Analysis and countermeasures of large deformation of deep-buried tunnel excavated in layered rock strata:A case study

Li, Jiaqi; Wang, Zhifeng; Wang, Yaqiong; Chang, Hongtao

doi:10.1016/j.engfailanal.2023.107057

Cited by 29 publications

(3 citation statements)

References 44 publications

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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). Therefore, an in-depth understanding of the development of cracks in muddy shale and the evolution of the network can provide a theoretical foundation and scientific basis for oil and gas exploration, engineering construction and disaster prevention.…”

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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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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“…Bian et al [16] studied the occurrence mechanism of large deformation for the Huangjiazhai tunnel in China by combining engineering analysis, laboratory analysis, and micro-analysis. Li et al [27] analyzed the large deformation mechanism of the Jiaoding tunnel in layered rock strata and proposed treatment measures based on field tests and theoretical analysis. However, the mechanism underlying the large deformation of a specific project generally calls for targeted research because of the complex geological conditions and ground stress at specific sites.…”

Section: Introductionmentioning

confidence: 99%

Characteristics and Mechanism of Large Deformation of Tunnels in Tertiary Soft Rock: A Case Study

Liu,

Huang,

Ding

et al. 2023

Buildings

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During the excavation of a water-conveyance tunnel in Tertiary soft rocks in China, significant deformation of the surrounding rocks and damage to the support were observed. Substantial horizontal deformation, reaching magnitudes of meters, was observed in the right side wall after a certain period of tunnel excavation. Extensive investigations, including field surveys, monitoring data analysis, laboratory tests, and numerical simulations, were conducted to understand the underlying mechanisms of this large deformation. The section of the tunnel with large deformation consisted of Tertiary sandy mudstone, mudstone interbedded with marl, and glutenite. Laboratory tests and mineral composition analysis revealed that the sandy mudstone and mudstone interbedded with marl exhibited low strength, which was closely related to the water content of the rock specimens. The compressive strength gradually decreased with increasing water content, and when the water content of mudstone interbedded with marl reached 26.96%, the uniaxial compressive strength decreased to only 0.24 MPa. Additionally, sandy mudstone and mudstone interbedded with marl contained a significant amount of hydrophilic minerals, with montmorillonite constituting 30% and 34% of the two rock samples, respectively. The tunnel passed beneath a perennially flowing gully, and a highly permeable glutenite layer was present in the middle of the tunnel. This resulted in groundwater seepage from the inverted arch during excavation, leading to the softening effect on the mudstone interbedded with marl in the lower part of the tunnel. Through numerical simulation and back-analysis techniques, the varying degrees of softening induced by groundwater were quantitatively analyzed in the surrounding rocks on the left and right sides. The study revealed that the large deformation of the tunnel was triggered by two factors: the plastic flow caused by tunnel excavation under the low strength of the surrounding rocks and the softening effect of groundwater. The damage to the support system was primarily attributed to the squeezing and swelling deformation of the surrounding rocks and the non-uniform deformation between different rock layers.

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“…With a thorough understanding and study of the rheological properties of soft rocks like mudstone, shale, sandstone, and phyllite, their behavior can be better predicted and managed. This enables more effective planning and design in constructing tunnels or underground structures, thereby ensuring their long-term stability and safety [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

The Secondary Development and Application of the Improved Nishihara Creep Model in Soft Rock Tunnels

Deng,

Shi,

et al. 2023

Buildings

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Given the complexity and diversity of rock formations, existing constitutive models struggle to accurately portray their mechanical properties, leading to substantial discrepancies between numerical simulation outcomes and reality. This inadequacy fails to meet the demands of numerical analysis in practical engineering. This study first analyzes the physical and mechanical properties of thin-layered carbonaceous phyllite. Subsequently, an improved Nishihara rheological constitutive model is established based on these analyses. Utilizing the secondary development function offered by FLAC3D, the proposed model is further developed. The program’s correctness and reliability are confirmed through a numerical simulation using the triaxial creep test from existing research. Finally, the established constitutive model is applied in the numerical simulation of an actual soft rock tunnel engineering, obtaining results compared to real monitoring data. The results demonstrate that the improved Nishihara model is more effective at describing the creep deformation characteristics of soft rock. Moreover, the findings from this study can serve as a theoretical reference for predicting deformation in soft rock tunnel engineering.

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Analysis and countermeasures of large deformation of deep-buried tunnel excavated in layered rock strata:A case study

Cited by 29 publications

References 44 publications

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

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

Characteristics and Mechanism of Large Deformation of Tunnels in Tertiary Soft Rock: A Case Study

The Secondary Development and Application of the Improved Nishihara Creep Model in Soft Rock Tunnels

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