Method for determining optimal construction timing of secondary lining of deep long-distance tunnel

Abstract

This invention relates to the field of tunnel construction technology in civil and hydraulic engineering, specifically a method for determining the optimal timing for secondary lining of deep-buried long-distance tunnels. The method includes the following steps: dividing the long-distance tunnel into several segments based on geological conditions, burial depth, and lining structure type along the tunnel route; numbering each segment according to the excavation sequence; and analyzing the optimal timing for secondary lining of each segment. Deformation monitoring is performed on several cross-sections of the excavated segments to obtain displacement-time relationship curves of the excavated cross-section deformation, calculating the excavated cross-section deformation rate ω, and obtaining the time history curve of the excavated cross-section deformation rate. This invention constructs an intelligent prediction model for tunnel deformation based on a BP neural network. This model can predict the later deformation trends of excavated or unexcavated tunnel segments, providing a theoretical basis for timely early warning measures or adjustments to construction plans during tunnel construction, thus ensuring tunnel construction safety.

Description

Technical Field

[0001] This invention relates to the field of tunnel construction technology in civil and hydraulic engineering, specifically to a method for determining the optimal timing for secondary lining of deep-buried long-distance tunnels. Background Technology

[0002] In recent years, deep-buried long-distance tunnels have become increasingly common in large-scale civil engineering and water conservancy projects in my country, such as the Sichuan-Tibet Railway and the Yunnan Central Water Diversion Project currently under construction. During the construction of such tunnels, technical challenges inevitably arise, including long excavation distances, high groundwater levels, complex geological conditions, and poor self-stabilizing capacity of the surrounding rock. Therefore, composite lining is often required. This involves immediately constructing an initial lining (also called primary lining or outer lining) after the tunnel cross-section excavation is completed, using anchor bolts, wire mesh, shotcrete, and steel supports to resist some of the external loads and prevent excessive deformation of the excavated cross-section. Then, after a period of time, a secondary lining (also called secondary lining or inner lining) is constructed to work with the primary lining to bear the remaining external loads.

[0003] For this type of composite lining, determining the appropriate timing for secondary lining construction based on existing on-site monitoring data has always been a key issue in tunnel structural design and dynamic construction. If the secondary lining is constructed too early, the primary lining may not fully utilize its load-bearing capacity, forcing the secondary lining to share a significant portion of the external load with the primary lining, which could jeopardize the safe and stable operation of the secondary lining in the later stages. Conversely, if the secondary lining is constructed too late, the external load of the tunnel will be primarily borne by the primary lining alone, which is detrimental to the operational safety of the primary lining and also fails to fully utilize the load-bearing capacity of the secondary lining, resulting in a waste of secondary lining materials.

[0004] Currently, there are relevant provisions in some specifications and standards regarding the appropriate timing for secondary lining. For example, the "Construction Quality Acceptance Standard for High-Speed ​​Railway Tunnel Engineering" (TB 10753-2018) stipulates that, under normal circumstances, secondary lining of deep-buried tunnels should be constructed after the initial support has stabilized. The "Technical Specification for Highway Tunnel Construction" (JTGT 3660-2020) stipulates that secondary lining should be constructed when the peripheral displacement rate is less than 0.1-0.2 mm / d or the crown settlement rate is less than 0.07-0.15 mm / d, or when the various displacements have reached 80%-90% of the total expected displacements, or when the surface cracks of the initial support (observation) no longer continue to develop.

[0005] However, in actual use, the construction time of secondary lining specified in relevant specifications and standards is mainly based on on-site test data. It is impossible to predict and determine the timing of secondary lining construction in advance, nor can it reflect the actual load distribution of the inner and outer linings and the utilization of materials when secondary lining is constructed at different times. Summary of the Invention

[0006] The purpose of this invention is to provide a method for determining the optimal timing for secondary lining construction in deep-buried long-distance tunnels, in order to solve the problems of not being able to predict and determine the timing of secondary lining construction in advance, and not being able to reflect the actual load distribution of the inner and outer linings and the utilization of materials when secondary lining is constructed at different times.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for determining the optimal timing for secondary lining of deeply buried long-distance tunnels, comprising the following steps:

[0008] S1. Divide the tunnel into typical sections: Based on the geological conditions, burial depth and lining structure of the long-distance tunnel, divide the long-distance tunnel into several sections, number each section according to the excavation sequence, and analyze the best time for secondary lining of each section.

