Method and device for optimizing thickness of secondary lining of tunnel

Abstract

This invention provides a method and apparatus for optimizing the thickness of secondary tunnel lining. It establishes an objective function to evaluate the safety and engineering economy of the secondary tunnel lining. Safety is characterized by a safety factor, while engineering economy is characterized by the thickness of the secondary lining. Based on a pre-established finite element model of the secondary tunnel lining and a preset thickness range, the thickness is iteratively optimized to obtain the optimal thickness value. The finite element model outputs the corresponding objective function value based on the objective function and the thickness range during each iteration. This invention can quickly and accurately determine a reasonable secondary lining thickness, providing a reference for tunnel design and construction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geotechnical engineering, and particularly to a tunnel secondary lining thickness optimization method and device. BACKGROUND

[0002] The secondary lining is a permanent supporting structure along the tunnel axis direction in the tunnel engineering, which can prevent the deformation of surrounding rock. According to the new Austrian tunneling method theory, the secondary lining can be used as a safety reserve of the tunnel. In the past design process, the following two points are insufficient. On the one hand, the current tunnel lining thickness design usually adopts the engineering analogy method, which is easy to cause the lining thickness design to be too thick or too thin. When the lining thickness design is too thick, it will cause the tunnel construction cost to be increased, the construction time to be prolonged and other adverse effects. When the lining thickness design is too thin, it may induce water seepage, damage and freezing damage and other accidents that affect the normal operation and maintenance of the tunnel. On the other hand, the current design index for evaluating the thickness of the secondary lining is relatively single, which is generally through the calculation of the lining safety factor as the design index, which ignores important indexes such as economy and construction period in the actual construction process. Therefore, the optimization design of the thickness of the secondary lining can determine the design thickness that meets the structural safety performance and the cost optimization, which has important engineering value. SUMMARY

[0003] Therefore, the purpose of the present application is to provide a tunnel secondary lining thickness optimization method and device to quickly and accurately determine a reasonable secondary lining thickness, which provides a reference for tunnel design and construction.

[0004] In the first aspect, the present application provides a tunnel secondary lining thickness optimization method, which comprises: establishing a target function for evaluating the safety and engineering economy of the tunnel secondary lining; wherein the safety is characterized based on the safety factor of the tunnel secondary lining, and the engineering economy is characterized based on the thickness of the tunnel secondary lining; based on the pre-established finite element model of the tunnel secondary lining and the pre-set thickness range, the thickness of the tunnel secondary lining is iteratively optimized to obtain the optimal thickness value of the tunnel secondary lining; wherein the finite element model outputs the corresponding target function value according to the target function and the thickness range at each iteration optimization.

[0005] Secondly, embodiments of the present invention also provide a device for optimizing the thickness of secondary tunnel lining. The device includes: a modeling module for establishing an objective function for evaluating the safety and engineering economy of secondary tunnel lining; wherein the safety is characterized based on the safety factor of the secondary tunnel lining, and the engineering economy is characterized based on the thickness of the secondary tunnel lining; and an optimization module for iteratively optimizing the thickness of the secondary tunnel lining based on a pre-established finite element model of the secondary tunnel lining and a preset thickness range to obtain the optimal thickness value of the secondary tunnel lining; wherein the finite element model outputs a corresponding objective function value based on the objective function and the thickness range during each iteration of optimization.

[0006] This invention provides a method and apparatus for optimizing the thickness of secondary tunnel lining. It establishes an objective function to evaluate the safety and engineering economy of the secondary tunnel lining. Safety is characterized by a safety factor, while engineering economy is characterized by the thickness of the secondary lining. Based on a pre-established finite element model of the secondary tunnel lining and a preset thickness range, the thickness is iteratively optimized to obtain the optimal thickness value. The finite element model outputs the corresponding objective function value based on the objective function and the thickness range during each iteration. This technique can quickly and accurately determine a reasonable secondary lining thickness, providing a reference for tunnel design and construction.

