Degradation of Mechanical Behavior of Sandstone under Freeze-Thaw Conditions with Different Low Temperatures

Gao, Ju; Xu, Chao; Xi, Yan; Fan, Lifeng

doi:10.3390/app112210653

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…DCS of rocks was determined using Hopkinson separated compression rods, with confining pressure gradients of 0, 2, 5, and 10 MPa, and an impact pressure range of 0.2 to 0.6 MPa. This paper conducted intelligent prediction research based on ten influencing factors affecting the DCS of rocks under freeze-thaw cycles [28,[47][48][49][50][51] , including FTC, CP, IP, DD, NWC, WA, ADW, P, PWV, and SR. After thorough investigation, this study amassed a total of 216 sets of data for the training and testing sets. The ratio between the training and testing sets was established at 8:2, with the initial 173 groups designated as training samples and the final 43 groups allocated for testing.…”

Section: Raw Data Processingmentioning

confidence: 99%

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

Lv,

Zhang,

Zhang

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The combined impact of freeze-thaw cycles and dynamic loads significantly influences the long-term durability of rock engineering in high-cold regions. Consequently, investigating the dynamic compressive strength (DCS) of rocks subjected to freeze-thaw cycles has emerged as a crucial area of scientific research to advance rock engineering construction in cold regions. Presently, the determination of the DCS of rocks under freeze-thaw cycles primarily relies on indoor experiments. However, this approach has faced criticism due to its drawbacks, including prolonged duration, high costs, and reliance on rock samples. To address these limitations, the exploration of using artificial intelligence technology to develop more accurate and convenient DCS prediction models for rocks under freeze-thaw cycles is a promising attempt. In this context, this paper introduces a DCS prediction model for rocks under freeze-thaw cycles, which integrates the Sparrow Search Algorithm (SSA) with Random Forest (RF). Firstly, employing a dataset of 216 samples, Principal Component Analysis (PCA) is utilized to reduce the dimensionality of ten influential factors. Subsequently, five optimization algorithms are employed to optimize the hyperparameters of both the BP and RF algorithms. Finally, a comprehensive evaluation and comparative analysis are carried out to assess the predictive performance of the optimized model, using evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).The research findings demonstrate that the SSA-RF model exhibits the best predictive performance, surpassing the other nine models in terms of generalization. The prediction model proposed in this study has good applicability for predicting DCS of freeze-thaw rock in cold regions, and also provides new ideas for the combination of machine learning and rock mass engineering in cold regions.

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Section: Raw Data Processingmentioning

confidence: 99%

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

Lv,

Zhang,

Zhang

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…If there is no water in the pores or micro-cracks, there is no so-called F-T damage, i.e., dry rocks are hardly affected by F-T cycles [29]. When the temperature rose, the ice in the pores or micro-cracks melted, accompanied the release of frozen stress and the migration of water, thus accelerating the damage [30]. After repeated F-T cycle tests, therefore, numerous defects inside the samples caused the changes in In general, the F-T damage of rocks is mainly affected by the following factors: lithology, moisture content, number of F-T cycles, ionic solution, temperature range, and stress state [26,27].…”

Section: Effect On Dry Densitymentioning

confidence: 99%

Effect of Freeze-Thaw Damage on the Physical, Mechanical, and Acoustic Behavior of Sandstone in Urumqi

Sha

2022

Applied Sciences

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The Urumqi area in China is a seasonally cold region, and the rock structures in the region are susceptible to freeze-thaw (F-T) weathering. Therefore, this study investigated the effect of F-T on the physical, mechanical, and fracture behavior of sandstone from Urumqi. The acoustic emission method (AE) was used to determine the stress thresholds for the initiation and development of cracks in the samples under cyclic F-T action. The results suggested that parameters such as P-wave velocity, elastic modulus, and peak stress presented a significant negative correlation with F-T damage, while porosity exhibited a close positive correlation. The elastic modulus of the sample was more sensitive to the F-T action with the smallest half-life (27 cycles) and the largest decay factor (0.0254). In addition, the stress threshold for micro-cracks development and macro-cracks initiation in the samples decreased with increasing F-T damage. After 30 F-T cycles, the stress threshold for micro-cracks propagation in the samples decreased from 20.73 MPa to 5.02 MPa by approximately 76%. The normalized stress threshold for the macro-cracks initiation was also decreased from 0.93 to 0.71. Moreover, the macro-cracks damage zone of the samples showed an increasing trend with F-T damage, from 7% under natural conditions to 29% after 30 cycles. It is concluded that F-T action lowers the stress thresholds for cracks development in sandstone in the Urumqi area, posing serious safety concerns for mass rock engineering in this area.

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“…It is a good choice to fill the cracks and joints of rock slopes with mortar [12]. This method can prevent melt water from flowing into the cracks between rock masses, thereby improving the integrity, strength, stiffness and F-T resistance of rock and soil [13,14]. However, one of the main problems of the filling method is to use a large amount of cement and sand, but the current unsustainable exploitation and non-renewable natural sand have led to less time for sand [15,16].…”

Section: Introductionmentioning

confidence: 99%

Microstructure and Shear Mechanical Properties of Fractured Rock Mass Reinforced by Aeolian-Sand (AS) Cement Mortar after Freeze-Thaw (F-T) Cycles

Liu,

Chen,

Zhang

et al. 2024

Preprint

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The rock joint fissure slope in cold regions is prone to deformation and failure problems such as relaxation, tension and collapse under freeze-thaw (F-T) cycles. Based on the ideology of clean production and promoting the structural integrity of geotechnical projects in cold areas, this study designed and prepared aeolian-sand (AS) to replace part of river sand as a filling mortar material by taking advantage of the low cost and easy availability of AS. Investigations were carried out to determine the impact of varying proportions of AS in mortar on the microstructure and shear properties of the mortar-rock interface under F-T cycles. Therefore, F-T cycle testing, nuclear magnetic resonance (NMR) testing and shear testing were carried out on grout with different proportions of AS. The results show that the right proportion of AS replacement could enhance the shear deformation resistance and F-T resistance of the mortar-rock interface, and 30% proportion is the optimal proportion. By increasing of F-T cycles, the three types of specimens showed different degrees of particle spalling, shedding and cracking. The slurry-rock interface layer in the A (0%) group exhibited the most severe F-T deterioration, while the B (15%) group showed a lesser degree, and the C (30%) group performed the best. T2 spectrum curve and total spectrum area change law from the microscopic point of view to verify the 30% AS proportion mortar-rock interface F-T resistance is the strongest, followed by 15% AS proportion mortar, the weakest is ordinary mortar. The degradation of the mortar-rock bond is primarily due to the conversion of solid to liquid phases and the movement of internal water caused by fluctuating temperatures. The extent of F-T deterioration in the slurry-rock interface layer is greatly affected by the varying proportions of AS. The shear strength and shear stiffness of specific proportion of AS decreased with the increase of F-T cycles. For same F-T cycles, the shear strength and shear stiffness of group C (30%) were the highest, followed by group B (15%) and group A (0%). The grout material not only reinforces the F-T joint fissure slope, but also reduces the cost of grout reinforcement and promotes the clean production of geotechnical structures.

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Degradation of Mechanical Behavior of Sandstone under Freeze-Thaw Conditions with Different Low Temperatures

Cited by 6 publications

References 53 publications

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

Effect of Freeze-Thaw Damage on the Physical, Mechanical, and Acoustic Behavior of Sandstone in Urumqi

Microstructure and Shear Mechanical Properties of Fractured Rock Mass Reinforced by Aeolian-Sand (AS) Cement Mortar after Freeze-Thaw (F-T) Cycles

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