[0009] S2. Deformation monitoring of excavated tunnel sections: Deformation monitoring is carried out on several sections of the excavated tunnel section to obtain the displacement-time relationship curve of the excavated section deformation, calculate the excavated section deformation rate ω at any time, and obtain the time history curve of the excavated section deformation rate, i.e., the ω-t relationship curve.

[0010] S3. Numerical simulation of tunnel construction process: Establish a numerical simulation model of tunnel construction process, divide the external load of the tunnel into several load steps N and apply them to the model, calculate the excavation section deformation rate ω of each load sub-step n, and then combine the ω-t relationship curve in step S2 to obtain the real time relationship corresponding to each load sub-step n. Simulate the construction of the secondary lining when the primary lining has borne different load sub-steps n respectively. According to the deformation and stress of the lining under each working condition, select the optimal value of n and obtain the optimal construction time of the secondary lining of the current tunnel section.

[0011] S4. Deformation monitoring and prediction of the next tunnel section: A BP neural network is used to construct an intelligent prediction model for tunnel deformation. The intelligent prediction model is trained and verified using the deformation monitoring results and numerical analysis results of the previous tunnel section, as well as the early deformation monitoring results of the next tunnel section. This model is used to predict the deformation trend of the next tunnel section in the later stage and the final deformation displacement convergence value, and to obtain the complete ω-t relationship curve of the tunnel section.

[0012] S5. Optimize the best time for subsequent secondary lining: Repeat steps S3 and S4, and successively optimize the best time for secondary lining of each tunnel section in combination with the actual construction progress.

[0013] Preferably, the formula for calculating the excavation section deformation rate ω at any time in step S2 is:

[0014]

[0015] The convergence value of the deformation displacement of the excavated section is Δ. max The deformation displacement at any time t is Δt .

[0016] Preferably, the numerical simulation model of the tunnel construction process established in step S3 integrates the tunnel burial depth, initial lining thickness, bottom cushion layer, steel lining, anchor bolts, and reserved deformation factors.

[0017] Preferably, the optimal timing for the secondary lining is determined based on the relationship between the principal stress of each lining material under all working conditions and its yield strength.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] 1. This invention constructs an intelligent prediction model for tunnel deformation based on a BP neural network. This model can predict the later deformation trend of tunnel sections that are being excavated or have not yet been excavated, providing a theoretical basis for timely early warning measures or adjustments to construction plans during tunnel construction, and ensuring the safety of tunnel construction.

[0020] 2. This invention also introduces the concept of excavation section deformation rate ω, compares the ω-n relationship curve in the numerical simulation calculation with the ω-t relationship curve in reality, obtains the correspondence between each load substep of the simulation model and the real time, and improves the numerical simulation effect and practical application value.

[0021] 3. This invention also proposes a method for determining the optimal construction time of deep-buried long-distance secondary lining based on on-site monitoring, numerical simulation and intelligent calculation. It fully considers the bearing capacity of the primary lining and the secondary lining, and from the perspective of ensuring the safety of the inner and outer lining structures and improving the utilization rate of lining materials, it can clearly indicate the optimal construction time of the secondary lining. It can provide theoretical basis and practical guidance value for the structural design and dynamic construction of deep-buried long-distance tunnels. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the ω-t relationship curve for tunnel segment ① of the present invention;

[0023] Figure 2 This is a schematic diagram of the general basic simulation model of the present invention;

[0024] Figure 3 This is a schematic diagram of the ω-n relationship curve for tunnel segment ① of the present invention;

[0025] Figure 4 This is a schematic diagram of the BP neural network model structure of the present invention;

[0026] Figure 5 This is a schematic diagram illustrating the deformation trend prediction of tunnel segment ② in this invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] In this embodiment, the tunnel is approximately 5000m long, with a burial depth mainly ranging from 150m to 250m, and a horseshoe-shaped cross-section. The geological conditions along the tunnel are complex, with Class IV and V surrounding rock sections accounting for more than 80% of the total, highlighting the issue of surrounding rock stability. Therefore, initial support was immediately implemented after tunnel excavation using wire mesh shotcrete, systematic anchor bolts, and steel supports. The specific types of initial lining support are shown in Table 1.

[0029] Table 1 Specific Types of Primary Lining Support

[0030]

[0031] After the initial lining is completed, the following steps should be taken to determine the optimal time for the secondary lining.

