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

Estimation of elastic parameters of porous rock like the compressibility of sandstone is scientifically important and yet an open issue. This study illustrates the estimation of the elastic compressibility of sandstone (ECS) based on the assumption that the ECS is determined closely by the mineral composition and microstructures. In this study, 37 samples are collected to evaluate the estimations of the ECS obtained by different methods. The regression analysis is first implemented using the 37 samples. The results show that ECS exhibits linear relations with the rock minerals, pores, and applied compressive stress. Then the support vector machine (SVM) optimized by the particle swarm optimization algorithm (PSO) is examined to generate estimations of the ECS based on the mineral composition and microstructures. The SVM is trained with 30 samples to search for optimal parameters using the PSO, and thus the estimation model is established. Afterwards, this model is validated to give predictions of the left 7 samples. By comparison with the regression methods, the proposed strategy, that is, the PSO optimized SVM, performs much better on the training samples and shows a good capability in generating estimations of the ECS of the 7 testing samples based on the mineral composition and microstructures

Evaluation and improvement of some numerical models for the analysis of slope stability and rock burst

La rupture des pentes et l’éclatement des roches, qui représentent deux types de risques naturels fréquents dans le monde, peuvent engendrer des conséquences économiques importantes et des pertes en vie humaine. Malgré que les phénomènes soient étudiés depuis de longues années, il reste encore des questions ouvertes et sans réponse et il est donc encore nécessaire poursuivre les recherches sur cette thématique. Le présent travail de thèse est consacré la modélisation numérique de la stabilité des grandes pentes et de l’éclatement des massifs rocheux en utilisant des méthodes basées sur l’intelligence artificielle en proposant des modifications et des améliorations de telles méthodes. En se basant sur des observations de déplacements de terrain, le glissement de terrain, qui est phénomène commun de la rupture de pentes, est étudié par le processus de Gauss afin de prédire son apparition temporelle. Ensuite, la question d’évaluation de la stabilité des pentes est abordée en utilisant la stratégie de machine à vecteurs de pertinence (RVM) avec des hyper-paramètres adaptatifs. Une approche itérative est proposée afin de déterminer les valeurs optimales des hyper-paramètres. Afin d’améliorer la prédiction, l’évaluation complète de la stabilité des pentes est réalisée en proposant un modèle basé sur la théorie de flou (CM) associé à un processus analytique d’hiérarchisation pondérée (WAHP). Ce modèle est utilisé à l’évaluation de la stabilité de la pente de rive gauche de la centrale hydroélectrique de Jinping 1, dans la région Sud-Ouest de Chine. Enfin, dans la dernière partie, la problématique de l’éclatement des massifs rocheux est abordée en utilisant des modèles basés sur la théorie du flou, en se basant sur une synthèse de 164 cas réels. Des comparaisons entre les résultats numériques et des données de terrain sont présentées pour de différents cas étudiés dans cette thèse.Slope failures and rock burst, which are two typical types of geological hazards, create tremendous economic damages and cause massive losses to the health of domestic humans or animals every year throughout the world. The severe situation implies that they are still in need to be further studied despite the fact that they have been discussed for a long time. The present work is devoted to presenting the analysis of slope failures and rock burst using some computational intelligent models with modifications and improvements. Then landslide, a common type of slope failure, is analyzed for time occurrence prediction using the Gaussian Process by means of field-observed displacement series. After that, the problem of slope stability evaluation is discussed using the strategy of relevance vector machine (RVM) with adaptive hyper-parameter. An iteration approach is presented to find optimal hyper-parameter values in this chapter. Afterwards, the comprehensive evaluation of slope stability is carried out with the cloud model (CM) and weighted analytical hierarchy process (WAHP) closely related to the left abutment slope of Jinping 1 Hydropower Station, southwest of China. Finally, prediction of rock burst classification is engaged using the cloud models synthesized with the attribution weights on the basis of 164 rock burst cases. In each modeling of the associated problems, comparisons are given on the performance of each strategy as well as some evaluations

Evaluation and improvement of some numerical models for the analysis of slope stability and rock burst

