A Non-local XFEM-Based Methodology for Modeling Mixed-mode Fracturing of Anisotropic Rocks

Mehraban, Mohammad R.; Bahrami, Bahador; Ayatollahi, M.R.; Nejati, Morteza

doi:10.1007/s00603-022-03134-w

Cited by 17 publications

(3 citation statements)

References 37 publications

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“…When the stress is zero, the rock material is completely damaged. The damage degree of the element is defined by the tension-separation criterion, as follows (Mehraban et al, 2023):…”

Section: Xfem Theory For Numerical Simulationmentioning

confidence: 99%

Experimental and numerical modeling of deformation-cracking mechanics of 3D-printed rock samples with single fracture

Song

Tian

et al. 2023

Adv. Geo-Energy Res.

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The analysis of mechanical response and deformation-cracking behavior contributes to the high-efficiency extraction of geo-energy and long-term safety of underground engineering structures. Compared to natural cores, the mechanical properties of 3D-printed samples made from quartz sand as raw material are relatively homogeneous, and can be used for quantitative studies on the influence of natural defects on the mechanical properties of rocks. In this work, 3D-printed samples with single fractures of different crack angles, lengths and widths were fabricated and used for uniaxial compression tests. Adopting the digital image correlation method, the stress-strain distribution during uniaxial compression tests were visualized, and the influence of prefabricated fracture characteristics (dip angle, length, and width) on the deformation-failure process were studied. An extended finite element method subroutine for ABAQUS ® software was modeled and used for the uniaxial compression simulation, which was validated by experiments. Then, the influence of mechanical parameters (Young's modulus, Poisson's ratio, cohesion, and internal friction angle) on the deformation-cracking mechanics were simulated and studied. The results indicate that, compared to the intact sample, fractures reduce the sample strength. With the extension of fracture length and width, or the decline of fracture angle, both the peak strain and strength of the 3D-printed samples decrease. The splitting tensile failure, or shear failure, or both were determined for the 3D-printed samples with different fracture angles. For the same axial strain, the extension length of the new crack increases linearly with rising Young's modulus and decreases linearly with increasing Poisson's ratio. The initial strain of new cracks decreases linearly with increasing Young's modulus, while little variations are found in samples with different Poisson's ratio. For the same axial displacement load, the peak stress increases linearly with growing internal friction angle and cohesion.

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“…When the stress is zero, the rock material is completely damaged. The damage degree of the element is defined by the tension-separation criterion, as follows (Mehraban et al, 2023):…”

Section: Xfem Theory For Numerical Simulationmentioning

confidence: 99%

Experimental and numerical modeling of deformation-cracking mechanics of 3D-printed rock samples with single fracture

Song

Tian

et al. 2023

Adv. Geo-Energy Res.

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“…For instance, extensive studies have been conducted to obtain theoretical solutions for fracture prediction of various types of notches, including sharp V- [11–14], blunt V- [15,16], U- [17–20], key-hole [21–24] and VO-shaped [25,26] notches. As computers have become more powerful, recent research has been more focused on computational methods such as phase-field [27–29], extended finite-element [30–33] and mesh-free [34,35] simulations.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, extensive studies have been conducted to obtain theoretical solutions for fracture prediction of various types of notches, including sharp V- [11][12][13][14], blunt V- [15,16], U- [17][18][19][20], key-hole [21][22][23][24] and VO-shaped [25,26] notches. As computers have become more powerful, recent research has been more focused on computational methods such as phase-field [27][28][29], extended finite-element [30][31][32][33] and mesh-free [34,35] simulations. This study aims to suggest an ML-based approach for fracture prediction of notched components at lower cost while maintaining the accuracy compared to the standard computational approaches.…”

Section: Introductionmentioning

confidence: 99%

Data-driven based fracture prediction of notched components

Talebi,

Bahrami,

Daneshfar

et al. 2023

Phil. Trans. R. Soc. A.

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A data-driven approach is developed to predict the fracture load of a notched component. To do so, more than 1500 fracture tests (507 unique experimental data points) on mixed-mode I/II loading of notched brittle samples were collected from the literature. After pre-processing the raw data, six features of maximum tangential stress ( σ θ θ max ) , maximum tangential stress angle ( θ σ θ θ max ) , ultimate tensile strength ( σ u ) , fracture toughness ( K I c ) , notch opening angle ( 2 α ) and notch tip radius ( ρ ) were selected by using the neighbourhood component analysis (NCA) technique. To predict the fracture load of various types of notched samples, several machine learning (ML) models were trained using the methods of Gaussian process regression (GPR), decision tree ensemble and artificial neural network (ANN). Then, the Bayesian optimization algorithm was applied to find the optimum hyperparameters for each model. Lastly, the performance of the models in predicting fracture load was evaluated against 124 unseen data points. The results revealed the high potential of data-driven methods for assessing the fracture load of notched brittle components with acceptable precisions of 92%, 89% and 88% accuracy, respectively, for GPR, decision tree ensemble and ANN models. The superior performance of the GPR method can be attributed to its ability to capture complex nonlinear relationships in the data while providing reliable uncertainty estimates. Furthermore, thanks to its interpolation capabilities, GPR is able to seamlessly fill the gaps between data points, resulting in more comprehensive and precise predictions across the entire range of input data. Additionally, the presented models were capable of predicting the fracture load of VO-shaped notched samples with acceptable accuracy, though this type of notch was not used in the model training process. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

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