Macro‐meso damage cracking and volumetric dilatancy of fault block‐in‐matrix rocks induced by freeze–thaw‐multistage constant amplitude cyclic (F‐T‐MSCAC) loads

Wang, Yu; Yi, Xuefeng; Cai, Meifeng; Sun, Tao

doi:10.1111/ffe.13798

Cited by 20 publications

(18 citation statements)

References 38 publications

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“…In soil-rock mixtures, the rock content is a key parameter in determining the physical-mechanical properties and directly affects the weight, cohesion, and internal friction angle (Kalender et al, 2014). The rock contact, the rock content greatly contribute to the stability coefficient of soil-rock nixture slopes (Wang et al, 2022a;Wang et al, 2022b;Wang et al, 2022c). If there are multicollinearities among the input parameters in machine learning, the accuracy of the prediction model can be affected (Hitouri et al, 2022;Selamat et al, 2022;Xia et al, 2022).…”

Section: Sample Analysismentioning

confidence: 99%

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Zhang²,

Wang³

et al. 2023

Front. Earth Sci.

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Soil-rock mixtures are geological materials with complex physical and mechanical properties. Therefore, the stability prediction of soil-rock mixture slopes using machine learning methods is an important topic in the field of geological engineering. This study uses the soil-rock mixture slopes investigated in detail as the dataset. An intelligent optimization algorithm-weighted mean of vectors algorithm (INFO) is coupled with a machine learning algorithm. One of the new ensemble learning models, which named IN-Voting, is coupled with INFO and voting model. Twelve single machine learning models and sixteen novel IN-Voting ensemble learning models are built to predict the stability of soil-rock mixture slopes. Then, the prediction accuracies of the above models are compared and evaluated using three evaluation metrics: coefficient of determination (R2), mean square error (MSE), and mean absolute error (MAE). Finally, an IN-Voting ensemble learning model based on five weak learners is used as the final model for predicting the stability of soil-rock mixture slopes. This model is also used to analyze the importance of the input parameters. The results show that: 1) Among 12 single machine learning models for the stability prediction of soil-rock mixture slopes, MLP (Multilayer Perceptron) has the highest prediction accuracy. 2) The IN-Voting model has higher prediction accuracy than single machine learning models, with an accuracy of up to 0.9846) The structural factors affecting the stability of soil-rock mixture slopes in decreasing order are the rock content, bedrock inclination, slope height, and slope angle.

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Section: Sample Analysismentioning

confidence: 99%

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Zhang²,

Wang³

et al. 2023

Front. Earth Sci.

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“…But the aforementioned methods do not allow the visualization of specimens, which has limited our understanding of the microcosmic mechanisms therein. In order to make up such deficiency, traditional X-ray CT, synchrotron X-ray micro CT, and even electronuclear machine-CT have been successively incorporated into triaxial tests with observational accuracies ranging from mm to μm scale [31,32]. The study used insitu CT aided torsional shear tests for the visualization of C-T fractures, which has not previously been attempted despite its potential to significantly enhance our understanding of fracture formation process and the disaster-dominating mechanisms that originate from fracture evolution.…”

Section: Geofluidsmentioning

confidence: 99%

Experimental Study on Compression and Torsion Fracture within 3D Printed Cementation-Weak and Porosity-High Sandstones

et al. 2023

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Cretaceous sandstones have weak cementation and high porosity while exhibit a high apparent brittleness. Compression and torsion (C-T) fractures are widely distributed in Cretaceous sandstones due to asymmetric tectonic convergence action. However, studies on C-T fracture formation and the mechanisms causing variability in Cretaceous sandstones containing no oil or gas are rare due to the challenges in sampling intact sandstone cores, despite their significance to mine shaft sinking. Therefore, this study used binder jetting-based 3D printing to prepare artificial Cretaceous sandstone and developed a real-time X-ray computed tomography- (CT-) aided torsion shear apparatus to test them. The test results showed that the 3D printed (3DP) sandstone had characteristic indexes that approached and even exceeded the lower limits of Cretaceous sandstone cores, thereby accurately representing the unavailable cores. Furthermore, the 3DP sandstones had anisotropic properties comparable to the sandstone cores. Under C-T action, the 3DP sandstone exhibited a pronounced strain gradient of 2.0 %/mm perpendicular to fracture inclination. The inclination angles of fractures formed under C-T action tended to increase as the cell pressure increased, and that approached the orientation angles of maximal principal stress. The maximal and minimal principal stresses exerted inclination-slip and width-stretching effects, respectively, on C-T fractures. But the effect of inclination-slip on the C-T fractures was stronger than that of width-stretching. This insight into C-T fracture formation will guide future studies on the fracture evolution and its disaster-dominating mechanisms arisen from disturbances by shaft sinking.

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“…The slope stability of soft rock has been paid more and more attention and studied by more and more scholars. In the field of geotechnical engineering, soft rock has unique physical and mechanical properties, exhibits obvious rheological characteristics under the action of external factors, and has obvious time effect (Guo et al, 2012;Li et al, 2012;Cerfontaine and Collin 2018;Hashemnejad et al, 2021;Bai et al, 2022b;Wang et al, 2022a;Wang et al, 2022b;Wang et al, 2022c) Therefore, soft rock has always been a key and difficult problem in the study. In many soft rock open pit slopes, the rock mass is usually subjected to continuous and repeated stress disturbance, which will affect the stability of the slope and cause slope instability accidents and disasters.…”

Section: Introductionmentioning

confidence: 99%

On the effect of water content on fatigue mechanical behaviors of mud-shale under stress disturbance conditions

Wu²,

Yang³

et al. 2023

Front. Mater.

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This paper aims to reveal the fatigue damage and instability behaviors of mud-shale under multistage increasing-amplitude fatigue loading. The fatigue loading tests combined with real-time acoustic emission (AE) monitoring technique were employed to investigate the influence of water content on the deformation, damage, and fracture characteristics. Testing results show that rock fatigue life decreases with the increase of water content, and the hysteresis curve changes regularly with time. The failure process can be divided into three stages: initial stage, stable development stage and acceleration stage. The acoustic emission output activities were also influenced by the water content. The acoustic emission ring count and acoustic emission energy both decrease with increasing water ratio and the accumulative count and energy are the least for a sample having high water ratio. The acoustic emission activity shows a sudden increase trend at the amplitude-increasing moment, indicating the occurrence of strong damage within rock sample. The damage propagation within a cyclic loading stage is relatively small compared to the stress-increasing moment. The results are helpful to understand the fatigue mechanical responses of water-sensitive soft rock, as well as the slope stability of the open-pit mine. The research results have important theoretical and practical significance for promoting slope treatment and disaster prevention.

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Macro‐meso damage cracking and volumetric dilatancy of fault block‐in‐matrix rocks induced by freeze–thaw‐multistage constant amplitude cyclic (F‐T‐MSCAC) loads

Cited by 20 publications

References 38 publications

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Experimental Study on Compression and Torsion Fracture within 3D Printed Cementation-Weak and Porosity-High Sandstones

On the effect of water content on fatigue mechanical behaviors of mud-shale under stress disturbance conditions

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