Disintegration Mechanism and Swelling–Consolidation Characteristics of Saturated Disintegrated Carbonaceous Mudstone

Fu, Hongxun; Yang, Qi-Yi; Zeng, Lingzao; Gao, Qian-Feng; Wen, Wei; Chen, Lu

doi:10.1007/s13369-022-07053-4

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…A tunnel passing through a low and gentle hilly landform area is a separate tunnel. The main surrounding rock is slightly weathered to fully weathered carbonaceous mudstone, with a large number of dense joints and bedding friction mirror surfaces [21] . The carbonaceous mudstone rock samples at the site are shown in Figure 4 below.…”

Section: Project Overviewmentioning

confidence: 99%

Research on Prediction of Weathering Degree of Carbonaceous Mudstone Based on Entropy Weight-Cloud Model

Bai

2023

Preprint

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The difference in the degree of rock weathering has always been an important concern in tunnel engineering. In view of the randomness and ambiguity of determining the degree of rock weathering, four quantitative indicators including wave velocity ratio Kv, weathering coefficient Kf, cohesion c and chemical alteration index CIA are selected to establish an evaluation index system. The entropy weight method is used to determine the weight of each index, and the collective cloud model theory is used to establish a quantitative prediction model of the weathering degree of carbonaceous mudstone. Comparing five groups of engineering examples with the established model, it is found that there are deviations in the results of the two groups. The reason for this result is that the traditional determination of the degree of rock weathering is uncertain, and there is no unified standard for quantitative analysis. The entropy weight-normal cloud model comprehensively considers multiple influencing factors, eliminates uncertainty as much as possible, and gives the results of quantitative analysis, which has good practicability and reliability. It can provide a new idea for the weathering degree of carbonaceous mudstone.

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Section: Project Overviewmentioning

confidence: 99%

Research on Prediction of Weathering Degree of Carbonaceous Mudstone Based on Entropy Weight-Cloud Model

Bai

2023

Preprint

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“…Muddy shale is a rock that occurs during the transition between mudstone and shale, which can expand and contract easily in the presence of water (Fu et al, 2023;Gao et al, 2022). The water saturation-dehydration circulation will lead to the propagation of hydraulic fractures and the formation of fracture network, which has important implications for oil and gas exploration, engineering construction, geological disaster prevention and so on (Zheng et al, 2018;Zeng et al, 2021;Li et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Automated crack detection and digital modeling of hydraulic fracture propagation in muddy shale using deep learning based on multi-scale feature and residual convolution improved U-net model

QU,

JIA,

CHEN

et al. 2024

Preprint

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The extension of fine microscopic cracks in muddy shale during water saturation-deydration circulation has an important role in the propagation of hydraulic fractures and the formation of fracture network. However, traditional image processing methods for segmenting CT scan images of muddy shale are prone to low efficiency and poor accuracy, as well as lack automation and intelligence. This study proposes a muddy shale crack segmentation network (MSCS-Net) based on the U-Net model that fuses the residual network and multi-scale features of Convolutional Neural Networks (CNNs). The proposed MSCS-ett efficiently segmented muddy shale cracks in CT scanned images after a degradation cycle, allowing for both qualitative and quantitative analysis. The results showed that the values of precision (P), recall (R), F1 score (F1_score), Intersection and Union Ratio (IoU) and Pixel Accuracy (PA) of the proposed MSCS-Net were 91.27%, 93.89%, 92.56%, 85.32% and 98.34%, respectively. Besides, the detection performance of the MSCS-Net was also compared with that of the other three different deep learning models (U-Net, U-Net3 + and Attention U-Net). The test results have demonstrated the superiority of the MSCS-Net over the other three network models in crack detection, localization and segmentation.

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“…The unique genesis, composition and internal structure of granite residual soil lead to its obvious particularity in engineering properties. Many scholars at home and abroad attach great importance to its engineering properties [6][7][8][9][10][11][12]. The granitic residual soil of different regions and different levels have obvious differences in grain size composition due to the different nature of parent rock, weathering degree and soil formation conditions [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Study on The Influence of Grain Size Composition on Engineering Properties of Granite Residual Soil

Tang¹,

Chen²,

Zhou³

et al. 2023

Pol. J. Environ. Stud.

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The parent rock properties, weathering degree and soil formation conditions of soil in different regions and different levels are different, which results in the obvious difference of grain size composition of soil. At present, the research on granite residual soil often ignores the mechanism of its grain size composition on engineering properties. In this study, a series of soil samples with different grain size composition were configured based on the content of gravel grain in the granite residual soil. This paper mainly studies the influence of the content of gravel grain group and fine grain group of the compaction and strength of granite residual soil. The results show that the optimal moisture content and cohesion of granite residual soil are mainly controlled by the content of fine grain, and the maximum dry density and internal friction angle are mainly controlled by gravel grain. For each 1% increase in the fine grain content of the soil, the optimal moisture content of the soil will approximately increase by 0.11%, and correspondingly, the cohesion of the soil will approximately increase by 0.27 kPa. In addition, the engineering properties of granite residual soil with different grain size composition are also affected by soil structure. Finally, based on the microstructure characteristics of granite residual soil, three different aggregates are proposed, and seven micro models with different structural characteristics are established. Micro models established by different aggregates can control different engineering properties of soil. The research results are helpful to understand the relationship between the engineering properties and grain size composition of granite residual soil and provide reference for the refinement of its engineering classification.

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Disintegration Mechanism and Swelling–Consolidation Characteristics of Saturated Disintegrated Carbonaceous Mudstone

Cited by 9 publications

References 27 publications

Research on Prediction of Weathering Degree of Carbonaceous Mudstone Based on Entropy Weight-Cloud Model

Research on Prediction of Weathering Degree of Carbonaceous Mudstone Based on Entropy Weight-Cloud Model

Automated crack detection and digital modeling of hydraulic fracture propagation in muddy shale using deep learning based on multi-scale feature and residual convolution improved U-net model

Study on The Influence of Grain Size Composition on Engineering Properties of Granite Residual Soil

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