La rupture des pentes et l éclatement des roches, qui représentent deux types de risques naturels fréquents dans le monde, peuvent engendrer des conséquences économiques importantes et des pertes en vie humaine. Malgré que les phénomènes soient étudiés depuis de longues années, il reste encore des questions ouvertes et sans réponse et il est donc encore nécessaire poursuivre les recherches sur cette thématique. Le présent travail de thèse est consacré la modélisation numérique de la stabilité des grandes pentes et de l éclatement des massifs rocheux en utilisant des méthodes basées sur l intelligence artificielle en proposant des modifications et des améliorations de telles méthodes. En se basant sur des observations de déplacements de terrain, le glissement de terrain, qui est phénomène commun de la rupture de pentes, est étudié par le processus de Gauss afin de prédire son apparition temporelle. Ensuite, la question d évaluation de la stabilité des pentes est abordée en utilisant la stratégie de machine à vecteurs de pertinence (RVM) avec des hyper-paramètres adaptatifs. Une approche itérative est proposée afin de déterminer les valeurs optimales des hyper-paramètres. Afin d améliorer la prédiction, l évaluation complète de la stabilité des pentes est réalisée en proposant un modèle basé sur la théorie de flou (CM) associé à un processus analytique d hiérarchisation pondérée (WAHP). Ce modèle est utilisé à l évaluation de la stabilité de la pente de rive gauche de la centrale hydroélectrique de Jinping 1, dans la région Sud-Ouest de Chine. Enfin, dans la dernière partie, la problématique de l éclatement des massifs rocheux est abordée en utilisant des modèles basés sur la théorie du flou, en se basant sur une synthèse de 164 cas réels. Des comparaisons entre les résultats numériques et des données de terrain sont présentées pour de différents cas étudiés dans cette thèse.Slope failures and rock burst, which are two typical types of geological hazards, create tremendous economic damages and cause massive losses to the health of domestic humans or animals every year throughout the world. The severe situation implies that they are still in need to be further studied despite the fact that they have been discussed for a long time. The present work is devoted to presenting the analysis of slope failures and rock burst using some computational intelligent models with modifications and improvements. Then landslide, a common type of slope failure, is analyzed for time occurrence prediction using the Gaussian Process by means of field-observed displacement series. After that, the problem of slope stability evaluation is discussed using the strategy of relevance vector machine (RVM) with adaptive hyper-parameter. An iteration approach is presented to find optimal hyper-parameter values in this chapter. Afterwards, the comprehensive evaluation of slope stability is carried out with the cloud model (CM) and weighted analytical hierarchy process (WAHP) closely related to the left abutment slope of Jinping 1 Hydropower Station, southwest of China. Finally, prediction of rock burst classification is engaged using the cloud models synthesized with the attribution weights on the basis of 164 rock burst cases. In each modeling of the associated problems, comparisons are given on the performance of each strategy as well as some evaluations.LILLE1-Bib. Electronique (590099901) / SudocSudocFranceF

Advances in Deformation and Permeability Evolution during Creep of Rocks

The goal of this paper is to review the research advances in deformation and permeability evolution during the creep of rocks in geoengineering problems through aspects of experiments, models, and methods. On the experimental side, we reviewed the reports related to creep-permeability evolution in resolving real geoengineering problems. In the section on the constitutive model, we summarized the equations of the relationship between creep deformation and permeability evolution in reproducing the interaction mechanism of creep-permeability. In addition, in the section on the numerical modeling method, we examined the modelling methods able to apply the mechanism of creep-permeability evolution as a real problem. Our report concludes that it is important to conduct experiments to demonstrate the deformation and permeability evolution during the creep of heterogeneous rocks in multi physics fields (Thermal-Mechanics-Hydraulic-Chemical). Additionally, we confirm that it is necessary to improve the proposed equation of permeability evolution by considering strain and damage. Finally, this paper suggests that the DEM (Discrete Element Method) is available to evaluate the influence of the heterogeneousness of rocks on deformation and permeability evolution

A Study on the Creep Characteristics of Airport Viscous Subsoil Based on Unsaturated Stress Level