[0032] Please see Figure 1-5 This invention provides a technical solution: a method for determining the optimal timing for secondary lining of deeply buried long-distance tunnels, comprising the following steps:

[0033] S1. Divide the tunnel into typical sections: Based on the geological conditions, burial depth, and lining structure of the long-distance, deeply buried tunnel, the distribution of Class III, IV, and V surrounding rocks along the tunnel and the burial depth of the tunnel are used to divide the tunnel into several typical sections according to the tunnel excavation direction. These sections can be numbered sequentially as ①, ②, ③, etc.

[0034] S2. Deformation monitoring of excavated tunnel sections: First, excavate tunnel section ① and set up several monitoring sections in tunnel section ①. At the top arch and left and right sidewalls of each monitoring section, multiple displacement gauges are set up to monitor the deformation of the excavated section. Since the soil layer of the initial tunnel section is often relatively shallow, the external load of the tunnel can be borne entirely by the initial lining until the deformation displacement of the tunnel section converges. Therefore, based on the on-site deformation monitoring results, a complete deformation displacement-time history curve of the excavated section can be obtained.

[0035] Introduce a parameter: the excavation cross-section deformation rate ω. If the convergence value of the deformation displacement of the excavation cross-section is Δ... max The deformation displacement at any time t after excavation is Δ t Then, at that moment, the deformation rate ω of the excavation section is:

[0036]

[0037] Based on the deformation-displacement-time relationship curve of the excavated section, the deformation rate ω of the excavated section at any time is calculated, and the time history curve of the deformation rate of the excavated section, i.e., the ω-t relationship curve, is obtained, as shown below. Figure 1 As shown, by monitoring the deformation of the excavation section in real time and calculating the deformation rate of the excavation section, the deformation state of the tunnel at different construction stages can be accurately reflected.

[0038] S3. Numerical simulation of tunnel construction process: Establish a general basic simulation model that considers the timing of inner and outer lining construction, such as... Figure 2 As shown, the model takes into account the influence of various factors such as burial depth, initial lining thickness, bottom cushion layer, steel lining, anchor bolts, and reserved deformation as much as possible in advance, and integrates these factors into a general basic model. Thus, by simply adding appropriate overburden thickness, adjusting soil parameters, and deleting or modifying relevant units on the basis of the general basic model, the simulation of most other typical tunnel sections with different burial depths, geological conditions, and lining types can be achieved.

[0039] A simulation model of tunnel section ① was established based on the general basic simulation model. The main basic mechanical parameters of the surrounding rock and each support structure are shown in Table 2.

[0040] Table 2 Basic Mechanical Parameters of Surrounding Rock and Support Structures

[0041]

[0042] The key mechanical parameters of the surrounding rock in each tunnel section are obtained by combining on-site measured data with numerical simulation orthogonal experiments and artificial neural network inversion. The calculation and simulation are carried out in the following steps according to the actual excavation and construction of the tunnel.

[0043] (1) Simulate the initial geostress field: Activate the soil element to simulate the stress state of the soil under its own weight stress, and read the initial geostress, i.e., the load file of the surrounding rock outside the tunnel. In this step, the key parameters in the numerical model need to be inverted and verified in combination with the existing field monitoring results:

[0044] ①Based on on-site monitoring data and engineering geological reports, or by analogy with relevant experience from similar projects, a suitable rock mass creep constitutive model is selected through comprehensive analysis;

[0045] ② The sensitivity analysis method was used to determine the mechanical parameters of the rock mass to be inverted and their range of values, and the orthogonal experimental design scheme was used to construct the parameter combination;

[0046] ③ Conduct numerical simulation calculations to obtain the deformation of the surrounding rock under different parameter combinations;

[0047] ④ Establish the nonlinear relationship between parameters and displacement using the surrounding rock displacement values ​​of different parameter combinations, and obtain the values ​​of the parameters to be inverted based on the measured displacement values;

[0048] ⑤ Substitute the parameters obtained from the inversion into the numerical model, calculate the error between the calculated and measured values ​​of the measuring points, and verify and evaluate the inversion results.

[0049] (2) Simulate the initial lining construction conditions: Excavate the soil, activate the relevant units such as the initial lining concrete, steel support, and system anchors, and divide the surrounding rock load into several load steps N (e.g., N=10) and apply them to the model. Calculate the excavation section deformation rate ω after each load sub-step n ends, and obtain the ω-n relationship curve, such as... Figure 3 As shown, by combining the ω-t relationship curve, the relationship between the actual time t corresponding to each load substep n can be obtained.