[0007] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0008] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0009] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a method for optimizing the thickness of secondary lining in a tunnel according to an embodiment of the present invention.

[0011] Figure 2This is an example diagram illustrating the iterative optimization of the secondary lining thickness of the tunnel in an embodiment of the present invention;

[0012] Figure 3 This is an example diagram of a typical cross-section finite element model in an embodiment of the present invention;

[0013] Figure 4 This is a graph showing the change in thickness values ​​during the iterative optimization process in this embodiment of the invention.

[0014] Figure 5 This is a graph showing the change history of the objective function value during the iterative optimization process in this embodiment of the invention;

[0015] Figure 6 This is a schematic diagram of a tunnel secondary lining thickness optimization device according to an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, 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.

[0017] Secondary lining is a permanent support structure cast along the tunnel axis in tunnel engineering to prevent deformation of the surrounding rock. According to the New Austrian Tunneling Method (NATM), secondary lining serves as a safety reserve for the tunnel. However, previous designs have two main shortcomings. First, current tunnel lining thickness design typically uses engineering analogy, which can easily lead to excessively thick or thin linings. Excessive thickness increases construction costs and extends construction time; insufficient thickness may induce seepage, deterioration, and frost damage, affecting normal tunnel operation and maintenance. Second, current evaluation criteria for secondary lining thickness are relatively simplistic, generally relying on the calculated safety factor, neglecting crucial factors in actual construction, such as economic efficiency and project duration. Therefore, optimizing the secondary lining thickness to determine the optimal thickness that balances structural safety and cost has significant engineering value.

[0018] Based on this, the present invention provides a method and apparatus for optimizing the thickness of secondary lining in tunnels, which can quickly and accurately determine a reasonable secondary lining thickness, providing a reference for tunnel design and construction.

[0019] To facilitate understanding of this embodiment, a method for optimizing the thickness of secondary lining in tunnels, as disclosed in this embodiment of the invention, will first be described in detail. (See [link to relevant documentation]). Figure 1As shown, the method may include the following steps:

[0020] Step S102: Establish an objective function for evaluating the safety and engineering economy of the secondary lining of the tunnel; wherein, safety is characterized based on the safety factor of the secondary lining of the tunnel, and engineering economy is characterized based on the thickness of the secondary lining of the tunnel.

[0021] Step S104: Based on the pre-established finite element model of the tunnel secondary lining and the preset thickness range, the thickness of the tunnel secondary lining is iteratively optimized to obtain the optimal thickness value of the tunnel secondary lining; wherein, the finite element model outputs the corresponding objective function value according to the objective function and the thickness range in each iteration optimization.

[0022] For example, before iteratively optimizing the thickness of the secondary lining of a tunnel, a corresponding tunnel model can be established based on the engineering geological survey report and tunnel design parameters, which can then be used as the finite element model of the secondary lining of the tunnel.

[0023] The safety factor is one of the important indicators in tunnel design. When iteratively optimizing the structural parameters of the anti-seepage wall, the safety factor required for each iteration can be calculated.

[0024] Generally, the thicker the secondary lining, the higher the tunnel construction cost; the thinner the secondary lining, the lower the tunnel safety factor. Therefore, taking the thickness of the tunnel secondary lining as the optimization target, an objective function jointly characterized by the safety factor and the cost of the tunnel secondary lining is established. This transforms the lining thickness design problem into a nonlinear optimization problem of the safety factor and cost, thus avoiding the limitations brought about by a single design index.

[0025] For example, the objective function can be established as follows:

[0026]

[0027] In the formula: P i The objective function value is optimized for the i-th iteration; The safety factor value corresponding to the i-th iteration optimization; (F s ) max M represents the maximum safety factor during the iterative optimization process. i M represents the value of the secondary lining engineering of the tunnel corresponding to the i-th iteration of the iterative optimization process; min M represents the minimum cost of the secondary lining of the tunnel during the iterative optimization process. i =αβγH, where α is the construction cost per linear meter of the secondary tunnel lining; H is the thickness of the secondary tunnel lining; β is the length of the secondary tunnel lining along the tunnel axis; γ is the centerline length of the secondary tunnel lining thickness; w F w is the weighting factor for the engineering safety factor.M This is a weighting factor for the economic efficiency of the project, and its value is selected based on the actual needs of the project to meet certain requirements. That's all.

