Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Liu, ChenYang; Wang, Yong; Zhang, XiaoPei; Du, Lizhi

doi:10.3221/igf-esis.49.52

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…The whitenization differential Equation (6) of the grey Verhulst model, which is the first-order differential equation, can be obtained from the x ð1Þ ðkÞ…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, China's engineering construction has developed rapidly, especially for deep foundation pit projects, caused by the need of large-scale public facilities and lots of exploitation of underground spaces [1][2][3][4][5][6][7][8][9]. However, the settlement of the foundation pit is affected by many factors, such as the excavation rate, the real-time excavation depth, changes in groundwater level, internal friction angle, soil weight, and number of supports [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Application of GA-BP Neural Network Optimized by Grey Verhulst Model around Settlement Prediction of Foundation Pit

Liu

Wang

et al. 2021

Geofluids

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Due to the limitation in the prediction of the foundation pit settlement, this paper proposed a new methodology which takes advantage of the grey Verhulst model and a genetic algorithm. In the previous study, excavation times are often the only factor to predict the settlement, which is mainly because the correspondence between real-time excavation depth and the excavation time is hard to determine. To solve this issue, the supporting times are precisely recorded and the excavation depth rate can be obtained through the excavation time length and excavation depth between two adjacent supports. After the correspondence between real-time excavation depth and the excavation time is obtained, the internal friction angle, cohesion, bulk density, Poisson’s ratio, void ratio, water level changes, permeability coefficient, number of supports, and excavation depth, which can influence the settlement, are taken to be considered in this study. For the application of the methodology, the settlement monitoring point of D4, which is near the bridge pier of the highway, is studied in this paper. The predicted values of the BP neural network, GA-BP neural network, BP neural network optimized by the grey Verhulst model, and GA-BP neural network optimized by the grey Verhulst model are detailed compared with the measured values. And the evaluation indexes of RMSE, MAE, MSE, MAPE, and R 2 are calculated for these models. The results show that the grey Verhulst model can greatly improve the consistency between predicted values and measured values, while the accuracy and resolution is still low. The genetic algorithm (GA) can greatly improve the accuracy of the predicted values, while the GA-BP neural network shows low reflection to the fluctuation of measured values. The GA-BP neural network optimized by the grey Verhulst model, which has taken the advantages of GA and the grey Verhulst model, has extremely high accuracy and well consistency with the measured values.

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“…The whitenization differential Equation (6) of the grey Verhulst model, which is the first-order differential equation, can be obtained from the x ð1Þ ðkÞ…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of GA-BP Neural Network Optimized by Grey Verhulst Model around Settlement Prediction of Foundation Pit

Liu

Wang

et al. 2021

Geofluids

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“…Brittleness is the key parameter describing the rock failure and is a fundamental parameter in various fields, e.g., mining engineering, rock mechanics, unconventional oil, and hydraulic fracturing [1][2][3][4][5][6][7][8][9][10]. Nowadays, the definition for rock brittleness is proposed in different engineering fields.…”

Section: Introductionmentioning

confidence: 99%

A New Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Liu

Zhang

et al. 2021

Lithosphere

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Brittleness is a crucial parameter of rock mass and the key indicator in rock engineerings, such as rockburst prediction, tunnelling machine borehole drilling, and hydraulic fracturing. To solve the problem of using present brittleness indexes, the existing rock brittleness indexes were firstly summarised in this paper. Then, a brittleness index (BL), which considers the ratio of stress drop rate and stress increase rate and the peak stress, was proposed. This new index has the advantage of simplifying the acquisition of key parameters and avoiding dimensional problems, as well as taking the complete stress-strain curves into account. While applying the BL, the peak strain is used to describe the difficulty of brittle failure before the peak point, and the ratio of stress drop to strain increase can reflect the stress drop rate without dimension problem. In order to verify the applicability of BL, through the PFC2D, the microparameters and confining pressure were changed to model different types of rock numerical specimens and different stress condition. The results show that the BL can well reflect and classify the brittleness characteristics of different rock types and characterise the constraint of confining pressure on rock brittleness. Moreover, the influence of microparameter on macroparameter was studied. In order to further verify the reliability of the brittleness index (BL), this study conducted uniaxial and triaxial compression tests (30 MPa) on marble, sandstone, limestone, and granite under different confining pressure.

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“…According to different research purposes, domestic and foreign scholars have proposed many indicators of rock brittleness according to the definition of brittleness and failure phenomena. Zhang et al (2020) have established a new brittleness evaluation index and revealed the evolution process of the overall fracture induced by the internal rock fracture, which is based on the energy mutation process of the quasistatic equilibrium equation of the two-body system [16]; Zhao and Guo (2020) have analyzed the failure characteristics of rocks from the energy point of view and established a brittleness evaluation method based on the relationship between fracture energy, mechanical parameters, and mineral composition [17]; have conducted experiments on two materials with different length-diameter ratios and found that the use of postpeak data is of great significance [18]; Zhou et al (2020) have analyzed the influence of unloading rate on rock mechanical parameters, failure characteristics, and brittleness, through axial loading and confining unloading experiments, and then proposed a brittleness evaluation method considering the unloading effect [19].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Brittle Instability Characteristics of 2000 m Deep Sandstone Influenced by Mineral Composition

Hao

Chen

Wei

et al. 2021

Shock and Vibration

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The study of dynamic brittleness and failure characteristics is of guiding significance for promoting the full exploitation and utilization of deep sandstone reservoirs. At present, there have been more comprehensive studies on the mechanical properties of deep sandstone reservoirs, but the study of mineral composition on the dynamic brittleness and failure characteristics of deep sandstone reservoirs is relatively weak. In this paper, XRD mineral composition analysis, uniaxial compression experiment, and Brazilian splitting are used to study the influence of mineral composition on mechanical properties and failure characteristics of deep sandstone reservoirs. It was concluded that (1) the mineral compositions of deep sandstone reservoirs are mainly three kinds of oxides: SiO2, Al2O3, and CaO. The failure modes of deep sandstone reservoir samples under uniaxial compression are more complicated, with tension failure and shear failure each accounting for half. In the Brazilian split test, the failure modes of sandstone samples are mainly shear failure. (2) The compressive strength decreases obviously with the increase of CaO content. The contents of SiO2, Al2O3, and CaO all have a great influence on the residual strength of deep sandstone reservoirs. The deformation modulus decreases gradually with the increase of Al2O3 content. (3) The brittleness increases slightly when the content of SiO2 increases, while the brittleness decreases slightly when the content of Al2O3 increases. Considering factors such as strength, modulus, brittleness, and failure characteristics, SiO2 content has the greatest influence on the mechanical properties of deep sandstone reservoirs, followed by Al2O3 content, and CaO content has the least influence. The research results have a guiding role in the utilization and development of oil and gas resources in deep sandstone reservoirs and promote oil and gas development from the middle to deeper layers.

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Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Cited by 9 publications

References 14 publications

Application of GA-BP Neural Network Optimized by Grey Verhulst Model around Settlement Prediction of Foundation Pit

Application of GA-BP Neural Network Optimized by Grey Verhulst Model around Settlement Prediction of Foundation Pit

A New Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Dynamic Brittle Instability Characteristics of 2000 m Deep Sandstone Influenced by Mineral Composition

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