Quantitative Risk Analysis of Tunnel Instability in Layered Rock Mass Considering the Spatial Variability of Elastic Modulus

Chen, Dongfang; Liu, Mingzhao; Li, Shaojun; Xu, Di; Yan, Zhou; Su, Guoshao; Li, Chaoxiang

doi:10.1088/1755-1315/861/4/042009

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Mohr-Coulomb soil constitutive model (MC model) is a common constitutive model in soil simulation. As an ideal elastoplastic model, it is considered that the stress and strain conform to Hooke's law before the soil reaches shear strength [8,9]. erefore, the elastic deformation of soil is controlled by elastic modulus and Poisson's ratio.…”

Section: Introduction To the Soil Constitutive Modelmentioning

confidence: 99%

Three-Dimensional Numerical Simulation of Soil Deformation during Shield Tunnel Construction

Liu

Yang

Liu

et al. 2022

Mathematical Problems in Engineering

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In order to effectively control the ground deformation and ground subsidence during subway construction, three-dimensional numerical simulation of soil deformation during shield tunnel construction is proposed. Based on a subway tunnel project, this essay firstly divides the shield construction process into several stages and analyzes the vertical displacement of soil in each stage. FLAC 3D was used for three-dimensional finite difference numerical simulation. By comparing the numerical simulation results with the field measured data, the soil settlement caused by shield tunnel excavation is studied deeply. The simulation results show that the maximum settlement value of the monitoring data is 0.59 mm, and the maximum settlement value of the numerical simulation is 0.82 mm, with a difference of 0.23 mm. The maximum value of uplift on both sides of the tunnel is 0.41 mm in monitoring data and 0.29 in numerical simulation, with a difference of 0.12 mm. The maximum settlement value of monitoring data is 2.59 mm, and the maximum settlement value of numerical simulation is 3.05 mm, with a difference of 0.46 mm. The maximum value of uplift on both sides of the tunnel is 0.32 mm in monitoring data and 1.89 mm in numerical simulation, with a difference of 1.57 mm. The settlement value of numerical simulation is slightly larger than that of monitoring data. Conclusion. The simulation can well simulate the state of soil uplift on both sides, and the width of settlement groove is in good agreement with the monitoring data.

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Section: Introduction To the Soil Constitutive Modelmentioning

confidence: 99%

Three-Dimensional Numerical Simulation of Soil Deformation during Shield Tunnel Construction

Liu

Yang

Liu

et al. 2022

Mathematical Problems in Engineering

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“…A dam datum point data collection system and a dam deformation prediction system for collecting dam data; The datum point data collection system of dam body includes datum test point distribution module, signal receiver and cloud service platform. The reference point distribution module communicates with the cloud server platform through the signal receiver [4] ; The cloud platform is used for storing and preprocessing the data collected by the benchmark test point distribution module; The dam deformation prediction system includes a computer or other programmable equipment that executes computer usable program code, and the computer usable program code includes convolutional neural network [5] for realizing dam deformation prediction; The convolutional neural network includes a data set construction module for selecting and constructing the input data set of the convolutional neural network, a feature extraction module for extracting features, and a prediction module for predicting dam deformation; The feature extraction module comprises a first feature extraction path and a second feature extraction path, wherein the first feature extraction path comprises at least one feature extraction module, and the feature extraction module has an alternating structure of convolution layers and pool layers, and the second feature extraction path is only cascaded with one convolution layer and one pool layer;…”

Section: Features Of a Dam Deformation Prediction System Based On Con...mentioning

confidence: 99%

Deformation prediction of layered soft rock tunnel based on convolution neural network

Chen

Yao²

2023

Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022)

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In order to improve the reliability of large deformation prediction of layered soft rock tunnel under complex geological conditions, a tunnel large deformation prediction method based on convolution neural network is proposed, which solves the problems of complicated calculation of multiple evaluation indexes' weights and diverse limit values in large deformation prediction. In order to fully consider the influence of layered weak surrounding rock strength, surrounding rock structure type, in-situ stress and groundwater on large deformation of tunnel, six sub-indexes, namely, compressive strength of rock mass, bedding dip angle, initial in-situ stress state, buried depth, corrected quality index of rock mass and groundwater development, are selected to predict the large deformation grade. According to the classification standard of large deformation, a large deformation prediction model based on in-situ stress inversion and on-site large deformation monitoring information is constructed. By using the large deformation information of the tunnel, a convolution neural network large deformation prediction model which accords with the actual law of the target tunnel site is constructed. Convolutional neural network model has high accuracy in predicting sample test sets.

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Quantitative Risk Analysis of Tunnel Instability in Layered Rock Mass Considering the Spatial Variability of Elastic Modulus

Cited by 2 publications

References 6 publications

Three-Dimensional Numerical Simulation of Soil Deformation during Shield Tunnel Construction

Three-Dimensional Numerical Simulation of Soil Deformation during Shield Tunnel Construction

Deformation prediction of layered soft rock tunnel based on convolution neural network

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