[0028] The parameter to be optimized is the thickness H of the secondary lining, which can usually be determined empirically within a range (i.e., the thickness range mentioned above).

[0029] H1≤H≤H2

[0030] In the formula: H1 is the lower limit of the search for the secondary lining thickness; H2 is the upper limit of the search for the secondary lining thickness. The optimization method for the secondary lining thickness is to find a secondary lining thickness within this range that makes the objective function optimal. Theoretically, the optimal value of the objective function is 1. The closer the objective function is to 1, the better the secondary lining thickness of the tunnel.

[0031] This invention provides a method for optimizing the thickness of secondary tunnel lining. It establishes an objective function to evaluate the safety and engineering economy of the secondary tunnel lining. Safety is characterized by a safety factor, while engineering economy is characterized by the thickness of the secondary lining. Based on a pre-established finite element model of the secondary tunnel lining and a preset thickness range, the thickness is iteratively optimized to obtain the optimal thickness value. The finite element model outputs the corresponding objective function value based on the objective function and the thickness range during each iteration. This technique can quickly and accurately determine a reasonable secondary lining thickness, providing a reference for tunnel design and construction.

[0032] As one possible implementation, step S104 (i.e., based on the pre-established finite element model of the tunnel secondary lining and the preset thickness range, iteratively optimizing the thickness of the tunnel secondary lining to obtain the optimal thickness value of the tunnel secondary lining) may include: for each iteration optimization, the cohesion and internal friction angle of the tunnel surrounding rock are reduced multiple times through the finite element model to obtain the safety factor value required for that iteration optimization.

[0033] For example, for each iteration of optimization, the cohesion and internal friction angle of the tunnel surrounding rock can be reduced multiple times using a finite element model according to a preset reduction coefficient and a preset reduction step size, until the second preset condition is met and the reduction ends. The reduction coefficient value corresponding to the result of the last reduction before the second preset condition is met is determined as the safety factor value required for this iteration of optimization. The result of each reduction is a set of cohesion and internal friction angle obtained after the reduction. The second preset condition is met when the difference between the results of multiple consecutive reductions is not less than a preset third threshold.

[0034] For each iteration of optimization, the expression for reducing the cohesion and internal friction angle of the tunnel surrounding rock can be:

[0035]

[0036]

[0037] Where: c is the cohesion of the tunnel surrounding rock used in each reduction; c ′ The cohesion of the surrounding rock of the tunnel after each reduction; The internal friction angle of the tunnel surrounding rock used in each reduction; F represents the internal friction angle of the tunnel surrounding rock after each reduction; s This is the reduction factor used for each reduction.

[0038] The specific steps for repeatedly reducing the cohesion and internal friction angle of the tunnel surrounding rock using a finite element model are as follows: Define a field variable such that the cohesion and internal friction angle change with the field variable; this field variable is the reduction coefficient (i.e., the safety factor); define the initial field variable value, which can generally be taken as 0.5 to 1.0; determine the working condition of the tunnel secondary lining, and perform multiple reduction calculations of the finite element model under this working condition; for each subsequent reduction calculation, linearly increase the value of the field variable according to a certain step size (i.e., the preset reduction step size mentioned above) until the finite element model does not converge (i.e., the second preset condition mentioned above is met), at which point the calculation terminates. The value of the field variable used in the last reduction before the finite element model does not converge can be determined as the safety factor value required for this iteration optimization.