[0050] (3) Simulation of secondary lining construction conditions: After the nth load sub-step of the primary lining construction condition is completed, the secondary lining unit is activated, indicating that the external load of the first to nth sub-steps is borne solely by the primary lining, that is, the primary lining bears n×10% of the external load alone. The external load of the nth to 10th sub-steps is borne jointly by the primary and secondary linings, that is, the remaining (10-n)×10% of the external load is borne jointly by the primary and secondary linings. The stress and deformation of the primary and secondary linings and other main structures are analyzed under different values ​​of n from 1 to 10.

[0051] (4) Simulate water-filled operation: After the simulation of the secondary lining construction is completed, internal water pressure is applied to simulate the situation where the primary and secondary linings jointly bear the internal water pressure load after the secondary lining is constructed at different times.

[0052] (5) Determine the timing of secondary lining construction: Summarize the deformation and maximum stress of the main structures such as primary lining concrete, steel support, and inner lining concrete under the above three working conditions, as shown in Table 3.

[0053] Table 3 Summary of Calculation Results for Tunnel Deformation and Lining Stress Maximum Values

[0054]

[0055] Table 3 extracts the calculation results when n is 6-10. The summary analysis shows that the smaller n is, the earlier the secondary lining is constructed, the smaller the maximum compressive stress in the primary lining concrete and the maximum compressive stress in the steel supports, which is beneficial to the safety of the primary lining. However, the maximum compressive stress and maximum tensile stress in the secondary lining concrete are significantly larger, which is detrimental to the stable bearing capacity of the secondary lining. Conversely, the larger n is, the later the secondary lining is constructed, the larger the maximum compressive stress in the primary lining concrete and the maximum compressive stress in the steel supports, fully utilizing the bearing capacity of the primary lining. However, the maximum compressive stress and maximum tensile stress in the secondary lining concrete are significantly reduced, failing to fully utilize the bearing capacity of the secondary lining. However, for tunnel section ①, due to its relatively shallow soil burial depth at the entrance section, the primary lining concrete and steel supports did not yield even when bearing all external loads. Therefore, theoretically, to ensure the safe operation of the secondary lining, later construction is more beneficial for its long-term safe and stable operation. Therefore, based on the stress condition of the secondary lining concrete, it is recommended to construct the secondary lining when n ≥ 9. Figure 3 According to the ω-n relationship curve, the optimal time for secondary lining of tunnel section ① is after 85 days.

[0056] S4. Monitoring and prediction of excavation deformation of the next tunnel section: As in step S2, monitoring of excavation deformation of the next tunnel section, namely tunnel section ②, is carried out. At this time, the excavation deformation displacement of the tunnel section has not yet fully converged. Only the time history curve of the early deformation displacement of the excavation section is obtained. Therefore, it is necessary to predict and analyze it.

[0057] A BP neural network is used to construct an intelligent prediction model for tunnel deformation. The model structure is as follows: Figure 4 As shown in the diagram. First, a three-layer backpropagation (BP) neural network is used. The number of input and output layers is determined based on specific circumstances, while the number of hidden layers is determined empirically. The weights and thresholds of the network are initialized, and the Cuckoo Search (CS) algorithm is used to optimize the weights and thresholds of the BP neural network model. Second, based on the tunnel excavation progress, influencing factors that contain excavation information and are related to the actual tunnel deformation, such as excavation time, daily excavation distance at the tunnel face, and distance between the cross-section and the tunnel face, are selected as the input layer of the model. Then, the constructed influencing factors are substituted into the CS-BP model for trial calculation, with the cumulative displacement of the cross-section measuring points as the prediction object, serving as the output layer of the model. Finally, the intelligent prediction model is trained, tested, and validated using the deformation monitoring results and numerical analysis results of tunnel segment ①, as well as the early deformation monitoring results of tunnel segment ②.

[0058] The established intelligent prediction model for tunnel deformation is used to predict the deformation trend of tunnel segment ② in the later stages, and the convergence prediction value of deformation displacement of the excavation section of this tunnel segment is obtained, such as... Figure 5 As shown, the complete ω-t relationship curve for this tunnel segment is obtained in this way.

[0059] S5. Optimize the timing of subsequent secondary lining: Based on the on-site construction progress, repeat steps S3 and S4 to optimize the timing of secondary lining for tunnel segment ②, and start the analysis of tunnel segment ③. By analogy, the monitoring, simulation, prediction and analysis of all tunnel segments of the deep-buried long-distance tunnel can be completed as the tunnel construction progresses, and the optimal timing of secondary lining for all subsequent tunnel segments can be optimized.

