Mechanical properties and energy characteristics of coal at different depths under cyclic triaxial loading and unloading

Zhang, Anlin; Xie, Heping; Zhang, Ru; Gao, Mingzhong; Xie, Jing; Jia, Zheqiang; Ren, L.; Zhang, Zetian

doi:10.1016/j.ijrmms.2022.105271

Cited by 68 publications

(18 citation statements)

References 57 publications

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“…Typical projects in these cold regions include major national construction projects such as the Sichuan Tibet Railway, Yunnan Tibet Railway, and New Tibet Railway [6][7][8] , which are the pillars of human activities in remote areas [9][10][11] . However, in high-altitude and cold regions, rock masses often contain a large number of initial defects, such as joints and cracks [12,13] . Moreover, due to extreme climate, environmental conditions, and other geological factors, these areas are more susceptible to severe frost damage, resulting in unstable tunnel rock frost heave, slope erosion, and collapse [14] .…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…In addition the energy variation is a complex phenomenon in tunnelling and underground mining projects [20]. This research investigates into examining the strain energy characteristics of two rock types when subjected to uniaxial compression loading, with a particular focus on incorporating both dissipated and elastic energy components.…”

Section: Introductionmentioning

confidence: 99%

Influence of chemical composition and discontinuities on energy transformation and rock mass behaviour: Insights into geological dynamic

Abbas,

Li,

Fissha

et al. 2024

The Journal of Engineering

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In this study, efforts were made to incorporate the influence of discontinuities and failure modes of rock into the classification of rock masses. The past tectonic activities may create microfractures in the rock body therefore the failure moods have been determined carefully under uniaxial compression. The results of the discontinuity analysis, conducted through kinematic study, highlighted the significant impact of wedge failure on the failure of the rock mass. In correlating the geological strength index with rock mass rating, it was observed that joint volume played a negative role, whereas compressive strength played a positive role. These correlations are particularly applicable for a certain rock type, as the compressive strength is inherently dependent on the type of rock. The analysis of failure modes under uniaxial compression reveals that the dissipation energy coefficient initially undergoes rapid increase before reaching its minimum value at the failure stage. The microstructures of the rock effect significantly the elastic and dissipation energy characteristics. Specifically, the axial splitting failure mode emerges as predominant. Given the area's past tectonic activity, these results emphasize the impact of microfractures within the rock body. Relating the failure criteria with the chemical composition of rock types reveals that rocks abundant in SiO2, such as gabbronorite, tend to exhibit brittle failure. Additionally, a dominance of Al2O3 over Fe2O3 suggests a predisposition towards brittle failure, while an increased ratio of CaO to MgO implies increased susceptibility to compression.

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“…Jing 29 has proposed a new noise cancellation method to analyse the acoustic emission signal after noise cancellation, which avoids modal mixing and improves efficiency. Zhang 30 found that coal rocks of different burial depths exhibited different mechanical properties and damage patterns when subjected to triaxial cyclic loading.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission characteristics and energy evolution law of rock damage process of different pore structures under cyclic loading

Jiang,

Dang,

Chen

et al. 2024

Sci Rep

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The AE and damage characteristics of three types of pore-structured rock under the same working conditions are studied by means of uniaxial cyclic loading and unloading tests. The results suggest that with repeated loading and unloading, AE ringing increases as a “jump”, and the denser the structure, the earlier the “jump” occurs. The AE cumulative energy shows a “step” upward trend, but there is a significant difference in the “step” spacing. By comparing the energy distribution of rocks with different pore structures, it can be seen that the smaller the porosity and the smaller the pore size, the greater the energy input and storage, and the earlier the internal failure. Compared with the other two energy-based damage calculation methods, the damage calculation method defined in this paper is closer to the true internal damage level of the rock loading cycle. The NSE value of the modified damage variable calculation method was significantly improved and it was shown that the dissipated energy before pore compaction is the main energy causing damage, after pore compaction the combined effects of dissipated energy and plastic deformation energy result in rock damage.

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Mechanical properties and energy characteristics of coal at different depths under cyclic triaxial loading and unloading

Cited by 68 publications

References 57 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

Influence of chemical composition and discontinuities on energy transformation and rock mass behaviour: Insights into geological dynamic

Acoustic emission characteristics and energy evolution law of rock damage process of different pore structures under cyclic loading

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