The present study takes the ratio of the matric suction to the net vertical stress and the ratio of the matric suction to the net mean stress as new unsaturated stress levels f and F, respectively. Based on the laboratory tests and theoretical derivation, the modified one-dimensional Mesri creep model and three-dimensional creep model were established, which takes the unsaturated stress level into account. Then, the one-dimensional and three-dimensional creep characteristics of the unsaturated viscous subsoil of an airport under different unsaturated stress levels were analyzed. The following conclusions could be drawn: (1) under different stress levels, the one-dimensional creep deformation of unsaturated soil has a power function relationship with time, and the change rate exponentially decreases with the stress level, which can be well-expressed by the proposed modified one-dimensional Mesri creep model; (2) under different stress levels, the three-dimensional creep strain of the unsaturated soil shows a hyperbolic curve with time and a near-linear relationship at the semilogarithmic coordinate, which can be well-expressed by the proposed modified three-dimensional creep model; (3) under different stress levels, both the one-dimensional creep and three-dimensional creep of the unsaturated soil can be divided into two stages, which are the accelerated creep stage and stable creep stage

Research on the Prediction of the Time of Initial Support of Soft Rock Tunnel Based on the Optimized Longitudinal Deformation Profile

The longitudinal deformation profile (LDP) of the surrounding rock can intuitively and effectively reflect that the deformation of the surrounding rock of the tunnel wall is affected by the “spatial effect” at the front end of the excavation face during the tunnel excavation process, and provides a theoretical basis for the best time for the construction of the supporting structure. Taking a large-section tunnel of soft rock as an example, the LDP equation (displacement release coefficient) of the surrounding rock is derived based on Unlu and Gercek, and the optimization and improvement are proposed after comprehensively considering Poisson’s ratio, the elastic modulus, cohesion, internal friction angle, blasting parameters, etc., using FLAC3D to analyze the rationality and validity of improving the LDP equation of the surrounding rock. The results show that: (1) by adding the “expanded convergent function” in the x ≥ 0 section, the LDP equation of surrounding rock is optimized and improved, and the correlation coefficient of the data is increased from the original R-square = 0.8 to R-square = 0.95; (2) by comparing with the numerical simulation data, the improved LDP equation of the surrounding rock can better match it, which confirms that the improved LDP equation of surrounding rock is more reasonable and practical; (3) it is proposed that the displacement release coefficient value is the best time to apply support when the displacement increment of the surrounding rock has a sharp increase point, and it is concluded that when the Class-III surrounding rock is constructed by the long step method, it is best to start supporting at about x = 2.24 m from the tunnel face. When the Class-IV surrounding rock is constructed by the reserved core soil method, it is best to start supporting at about x = 1.47 m from the face of the tunnel

Research on the Prediction of the Time of Initial Support of Soft Rock Tunnel Based on the Optimized Longitudinal Deformation Profile

The longitudinal deformation profile (LDP) of the surrounding rock can intuitively and effectively reflect that the deformation of the surrounding rock of the tunnel wall is affected by the “spatial effect” at the front end of the excavation face during the tunnel excavation process, and provides a theoretical basis for the best time for the construction of the supporting structure. Taking a large-section tunnel of soft rock as an example, the LDP equation (displacement release coefficient) of the surrounding rock is derived based on Unlu and Gercek, and the optimization and improvement are proposed after comprehensively considering Poisson’s ratio, the elastic modulus, cohesion, internal friction angle, blasting parameters, etc., using FLAC3D to analyze the rationality and validity of improving the LDP equation of the surrounding rock. The results show that: (1) by adding the “expanded convergent function” in the x ≥ 0 section, the LDP equation of surrounding rock is optimized and improved, and the correlation coefficient of the data is increased from the original R-square = 0.8 to R-square = 0.95; (2) by comparing with the numerical simulation data, the improved LDP equation of the surrounding rock can better match it, which confirms that the improved LDP equation of surrounding rock is more reasonable and practical; (3) it is proposed that the displacement release coefficient value is the best time to apply support when the displacement increment of the surrounding rock has a sharp increase point, and it is concluded that when the Class-III surrounding rock is constructed by the long step method, it is best to start supporting at about x = 2.24 m from the tunnel face. When the Class-IV surrounding rock is constructed by the reserved core soil method, it is best to start supporting at about x = 1.47 m from the face of the tunnel

