Quantitative Evaluation and Classification Method of the Cataclastic Texture Rock Mass Based on the Structural Plane Network Simulation

Dong, Shan; Yi, Xiaoyu; Feng, Wenkai

doi:10.1007/s00603-018-1635-6

Cited by 15 publications

(8 citation statements)

References 23 publications

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“…Considering that the cataclastic rock mass samples designed in this experiment were all composed of regularly distributed joints, and the random distribution of the joints was not considered; the trace line nodes index, trace line segment length, and block area that was cut and confined by the trace line were used to evaluate the fragmentation degree of the experimental samples. The trace line nodes index (TLNI) was proposed by Dong et al 1 , and is defined as the number of trace line nodes on a unit trace line in the unit trace line plane area (Fig. 7 a): where is the number of trace line nodes in the unit trace line plane area, and is the number of trace lines in the unit trace line plane area.…”

Section: Methodsmentioning

confidence: 99%

“…A cataclastic rock mass is a poorer type of engineering rock mass, which is extensively developed in large active fault zones, deep canyons, and regions with complex lithological structures 1 , 2 . Due to the high heterogeneity, discontinuity, and anisotropy of a cataclastic rock mass, its integrity and overall strength are low, in addition to its stability, which is the focus of large-scale engineering construction.…”

Section: Introductionmentioning

confidence: 99%

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Experimental study on strength and failure characteristics of sandstone rock mass with complex cataclastic structure using 3D printing models

Dong

Huang

2023

Sci Rep

Self Cite

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A cataclastic rock mass is a poor type of engineering geological rock mass. The determination of the shear failure characteristics and shear strengths of cataclastic rock masses can provide key basis for the design and construction of infrastructure. Physical model samples of a sandstone cataclastic rock mass were first produced by a combination of three-dimensional (3D) printing technology and manual pouring. Shear tests were conducted with respect to the shear stresses parallel to the trace line plane and perpendicular to the trace line plane of the cataclastic rock mass model. Based on an extensive analysis of the shear failure characteristic, shear stress evolution characteristic curve and shear strength. When the shear stress was parallel to the trace line plane, and when the rock block that was cut and confined by the trace line exhibited a significant tip, the end stress concentration effect of the cataclastic rock mass was more significant during the shear process with the anisotropy of the rock block increased. In addition, the shapes of the rock blocks that were confined and cut by the joints were the main influencing factors of the strength of the cataclastic rock mass. When the shear stress was perpendicular to the trace line plane, the structure of the rock wall was the main influencing factor of the deformation and failure process of the shear failure plane and the shear strength. The physical and mechanical properties of the shear failure plane of the cataclastic rock mass were found to be closely related to the joint–rock wall system characteristics of the cataclastic rock mass. Therefore, when determining the shear strength of cataclastic rock mass, the shape and combination form of the rock block, shear direction, and structural failure characteristics of the rock wall should be comprehensively considered during the shear process.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental study on strength and failure characteristics of sandstone rock mass with complex cataclastic structure using 3D printing models

Dong

Huang

2023

Sci Rep

Self Cite

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“…A viable method is to generate the structural plane network of the rock mass by manual drawing or computer simulation based on the actual measured data. Numerous studies have shown that the parameters reflecting the structural plane distribution, such as spacing, trace length, dip direction, and dip angle, obey a probability distribution [54][55][56][57]. Based on the statistical characteristics of the structural plane distribution parameters, the equivalent structural plane network of the rock mass can be generated by using the Monte Carlo method [58][59][60][61].…”

Section: Determination Of Fractal Dimension D Based On Structural Pla...mentioning

confidence: 99%

Determination of Integrity Index Kv in CHN-BQ Method by BP Neural Network Based on Fractal Dimension D

et al. 2023

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The integrity index Kv is the quantitative index in the CHN-BQ method, which can be determined by the acoustic wave test, volume joint number Jv, or empirical judgment. However, these methods are not convenient and require the practitioner to have extensive experience. In this study, a new quantitative evaluation of Kv is proposed to determine Kv accurately and conveniently. A method for determining the fractal dimension D based on the structural plane network simulation is proposed. A quantitative relationship between fractal dimension D and integrity index Kv is established based on the geological information from 80 sampling windows in Mingtang Tunnel. To further consider the effect of structural plane conditions on Kv, a BP neural network is constructed with the fractal dimension D and structural plane condition index R3 as input and Kv as output. The BP neural network is trained by 260 groups of tunnel data and validated by 39 groups of test data. The results show that the correlation coefficient R2 between the predicted Kvp and measured Kvm is 0.93, and the average relative error is 7.51%. In addition, the predicted Kvp from the 39 groups of data is compared with the Kvd determined directly by fractal dimension D. It can be found that the Kvd has a larger error compared with the Kvp, especially in the case of a Kv less than 0.5. Finally, the BP neural network for predicting Kv is applied to the Jiulaopo Tunnel. The maximum relative error between the measured Kvm and the predicted Kvp is 5.13%, and the average relative error is 2.71%. The BP neural network is well trained and can accurately predict Kv based on the fractal dimension D and the structural plane condition index R3.

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“…Cable Support As shown in Figure 13, a tunnel excavation model was established, which included tunnel section, uniformly distributed joints, and dominant joints [15]. e geostress of the model was estimated at 500 m depth, the vertical geostress was 12.5 MPa, the maximum horizontal stress was 21 MPa, and the minimum horizontal stress was 12.5 MPa.…”

Section: Numerical Analysis Of Npr Anchormentioning

confidence: 99%

Application of Constant Resistance and Large Deformation Anchor Cable in Soft Rock Highway Tunnel

Sun¹,

Zhang

Gan

et al. 2019

Advances in Civil Engineering

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Muzhailing Highway Extra-long Tunnel in Lanzhou, Gansu Province, China, belongs to the soft rock tunnel in the extremely high geostress area. During the construction process, large deformation of the soft rock occurred frequently. Taking the no. 2 inclined shaft of Muzhailing tunnel as the research object, an NPR (negative Poisson’s ratio) constant resistance and large deformation anchor cable support system based on high prestress force, constant resistance, and releasing surrounding rock pressure was proposed. The characteristics of the surrounding rock under the steel arch support and NPR anchor cable support were compared and analyzed by using 3DEC software. A series of field tests were conducted in the no. 2 inclined shaft, and the rock strength, displacement of the surrounding rock, deep displacement of the surrounding rock, internal force of steel arch, and axial force of anchor cable were measured to study the application effect of the NPR anchor cable support system in tunnel engineering. Moreover, the 3DEC numerical simulation results were compared with the field test results. The research results show that the application of NPR constant resistance and large deformation anchor cable support system in tunnel engineering has achieved good results, and it plays a significant role in controlling the large deformation of the tunnel surrounding rock.

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Quantitative Evaluation and Classification Method of the Cataclastic Texture Rock Mass Based on the Structural Plane Network Simulation

Cited by 15 publications

References 23 publications

Experimental study on strength and failure characteristics of sandstone rock mass with complex cataclastic structure using 3D printing models

Experimental study on strength and failure characteristics of sandstone rock mass with complex cataclastic structure using 3D printing models

Determination of Integrity Index Kv in CHN-BQ Method by BP Neural Network Based on Fractal Dimension D

Application of Constant Resistance and Large Deformation Anchor Cable in Soft Rock Highway Tunnel

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