[0060] This invention, based on existing construction site monitoring data, rationally and quantitatively determines the timing of secondary lining construction to improve the safety and stability of tunnel construction and operation, and to fully utilize the utilization rate of both inner and outer lining materials. By introducing the excavation section deformation rate ω parameter and combining it with on-site construction monitoring data, a tunnel deformation-time relationship curve is established. Numerical simulation technology is then used to simulate the tunnel construction process, dividing the external load into multiple load sub-steps. Combined with the deformation rate time history curve, the actual time relationship corresponding to each load sub-step is obtained. Based on this, a BP neural network is used to construct an intelligent prediction model for tunnel deformation, enabling the prediction of deformation trends in subsequent tunnel sections, thus allowing for the early determination of the optimal timing for secondary lining construction in each section. This invention not only considers the differences in geological conditions, burial depth, and lining structure type along the tunnel route, but also obtains key mechanical parameters of the surrounding rock in each tunnel section through numerical simulation orthogonal experiments and artificial neural network inversion, ensuring the accuracy and reliability of the prediction results. Furthermore, the optimal timing for secondary lining construction is determined based on the relationship between the principal stress and yield strength of the lining material under all working conditions, further ensuring the stability and safety of the tunnel structure. In summary, this invention provides a scientific basis for the construction of secondary lining of deep-buried long-distance tunnels, effectively improving construction efficiency and the overall performance of the tunnel.

[0061] This not only improves the accuracy and scientific nature of determining the timing of secondary lining construction, but also provides a strong theoretical basis and practical guidance for the design and construction of deep-buried long-distance tunnels. It helps to improve the safety and efficiency of tunnel construction, while optimizing material use and reducing costs, and has important engineering practical significance and economic benefits.

[0062] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0063] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for determining the optimal timing for secondary lining of deeply buried long-distance tunnels, characterized in that: Includes the following steps: S1. Divide the tunnel into typical sections: Based on the geological conditions, burial depth and lining structure of the long-distance tunnel, divide the long-distance tunnel into several sections, number each section according to the excavation sequence, and analyze the best time for secondary lining of each section. S2. Deformation monitoring of excavated tunnel sections: Deformation monitoring is performed on several sections of the excavated tunnel section to obtain the displacement-time relationship curve of the excavated section deformation and calculate the deformation rate of the excavated section at any time. ω The time history curve of the excavation section deformation rate is obtained, i.e. ω - t Relationship curve; S3. Numerical Simulation of Tunnel Construction Process: Establish a numerical simulation model of the tunnel construction process, dividing the external load of the tunnel into several load steps. N Apply loads to the model and calculate each load substep. n Excavation cross-section deformation rate ω Combined with step S2 ω - t Relationship curves are used to obtain the load substeps. n The corresponding real-time relationships are used to simulate the initial lining under different load sub-steps. n When constructing the secondary lining, the optimal lining should be selected based on its deformation and stress under various working conditions. n The value is used to determine the optimal time for secondary lining construction in the current tunnel section; S4. Deformation Monitoring and Prediction of the Next Tunnel Section: A BP neural network is used to construct an intelligent prediction model for tunnel deformation. The model is trained and validated using deformation monitoring and numerical analysis results from the previous tunnel section, as well as preliminary deformation monitoring results from the next tunnel section. This allows for the prediction of the later deformation trend and the final deformation displacement convergence value of the next tunnel section, thus obtaining a complete picture of the tunnel section. ω - t Relationship curve; S5. Optimize the best time for subsequent secondary lining: Repeat steps S3 and S4, and successively optimize the best time for secondary lining of each tunnel section in combination with the actual construction progress.

2. The method for determining the optimal timing for secondary lining of a deep-buried long-distance tunnel according to claim 1, characterized in that: The deformation rate of the excavation section at any time in step S2 ω The calculation formula is: The convergence value of the deformation displacement of the excavated section is Δ. max at any time t The deformation displacement is Δ t .

3. The method for determining the optimal timing for secondary lining of a deep-buried long-distance tunnel according to claim 1, characterized in that: The numerical simulation model of the tunnel construction process established in step S3 integrates tunnel burial depth, initial lining thickness, bottom cushion layer, steel lining, anchor bolts, and reserved deformation factors.

4. The method for determining the optimal timing for secondary lining of a deep-buried long-distance tunnel according to claim 1, characterized in that: The optimal timing for secondary lining is determined based on the relationship between the principal stresses of each lining material under all working conditions and its yield strength.

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