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

Estimation of elastic parameters of porous rock like the compressibility of sandstone is scientifically important and yet an open issue. This study illustrates the estimation of the elastic compressibility of sandstone (ECS) based on the assumption that the ECS is determined closely by the mineral composition and microstructures. In this study, 37 samples are collected to evaluate the estimations of the ECS obtained by different methods. The regression analysis is first implemented using the 37 samples. The results show that ECS exhibits linear relations with the rock minerals, pores, and applied compressive stress. Then the support vector machine (SVM) optimized by the particle swarm optimization algorithm (PSO) is examined to generate estimations of the ECS based on the mineral composition and microstructures. The SVM is trained with 30 samples to search for optimal parameters using the PSO, and thus the estimation model is established. Afterwards, this model is validated to give predictions of the left 7 samples. By comparison with the regression methods, the proposed strategy, that is, the PSO optimized SVM, performs much better on the training samples and shows a good capability in generating estimations of the ECS of the 7 testing samples based on the mineral composition and microstructures

Multi-step real-time prediction of hard-rock TBM penetration rate combining temporal convolutional network and squeeze-and-excitation block

Abstract Accurate penetration rate prediction enhances rock-breaking efficiency and reduces disc cutter damage in tunnel boring machine (TBM) construction. However, this process faces significant challenges such as the high uncertainty of ground conditions and the complexity of maintaining optimal TBM operation in long and large tunnels. To address these challenges, we propose TCN-SENet++, a novel hybrid multistep real-time penetration rate prediction model that combines a temporal convolutional network (TCN) and a squeeze-and-excitation (SENet) block for aided tunneling. This study aims to demonstrate the application of TCN-SENet++, as well as other models such as RNN, LSTM, GRU, and TCN, for TBM penetration rate prediction. The model was developed using actual datasets collected from the Yin-Song diversion project. We employ a 30-s time step to predict the future time steps of the penetration rate (1st, 3rd, 5th, 7th, and 9th). The features that influence the penetration rate, such as the cutterhead torque, thrust, and cutterhead power, were considered. A comparative analysis using the mean absolute error and mean squared error revealed that the TCN-SENet++ model outperformed the other models, including RNN, LSTM, GRU, TCN, and TCN-SENet+. In comparison, TCN-SENet++ achieved average MSE reductions of 18%, 6%, 3%, 1%, and 2%, respectively. The TCN-SENet++ model demonstrated fewer errors in the new project, validating its effectiveness and suitability for real-time penetration rate prediction in TBM construction

Anisotropic strength, deformation and failure of gneiss granite under high stress and temperature coupled true triaxial compression

The anisotropic mechanical behavior of rocks under high-stress and high-temperature coupled conditions is crucial for analyzing the stability of surrounding rocks in deep underground engineering. This paper is devoted to studying the anisotropic strength, deformation and failure behavior of gneiss granite from the deep boreholes of a railway tunnel that suffers from high tectonic stress and ground temperature in the eastern tectonic knot in the Tibet Plateau. High-temperature true triaxial compression tests are performed on the samples using a self-developed testing device with five different loading directions and three temperature values that are representative of the geological conditions of the deep underground tunnels in the region. Effect of temperature and loading direction on the strength, elastic modulus, Poisson's ratio, and failure mode are analyzed. The method for quantitative identification of anisotropic failure is also proposed. The anisotropic mechanical behaviors of the gneiss granite are very sensitive to the changes in loading direction and temperature under true triaxial compression, and the high temperature seems to weaken the inherent anisotropy and stress-induced deformation anisotropy. The strength and deformation show obvious thermal degradation at 200 °C due to the weakening of friction between failure surfaces and the transition of the failure pattern in rock grains. In the range of 25 °C–200 °C, the failure is mainly governed by the loading direction due to the inherent anisotropy. This study is helpful to the in-depth understanding of the thermal-mechanical behavior of anisotropic rocks in deep underground projects