Estimation of Elasticity of Porous Rock Based on Mineral Composition and Microstructure

Liu, Zaobao; Shao, Jian‐Fu; Xu, Weiya; Shi, Chong

doi:10.1155/2013/512727

Cited by 18 publications

(6 citation statements)

References 31 publications

(33 reference statements)

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“…In recent years, artificial intelligence and soft computing models have been broadly applied in geotechnical applications, e.g., tunnel resources (Mahmoodzadeh and Zare 2016;Mahmoodzadeh et al 2019;Mahmoodzadeh et al 2020a;Mahmoodzadeh et al 2020b;Mahmoodzadeh et al 2021a), mechanical property (Liu et al 2013;Liu et al 2015), tunnel surface settlement (Mahmoodzadeh et al 2020c), landslide displacement (Youssef et al 2016), rock bursts (Zhou et al 2016), tunnel geomechanical properties (Mahmoodzadeh et al 2021b;Mahmoodzadeh et al 2021c), sidewall displacement of underground caverns (Mahmoodzadeh et al 2021d).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Factor of Safety of Slopes Stability Using Several Machine Learning Techniques

Mahmoodzadeh

Mohammadi

2021

Preprint

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Because of the disasters associated with slope failure, the analysis and forecasting of slope stability for geotechnical engineers are crucial. In this work, in order to forecast the factor of safety (FOS) of the slopes, six machine learning (ML) techniques of Gaussian process regression (GPR), support vector regression (SVR), decision trees (DT), long-short term memory (LSTM), deep neural networks (DNN), and K-nearest neighbors (KNN) were performed. A total of 327 slope cases in Iran with various geometric and shear strength parameters analyzed by PLAXIS software to evaluate their FOS, were employed in the models. The K-fold (K=5) cross-validation (CV) method was applied to evaluate the performance of models’ prediction. Finally, all the models produced acceptable results and almost close to each other. However, the GPR model with R2 = 0.8139, RMSE = 0.160893, and MAPE = 7.209772%, was the most accurate model to predict slope stability. Also, the backward selection method was applied to evaluate the contribution of each parameter in the prediction problem. The results showed that all the features considered in this study have significant contributions to slope stability. However, features φ (friction angle) and γ (unit weight) were the most effective and least effective parameters on slope stability, respectively.

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Section: Introductionmentioning

confidence: 99%

Forecasting Factor of Safety of Slopes Stability Using Several Machine Learning Techniques

Mahmoodzadeh

Mohammadi

2021

Preprint

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“…When the neural network is used for slope stability evaluation, it is difficult to reasonably determine the network structure; the training speed is slow, and the phenomenon of falling into the local minimum solution exists 33 . The penalty parameters of the support vector machine (SVM) method are robust and difficult to identify 34 . Artificial intelligence methods often require a large number of engineering cases for training, but actual engineering projects often do not have the conditions necessary to provide a large number of engineering cases.…”

Section: Introductionmentioning

confidence: 99%

Research on the stability evaluation method of anchored slopes based on group decision making and matter element analysis

Xia

Ying

2021

Sci Rep

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This research is focused on the evaluation method of anchored slope stability, and an accurate evaluation method with a simple operation is proposed. Group decision theory and the analytic hierarchy process are used to determine the weight of each evaluation element, the correlation degree of each indicator is determined based on matter element analysis theory, and inverse hierarchical calculations are performed based on the obtained weight value and correlation degree to finally obtain the criteria layer correlation degree used for stability evaluation. The results show the following: (1) the evaluation method better integrates the effects of multiple factors on the stability of the anchored slope, and the evaluation results are accurate and consistent with the actual situation of the project; (2) the evaluation method can make full use of the experience of the expert group and effectively avoid the evaluation error caused by the subjective deviation of a single expert; (3) the group decision theory-entropy model was introduced to realize the quantitative evaluation of the reliability of expert scoring and effectively improve the efficiency of expert discussion; and (4) the evaluation result is intuitive, and the correlation degree obtained can not only reflect the stability grade of the anchored slope but also reflect the "distance" between the anchored slope and other stability grades.

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“…e differences in the mechanical behavior of soil-rock mixture samples were studied in depth [29]. Based on the previous research studies [30][31][32], Yang et al [33] studied the elastic modulus of the frozen soil-rock mixture in the freezing condition by establishing a suitable micromechanical model. Coli et al [34] performed a largescale field test, and the test results show that there is a positive correlation between the internal friction angle and the rock block content.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation on the Seepage Properties of Soil‐Rock Mixture

Zhao

Wang

Meng

et al. 2018

Advances in Materials Science and Engineering

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To reveal the mechanism of water inrush in a fault tunnel and study the influence of different structures on the seepage process, a filling structure composed of a soil skeleton (fixed particles), a movable particles phase, and a water phase is constructed to simulate the rock mass broken by the fault. Fluent software is used to simulate the migration of the water phase in three different filling structures under different initial water velocities and dynamic viscosities. The variation law of the seepage time with the initial water velocity and dynamic viscosity in the three types of filling structures is obtained. The research shows the following: (1) the looser the filling structure, the greater the influence of gravity on the water phase seepage; the more compact the filling structure, the greater the spread range of the water phase and the more uniform the spread of the water phase. (2) The seepage time decreases with the increase in the initial water velocity. The seepage time and initial water velocity can be fitted by an exponential function. The effect of initial water velocity on seepage time is much greater than that of the structure. (3) The seepage time is related to both the dynamic viscosity and the structure. The seepage time increases with the increase in dynamic viscosity.

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Estimation of Elasticity of Porous Rock Based on Mineral Composition and Microstructure

Cited by 18 publications

References 31 publications

Forecasting Factor of Safety of Slopes Stability Using Several Machine Learning Techniques

Forecasting Factor of Safety of Slopes Stability Using Several Machine Learning Techniques

Research on the stability evaluation method of anchored slopes based on group decision making and matter element analysis

Numerical Simulation on the Seepage Properties of Soil‐Rock Mixture

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