[0039] As one possible implementation, the result of each iteration optimization is the maximum value of the objective function output by the finite element model in that iteration optimization; the above step S104 (i.e., based on the pre-established finite element model of the tunnel secondary lining and the preset thickness range, iteratively optimize the thickness of the tunnel secondary lining to obtain the optimal thickness value of the tunnel secondary lining) may include: based on the finite element model and the thickness range, using a preset artificial fish swarm algorithm to iteratively optimize the thickness of the tunnel secondary lining until the first preset condition is met, and then the iterative optimization ends, and the thickness value corresponding to the result of the last iteration optimization is determined as the optimal thickness value; wherein, meeting the first preset condition means that the number of iteration optimizations reaches the preset maximum number of iterations or the difference between the results of multiple consecutive iteration optimizations is less than a preset first threshold.

[0040] For example, the steps described above for iteratively optimizing the thickness of the secondary lining of the tunnel using a preset artificial fish swarm algorithm based on the finite element model and thickness range can be performed as follows:

[0041] (11) Generate an initial fish swarm randomly based on the thickness range; wherein each artificial fish in the initial fish swarm is a thickness value.

[0042] For example, a thickness range [H1, H2] can be set based on experience. Then, multiple artificial fish can be randomly generated within this thickness range according to the initial fish population size. These artificial fish can then be combined into an initial fish population, denoted as H1. Where N is the size of the initial population.

[0043] (12) Generate a reverse fish swarm based on the thickness range and the initial fish swarm.

[0044] For example, continuing from the previous example, the thickness range [H1,H2] and the initial fish swarm X can be used as a basis. (0) Generate a reverse fish swarm using the following formula:

[0045]

[0046] (13) Output the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm based on the objective function, the initial fish swarm and the reverse fish swarm using the finite element model.

[0047] For example, after obtaining the initial fish swarm and the reverse fish swarm, each artificial fish in the initial fish swarm and the reverse fish swarm can be input into the finite element model. A safety factor value and the construction value of the secondary lining of the tunnel can be calculated through the finite element model. Then, the safety factor value and the construction value of the secondary lining of the tunnel can be substituted into the objective function through the finite element model to calculate the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm.

[0048] (14) Generate a final initial fish swarm based on the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm; wherein the objective function value of each artificial fish in the final initial fish swarm is greater than a preset second threshold.

[0049] For example, after obtaining the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm, the artificial fish with objective function values ​​greater than a preset second threshold can be used to form the final initial fish swarm.

[0050] (15) The thickness of the secondary lining of the tunnel is iteratively optimized based on the finite element model and the final initial fish swarm.

[0051] As one possible implementation, in the iterative optimization of the thickness of the secondary lining of the tunnel based on the finite element model and the final initial fish swarm, each iteration of optimization can perform the following steps A and B:

[0052] A. Using the finite element model, in this iteration, output the objective function value of each artificial fish in the current fish swarm based on the objective function and each artificial fish in the current fish swarm, and make each artificial fish in the current fish swarm perform foraging behavior, grouping behavior, tail chasing behavior and random behavior.

[0053] B. Record the maximum value of the objective function output by the finite element model in this iteration as the result of this iteration optimization, and generate the next generation of fish based on each artificial fish in the current fish swarm and the objective function output by the finite element model in this iteration.

[0054] As one possible implementation, the steps of iteratively optimizing the thickness of the secondary lining of the tunnel based on the finite element model and the final initial fish swarm may further include: setting the values ​​of the initial parameters of the artificial fish swarm algorithm; wherein the initial parameters include: fish swarm size, field of view, step size, crowding factor, maximum number of attempts, and maximum number of iterations.

[0055] For ease of understanding, the method for optimizing the thickness of secondary lining in tunnels is described below using a specific application as an example. See [link / reference] Figure 2 As shown, the above-mentioned method for optimizing the thickness of the secondary lining of tunnels can be carried out in the following manner:

[0056] Step 1: Determine the thickness range [H1, H2] based on experience, and obtain multiple thickness values ​​within this range by randomly selecting values.

