic Fracturing and Rock Mechanics

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Analytical Porothermoelastodynamic Modeling of Stress Wave Through a Fluid‐Saturated Porous Cylinder

Liu

2024

Num Anal Meth Geomechanics

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Analytical porothermoelastodynamic (PTED) solutions are rare in the literature. The responses of fluid‐saturated porous materials subject to coupled mechanisms of loading frequency, fluid flow, stress, and temperature are unclear. In this paper, we use the PTED theory and derive the analytical solutions of pore pressure, temperature, stress, force, and displacement for an isotropic fluid‐saturated porous cylinder subject to a harmonic vibration. The coupled partial differential equations among pore pressure, displacement, and temperature are decoupled by matrix diagonalization and solved by further introducing a potential function and separation of variables. The PTED solution reproduces the poroelastodynamic (PED) one by easing the thermal effect. A demonstration example shows that the coupled mechanisms among pore pressure, stress, and displacement are highly frequency dependent. The thermal effect is more pronounced at low frequencies than at high frequencies when the inertial impact is more significant. Pore pressure is almost uniform in the ‐direction at low frequencies and becomes nonuniform at high frequencies for both PTED and PED cases. Displacements exhibit linear behavior at low frequencies and become nonlinear at high frequencies. Thermal stress and expansion significantly impact the pore pressure and displacement. A brief sensitivity analysis shows that pore pressure responds linearly and monotonically with the increase of the volumetric thermal expansion coefficient of the solid matrix at low frequencies and becomes nonlinear and nonmonotonic at high frequencies. The volumetric thermal expansion coefficient of the solid matrix has a minor effect on the vertical displacement and significantly influences the radial displacement.

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

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Analytical Modeling of Elastic Moduli Dispersion and Poromechanical Responses of a Dual-Porosity Dual-Permeability Porous Cylinder Under Dynamic Forced Deformation Test

Cited by 4 publications

References 73 publications

Reservoir Characteristics

Reservoir Characteristics

Analytical Porothermoelastodynamic Modeling of Stress Wave Through a Fluid‐Saturated Porous Cylinder

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

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