Research on Deformation Characteristics of Tunnel Box Culvert Structure under Different Construction Loads

Yang

Mathematical Problems in Engineering

et al. 2022

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.

Section: Re Ned Modeling Stepsmentioning

confidence: 99%

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

Yang

Mathematical Problems in Engineering

et al. 2022

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

“…Its concrete realization is as follows: the finite in-situ stress data is obtained by hydraulic fracturing method, and the initial in-situ stress state is obtained by inversion and regression; During the construction period, the rock samples near the tunnel face are selected for laboratory tests to obtain the compressive strength of rock masses of various sections, and the bedding dip angle and groundwater development are observed and recorded. According to the classification scheme of large deformation (as shown in Table 2), the actual large deformation on site can be divided into three grades: slight, medium and strong [15][16][17] . At this point, the one-to-one correspondence relationship between the strength of surrounding rock, the structure type of surrounding rock, the size of in-situ stress, the development of groundwater and the site large deformation grade of each section is established, and a complete learning sample is obtained.…”

Section: Convolutional Neural Network Prediction Methods For Large De...mentioning

confidence: 99%

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

Chen

Yao²

2023

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.

“…These sensors were able to detect and measure strain and stress on the culvert in order to monitor structural health via the stress–strain measurement [ 25 ]. Additionally, recent work has been focused on reducing the effects of load on box culverts under different loads of construction such as culverts near tunnels or next to train tracks [ 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Influence of Haunch Geometry and Additional Steel Reinforcement on Load Carrying Capacity of Reinforced Concrete Box Culvert

et al. 2023

The culverts are used to safely convey water under railways, highways, and overpasses. They are utilized in drainage areas or water channels and in areas where the bearing capacity of soil is low. The design and construction of this crucial infrastructure need to be improved to meet contemporary demands of reliability and affordability. Precast reinforced box culverts are popular alternatives as they ensure strength, durability, rigidity, and economy. This research seeks to develop an effective and affordable design improvement procedure for a precast box culvert using modern numerical tools. The Finite Element Method (FEM) based approach is used in studying the effects of haunch geometry and additional steel reinforcement on the load-bearing capacity of box culverts. A conventional box culvert is analyzed to create the numerical models in the Abaqus FEM code and to investigate the load-bearing capacity of culverts with an expanded span. The outcomes of the study reveal the critical places for stress concentration as well as the location of maximum damage. It is found that haunch geometry and additional reinforcement at these critical places significantly affect the load-carrying capacity of a culvert. From the comparison of capacity curves of models with and without haunches and diagonal reinforcement, it is found that a 25% increase in load-carrying capacity is achievable with the recommended changes. The proposed design improvement technique can be employed for the cost-effective and safe design of a concrete box culvert with larger span lengths and high water-flowing capacities. The findings of this study are expected to assist practitioners in strength enhancement tasks of box culverts for increased structural stability and drainage efficiency.