[0057] Step 2: Treat each thickness value as an artificial fish, combine multiple thickness values ​​to form an initial fish swarm, and generate a corresponding reverse fish swarm based on the initial fish swarm through reverse learning.

[0058] Step 3: Calculate the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm using the above finite element model, and form the final initial fish swarm by artificial fish whose objective function value is greater than the preset second threshold in the initial fish swarm and the reverse fish swarm.

[0059] Step 4: Based on the final initial fish swarm, the thickness of the secondary lining of the tunnel is iteratively optimized using a finite element model.

[0060] In step 4, for the first iteration optimization, the thickness value corresponding to the final initial fish swarm (i.e., the initial thickness value of the secondary lining) is substituted into the finite element model to calculate the safety factor value and the construction value of the tunnel secondary lining corresponding to the first iteration optimization. These values ​​are then substituted into the objective function to obtain the objective function value for the first iteration optimization. The maximum value of the objective function value for the first iteration optimization is then recorded as the result of the first iteration optimization, completing the first iteration optimization. After the first iteration optimization is completed, the next generation fish swarm (i.e., the fish swarm corresponding to the next iteration optimization) is generated based on the objective function value. For each subsequent iteration optimization after the first iteration optimization, the fish swarm corresponding to that iteration optimization is used as the current fish swarm. The thickness value corresponding to the current fish swarm is substituted into the finite element model to calculate the safety factor value and the construction value of the tunnel secondary lining corresponding to that iteration optimization. These values ​​are then substituted into the objective function to obtain the objective function value for that iteration optimization. The maximum value of the objective function value for that iteration optimization is then recorded as the result of that iteration optimization. For each subsequent iteration after the first iteration, the result of the current iteration can be used to update the result of the previous iteration, and the next generation of fish can be generated based on the objective function value corresponding to the current iteration.

[0061] In step 4, for each iteration of optimization, the parameters of the finite element model can be adjusted based on the results of that iteration.

[0062] Step 5: Continue iterative optimization until the preset maximum number of iterations is reached, then end the iterative optimization and determine the thickness value corresponding to the result of the last iteration optimization at the end of the iteration optimization as the optimal thickness value.

[0063] To facilitate understanding, the above method for optimizing the thickness of secondary lining in tunnels is described exemplarily using a specific application example as follows:

[0064] A tunnel section of a tunnel project is selected as the target tunnel section. The target tunnel section is designed according to the New Austrian Tunneling Method (NATM) construction principle and engineering analogy. The initial support material is C20 shotcrete with a thickness of 250mm; the secondary lining material is C30 cast-in-place concrete with a thickness of H, which is the target to be optimized.

[0065] A two-dimensional finite element model was established in finite element software based on the structure of the target tunnel. The model adopted an elastoplastic model with the Mohr-Coulomb yield criterion. The target tunnel section has a burial depth of 40m, a model width of 80m, and net dimensions of 8.0m x 7.5m. The boundary conditions of the calculation model included: horizontal constraints on the left and right sides, a fixed constraint on the bottom boundary, and no constraint on the top boundary. The calculation model is shown below.Figure 3 As shown.

[0066] The calculation condition is under self-weight, with initial support and secondary lining carried out in stages after full-section excavation. Based on the geological survey report and design data, the parameter values ​​for each material are shown in Table 1.

[0067] Table 1. Examples of parameter values ​​for each material.

[0068]

[0069] The engineering cost α per linear meter of tunnel secondary lining can be set to 1300 / cubic meter, and the length β of the tunnel secondary lining along the tunnel axis can be set to 100m. The value of the centerline length γ of the tunnel secondary lining thickness can be set according to the actual engineering needs, and the engineering safety factor w can be used as the weighting factor. F The value is set to 0.55, which is the weighting factor for the project's economic efficiency. M The value is set to 0.45, which satisfies the setting of the lower and upper limits of the thickness search to 0.3m and 1.8m respectively. Then, the aforementioned artificial fish swarm optimization algorithm is used to iteratively optimize the secondary lining thickness H of the tunnel. The initial parameters of the artificial fish swarm algorithm are set as follows: number of fish swarms is 10, maximum number of iterations is 50, maximum number of trials is 10, field of view is 0.2, crowding factor is 0.618, and step size is 0.2.

