Intelligent construction method for excavation support of water-rich fault tunnel

Abstract

The invention relates to an intelligent construction method for a water-rich fault tunnel excavation support, and aims to improve the safety and efficiency of tunnel excavation in a water-rich fault region through advanced numerical simulation, model test and intelligent control technologies. The method comprises the steps that geological conditions of the water-rich fault are analyzed in details, optimal excavation parameters and an optimal supporting scheme are determined through numerical simulation, and the tunnel excavation process is dynamically adjusted in combination with a real-time monitoring technology. And the support effect is evaluated and optimized by adopting fuzzy logic and a deep learning algorithm, so that intelligent management of tunnel excavation support under complex geological conditions is realized. The construction risk can be effectively reduced, the construction period is shortened, and the construction quality is improved.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel construction, in particular to tunnel excavation and support technology for water-rich fault areas, and belongs to application research of intelligent construction methods. Background Art

[0002] When excavating tunnels in water-rich fault areas, there are often many challenges such as complex geological conditions, abundant groundwater, and significant soil deformation. Traditional construction methods are difficult to ensure safety and efficiency. Existing technologies mostly rely on manual experience to formulate construction plans, lack scientific data support and real-time monitoring, resulting in high construction risks, extended construction periods, and increased costs.

[0003] Therefore, developing a water-rich fault tunnel excavation and support method based on numerical simulation and intelligent control is of great significance to improving the safety and economy of tunnel construction. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent construction method for excavation and support of water-rich fault tunnels, which can improve the safety and efficiency of tunnel construction in water-rich fault areas by comprehensively applying numerical simulation, model testing and intelligent control technology.

[0005] To achieve the above object, the present invention proposes an intelligent construction method for excavation support of a water-rich fault tunnel, characterized in that the method comprises the following steps:

[0006] S1. Conduct geological survey and data collection of water-rich faults;

[0007] S2. Use numerical simulation technology to analyze the impact of tunnel excavation on the surrounding environment;

[0008] S3. Design and optimize support scheme;

[0009] S4. Carry out excavation operations and conduct real-time monitoring and dynamic adjustments.

[0010] Preferably, the geological survey includes a comprehensive analysis of the fault hydrological characteristics, soil layer distribution and rock mechanical properties, and water flow simulation using the following mathematical model:

[0011]

[0012] Among them, Q is the water flow rate, k is the permeability coefficient, A is the flow area, h1 and h2 are the water level heights, and L is the flow path length.

[0013] Preferably, the numerical simulation technique is performed using the finite element method (FEM), stress analysis and deformation calculation, and the principal stress is calculated by the following formula:

[0014]

[0015] Among them, σ is stress, M is bending moment, and I is the section moment of inertia.

[0016] Preferably, the support scheme includes but is not limited to shotcrete, anchor support and steel support, and the best support combination is selected through an optimization algorithm to ensure the support effect.

[0017] Preferably, when carrying out excavation operations, automated monitoring equipment is used to monitor soil displacement, stress state and water level changes in real time, and the monitoring data is processed through a fuzzy logic control system.

[0018] Preferably, the decision rules of the fuzzy logic control system are based on the following mathematical model:

[0019]

[0020] Among them, F(x) is the fuzzy comprehensive evaluation result, wi is the weight coefficient, and fi(x) is the fuzzy membership function of the input variable.

[0021] Preferably, the real-time monitoring and dynamic adjustment include monitoring the support structure, analyzing the support effect using a deep learning algorithm, and achieving intelligent optimization.

[0022] Prioritizing, the deep learning model can predict the support effect by training on historical data and dynamically adjust the support parameters to adapt to the actual excavation conditions.

[0023] Preferably, the construction process adopts a full-cycle management approach, including four stages: excavation, support, monitoring and feedback, to achieve closed-loop control.

[0024] Preferably, by implementing this method, the safety risks in the excavation process of water-rich fault tunnels can be effectively reduced, and the construction efficiency and economic benefits can be improved.

[0025] The beneficial effects of the present invention based on its technical solution are:

[0026] 1. Improve the safety of tunnel excavation in water-rich faults and reduce potential risks;

[0027] 2. Improve construction efficiency, shorten construction period and reduce costs;

[0028] 3. Realize real-time monitoring and dynamic adjustment of the excavation process to ensure construction quality;

[0029] 4. Combined with intelligent algorithms, the assessment accuracy and optimization capabilities of support effects can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1This is a flow chart of an intelligent construction method for excavation and support of a water-rich fault tunnel according to the present invention;

[0031] Figure 2 This is a diagram of the survey tool in the present invention;

[0032] Figure 3 A schematic diagram of the impact of tunnel excavation on the surrounding environment analyzed by the numerical simulation technology of the present invention;

[0033] Figure 4 A diagram of a real-time monitoring and dynamic adjustment system in the present invention;

[0034] 1-survey tool; 1-1-survey tool rod; 1-2-survey tool data acquisition system; 1-3-survey tool data acquisition device; 2-data input module; 3-data modeling module; 4-numerical simulation display device; 5-numerical simulation analysis module; 6-numerical simulation suggestion module; 7-real-time monitoring and dynamic adjustment system device; 7-1-monitoring data summary module; 7-2-monitoring data processing module; 7-3-dynamic data integration module; 7-4-dynamic data sending module; 8-monitoring data feedback device; DETAILED DESCRIPTION

[0035] In order to achieve the purpose of the present invention and to better understand the technical solution and effects, the embodiments of the present invention will be further described in conjunction with the accompanying drawings.