[0070] In addition, to avoid the tunnel secondary lining thickness becoming too discrete during the iterative optimization process, resulting in a slow convergence speed, the tunnel secondary lining thickness can be normalized before the iterative optimization begins, and then reverse-normalized after the iterative optimization ends.

[0071] See Figure 4 As shown, three thickness values ​​were generated during the above iterative optimization process, denoted as optimal 1, optimal 2, and optimal 3, respectively. Optimal 3 is the optimal thickness value in this iteration. Table 2 summarizes the upper limit, lower limit, and optimal value (i.e., the optimal thickness value mentioned above) of the tunnel secondary lining thickness.

[0072] Table 2. Example table of upper limit, lower limit and optimal value for secondary lining thickness in tunnels.

[0073]

[0074] Depend on Figure 5 As can be seen, during the above iterative optimization process, the objective function value continuously increases, from the initial 0.8653 to 0.9018, indicating that as the iterative optimization proceeds, the tunnel secondary lining thickness achieves a good balance between safety and engineering economy, proving that the above tunnel secondary lining thickness optimization method is effective and feasible.

[0075] Based on the above-described method for optimizing the thickness of secondary tunnel lining, this invention also provides a device for optimizing the thickness of secondary tunnel lining. (See attached image.) Figure 6 As shown, the device may include the following modules:

[0076] Module 602 is established to establish an objective function for evaluating the safety and engineering economy of tunnel secondary lining; wherein the safety is characterized based on the safety factor of tunnel secondary lining, and the engineering economy is characterized based on the thickness of tunnel secondary lining.

[0077] The optimization module 604 is used to iteratively optimize the thickness of the secondary lining of the tunnel based on a pre-established finite element model of the secondary lining of the tunnel and a preset thickness range, so as to obtain the optimal thickness value of the secondary lining of the tunnel; wherein, the finite element model outputs the corresponding objective function value according to the objective function and the thickness range in each iteration optimization.

[0078] This invention provides a tunnel secondary lining thickness optimization device, which establishes an objective function for evaluating the safety and engineering economy of the tunnel secondary lining. Safety is characterized by a safety factor, while engineering economy is characterized by the thickness of the secondary lining. Based on a pre-established finite element model of the tunnel secondary lining and a preset thickness range, the device iteratively optimizes the thickness to obtain the optimal value. The finite element model outputs the corresponding objective function value based on the objective function and the thickness range during each iteration. This technique can quickly and accurately determine a reasonable secondary lining thickness, providing a reference for tunnel design and construction.

[0079] The result of each iteration optimization is the maximum value of the objective function output by the finite element model in that iteration optimization; the optimization module 604 can also be used to: based on the finite element model and the thickness range, use a preset artificial fish swarm algorithm to iteratively optimize the thickness of the secondary lining of the tunnel until the first preset condition is met, and then end the iteration optimization, and determine the thickness value corresponding to the result of the last iteration optimization as the optimal thickness value; wherein, meeting the first preset condition means that the number of iteration optimizations reaches the preset maximum number of iterations or the difference between the results of multiple consecutive iteration optimizations is less than the preset first threshold.

[0080] The optimization module 604 described above can also be used to: randomly generate an initial fish swarm based on the thickness range; wherein each artificial fish in the initial fish swarm represents a thickness value; generate a reverse fish swarm based on the thickness range and the initial fish swarm; output the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm using the finite element model according to the objective function, the initial fish swarm, and the reverse fish swarm; generate a final initial fish swarm based on the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm; wherein the objective function value of each artificial fish in the final initial fish swarm is greater than a preset second threshold; and iteratively optimize the thickness of the secondary lining of the tunnel based on the finite element model and the final initial fish swarm.

[0081] The optimization module 604 described above can also be used to perform the following steps A and B in each iteration: A. Using the finite element model, output the objective function value of each artificial fish in the current fish swarm based on the objective function and each artificial fish in the current fish swarm during this iteration, and make each artificial fish in the current fish swarm perform foraging behavior, grouping behavior, tail-chasing behavior, and random behavior; B. Record the maximum value of the objective function value output by the finite element model during this iteration as the result of this iteration optimization, and generate the next generation of fish swarm based on each artificial fish in the current fish swarm and the objective function value output by the finite element model during this iteration.

[0082] The optimization module 604 described above can also be used to: set the values ​​of the initial parameters of the artificial fish swarm algorithm; wherein the initial parameters include: fish swarm size, field of view, step size, crowding factor, maximum number of attempts, and maximum number of iterations.

[0083] The aforementioned optimization module 604 can also be used to: for each iteration of optimization, reduce the cohesion and internal friction angle of the tunnel surrounding rock multiple times using the finite element model to obtain the safety factor value required for that iteration of optimization.

[0084] The aforementioned optimization module 604 can also be used to: reduce the cohesion and internal friction angle of the tunnel surrounding rock multiple times using the finite element model according to a preset reduction coefficient and a preset reduction step size, until the second preset condition is met and the reduction ends, and the reduction coefficient value corresponding to the result of the last reduction before the second preset condition is met is determined as the safety factor value required for this iteration optimization; wherein, the result of each reduction is a set of cohesion and internal friction angle obtained after the reduction; the second preset condition is met when the difference between the results of multiple consecutive reductions is not less than a preset third threshold.

[0085] The tunnel secondary lining thickness optimization device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned tunnel secondary lining thickness optimization method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0086] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the thickness of secondary lining in tunnels, characterized in that, The method includes: An objective function is established to evaluate the safety and engineering economy of tunnel secondary lining; wherein the safety is characterized based on the safety factor of tunnel secondary lining, and the engineering economy is characterized based on the thickness of tunnel secondary lining. Based on a pre-established finite element model of the tunnel secondary lining and a preset thickness range, the thickness of the tunnel secondary lining is iteratively optimized to obtain the optimal thickness value of the tunnel secondary lining; wherein, the finite element model outputs the corresponding objective function value according to the objective function and the thickness range during each iteration of optimization; The result of each iteration optimization is the maximum value of the objective function output by the finite element model in that iteration optimization. The step of iteratively optimizing the thickness of the secondary tunnel lining based on the pre-established finite element model and the preset thickness range to obtain the optimal thickness value of the secondary tunnel lining includes: iteratively optimizing the thickness of the secondary tunnel lining using a preset artificial fish swarm algorithm based on the finite element model and the thickness range until a first preset condition is met, and then ending the iteration optimization, and determining the thickness value corresponding to the result of the last iteration optimization as the optimal thickness value; wherein, meeting the first preset condition means that the number of iteration optimizations reaches a preset maximum number of iterations or the difference between the results of multiple consecutive iteration optimizations is less than a preset first threshold. The steps for iteratively optimizing the thickness of the secondary lining of the tunnel using a preset artificial fish swarm algorithm based on the finite element model and the thickness range include: randomly generating an initial fish swarm based on the thickness range; wherein each artificial fish in the initial fish swarm represents a thickness value; generating a reverse fish swarm based on the thickness range and the initial fish swarm; outputting the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm using the finite element model according to the objective function, the initial fish swarm, and the reverse fish swarm; generating a final initial fish swarm based on the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm; wherein the objective function value of each artificial fish in the final initial fish swarm is greater than a preset second threshold; and iteratively optimizing the thickness of the secondary lining of the tunnel based on the finite element model and the final initial fish swarm.

2. The method according to claim 1, characterized in that, The steps for iteratively optimizing the thickness of the secondary tunnel lining based on a pre-established finite element model and a preset thickness range to obtain the optimal thickness value include: For each iteration of optimization, the cohesion and internal friction angle of the tunnel surrounding rock are reduced multiple times using the finite element model to obtain the safety factor value required for that iteration of optimization.