[0036] Please refer to Figure 1 , Figure 1 This is a flow chart of an intelligent construction method for excavation and support of a water-rich fault tunnel of the present invention; the purpose of the present invention is to provide an intelligent construction method for excavation and support of a water-rich fault tunnel, which improves the safety and efficiency of tunnel construction in water-rich fault areas by comprehensively applying numerical simulation, model testing and intelligent control technology.

[0037] To achieve the above object, the present invention proposes an intelligent construction method for excavation support of a water-rich fault tunnel, characterized in that the method comprises the following steps: Figure 3 , 2-data input module; 3-data modeling module; 4-numerical simulation display device; 5-numerical simulation analysis module; 6-numerical simulation suggestion module;

[0038] S1. Conduct geological survey and data collection of water-rich faults;

[0039] S2. Use numerical simulation technology to analyze the impact of tunnel excavation on the surrounding environment;

[0040] S3. Design and optimize support scheme;

[0041] S4. Carry out excavation operations and conduct real-time monitoring and dynamic adjustments.

[0042] The geological survey includes a comprehensive analysis of the fault hydrological characteristics, soil layer distribution and rock mechanical properties, and the following mathematical models are used for water flow simulation:

[0043]

[0044] Among them, Q is the water flow rate, k is the permeability coefficient, A is the flow area, h1 and h2 are the water level heights, and L is the flow path length.

[0045] The numerical simulation technology adopts the finite element method (FEM) to perform stress analysis and deformation calculation, and the principal stress is calculated by the following formula:

[0046]

[0047] Among them, σ is stress, M is bending moment, and I is the section moment of inertia.

[0048] The support scheme includes but is not limited to shotcrete, anchor support and steel support. The best support combination is selected through an optimization algorithm to ensure the support effect.

[0049] During the excavation operation, automatic monitoring equipment is used to monitor soil displacement, stress state and water level changes in real time, and the monitoring data is processed through a fuzzy logic control system. Figure 2 , 1-surveying tool; 1-1-surveying tool rod; 1-2-surveying tool data acquisition system; 1-3-surveying tool data acquisition device;

[0050] The decision rule of the fuzzy logic control system is based on the following mathematical model:

[0051]

[0052] Among them, F(x) is the fuzzy comprehensive evaluation result, wi is the weight coefficient, and fi(x) is the fuzzy membership function of the input variable.

[0053] The real-time monitoring and dynamic adjustment include monitoring the support structure, analyzing the support effect using deep learning algorithms, and achieving intelligent optimization. Figure 4 , 7-real-time monitoring and dynamic adjustment system device; 7-1-monitoring data aggregation module; 7-2-monitoring data processing module; 7-3-dynamic data integration module; 7-4-dynamic data sending module; 8-monitoring data feedback device;

[0054] By training on historical data, the deep learning model can predict the support effect and dynamically adjust the support parameters to adapt to actual excavation conditions.

[0055] The construction process adopts a full-cycle management method, including four stages: excavation, support, monitoring and feedback, to achieve closed-loop control.

[0056] By implementing this method, the safety risks in the excavation process of water-rich fault tunnels can be effectively reduced, and the construction efficiency and economic benefits can be improved.

Claims

1. The present invention proposes an intelligent construction method for excavation and support of a water-rich fault tunnel, which is characterized by: The method comprises the following steps: S1. Conduct geological survey and data collection of water-rich faults; S2. Use numerical simulation technology to analyze the impact of tunnel excavation on the surrounding environment; S3. Design and optimize support scheme; S4. Carry out excavation operations and conduct real-time monitoring and dynamic adjustments.

2. The method according to claim 1, characterized in that The geological survey includes a comprehensive analysis of the fault hydrological characteristics, soil layer distribution and rock mechanical properties, and the following mathematical models are used for water flow simulation: Among them, Q is the water flow rate, k is the permeability coefficient, A is the flow area, h1 and h2 are the water level heights, and L is the flow path length.

3. The method according to claim 1, characterized in that The numerical simulation technology adopts the finite element method (FEM) to perform stress analysis and deformation calculation, and the principal stress is calculated by the following formula: Among them, σ is stress, M is bending moment, and I is the section moment of inertia.

4. The method according to claim 1, characterized in that: The support scheme includes but is not limited to shotcrete, anchor support and steel support. The best support combination is selected through an optimization algorithm to ensure the support effect.

5. The method according to claim 1, characterized in that When implementing excavation operations, automated monitoring equipment is used to monitor soil displacement, stress state and water level changes in real time, and the monitoring data is processed through a fuzzy logic control system.

6. The method according to claim 5, characterized in that The decision rule of the fuzzy logic control system is based on the following mathematical model: Among them, F(x) is the fuzzy comprehensive evaluation result, wi is the weight coefficient, and fi(x) is the fuzzy membership function of the input variable.

7. The method according to claim 1, characterized in that The real-time monitoring and dynamic adjustment include monitoring the support structure, analyzing the support effect using a deep learning algorithm, and achieving intelligent optimization.

8. The method according to claim 7, characterized in that By training on historical data, the deep learning model can predict the support effect and dynamically adjust the support parameters to adapt to actual excavation conditions.

9. The method according to claim 1, characterized in that: The construction process adopts a full-cycle management method, including four stages: excavation, support, monitoring and feedback, to achieve closed-loop control.

10. The method according to claim 1, characterized in that By implementing this method, the safety risks in the excavation process of water-rich fault tunnels can be effectively reduced, and the construction efficiency and economic benefits can be improved.

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