3. The method according to claim 2, characterized in that, The steps for obtaining the safety factor value required for this iteration of optimization by repeatedly reducing the cohesion and internal friction angle of the tunnel surrounding rock using the finite element model include: The cohesion and internal friction angle of the tunnel surrounding rock are reduced multiple times using the finite element model according to a preset reduction coefficient and a preset reduction step size until a second preset condition is met. The reduction coefficient value corresponding to the result of the last reduction before the second preset condition is met is determined as the safety factor value required for this iteration optimization. The result of each reduction is a set of cohesion and internal friction angle obtained after the reduction. The second preset condition is met when the difference between the results of multiple consecutive reductions is not less than a preset third threshold.

4. The method according to claim 1, characterized in that, The steps for iteratively optimizing the thickness of the secondary lining of the tunnel based on the finite element model and the final initial fish swarm include: Each iteration of optimization involves performing the following steps A and B: A. In this iteration, the finite element model outputs the objective function value of each artificial fish in the current fish swarm based on the objective function and each artificial fish in the current fish swarm, and causes each artificial fish in the current fish swarm to perform foraging behavior, grouping behavior, tail chasing behavior and random behavior; B. Record the maximum value of the objective function output by the finite element model in this iteration as the result of this iteration optimization, and generate the next generation of fish based on each artificial fish in the current fish swarm and the objective function output by the finite element model in this iteration.

5. The method according to claim 1, characterized in that, The steps for iteratively optimizing the thickness of the secondary lining of the tunnel based on the finite element model and the final initial fish swarm include: Set the initial parameters of the artificial fish swarm algorithm; wherein the initial parameters include: swarm size, field of view, step size, crowding factor, maximum number of attempts, and maximum number of iterations.

6. A device for optimizing the thickness of secondary lining in tunnels, characterized in that, The device includes: A module is established to create an objective function for evaluating the safety and engineering economy of tunnel secondary lining; wherein the safety is characterized based on the safety factor of the tunnel secondary lining, and the engineering economy is characterized based on the thickness of the tunnel secondary lining. An optimization module is used to iteratively optimize the thickness of the secondary tunnel lining based on a pre-established finite element model of the secondary tunnel lining and a preset thickness range, to obtain the optimal thickness value of the secondary tunnel lining; wherein, the finite element model outputs a corresponding objective function value according to the objective function and the thickness range during each iteration of optimization; The result of each iteration is the maximum value of the objective function output by the finite element model in that iteration. The optimization module is further configured to: based on the finite element model and the thickness range, use a preset artificial fish swarm algorithm to iteratively optimize the thickness of the secondary lining of the tunnel until a first preset condition is met, and then end the iteration optimization, and determine the thickness value corresponding to the result of the last iteration optimization as the optimal thickness value; wherein, meeting the first preset condition means that the number of iterations reaches a preset maximum number of iterations or the difference between the results of multiple consecutive iterations is less than a preset first threshold. The optimization module is further configured to: randomly generate an initial fish swarm based on the thickness range; wherein each artificial fish in the initial fish swarm represents a thickness value; generate a reverse fish swarm based on the thickness range and the initial fish swarm; output the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm using the finite element model according to the objective function, the initial fish swarm, and the reverse fish swarm; generate a final initial fish swarm based on the objective function value of each artificial fish in the initial fish swarm and the reverse fish swarm; wherein the objective function value of each artificial fish in the final initial fish swarm is greater than a preset second threshold; and iteratively optimize the thickness of the secondary lining of the tunnel based on the finite element model and the final initial fish swarm.

7. The apparatus according to claim 6, characterized in that, The optimization module is also used for: For each iteration of optimization, the cohesion and internal friction angle of the tunnel surrounding rock are reduced multiple times using the finite element model to obtain the safety factor value required for that iteration of optimization.

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