Method for determining construction design parameters of isolation piles between newly-built foundation pit and existing tunnel

Abstract

The invention relates to the field of building construction, in particular to a method for determining construction design parameters of an isolation pile between a newly-built foundation pit and an existing tunnel, and the method can accurately predict the horizontal displacement and other performance indexes of the tunnel through three-dimensional numerical simulation and a DE-GWO-SVM nonlinear mapping regression model. By means of the prediction capacity, the design parameters of the isolation piles can be adjusted in real time in the construction process, and therefore tunnel deformation is effectively controlled, structural damage is avoided, construction safety is ensured, and risks in the engineering implementation process are reduced. A multi-objective optimization framework based on DE-GWO-SVM-NSGA-II is introduced, so that the design parameters of the isolation piles are further optimized on the premise of meeting the safety requirements of the tunnel. The optimization target not only comprises tunnel deformation minimization, but also considers the construction cost minimization of the isolation piles. And finally, the construction parameters of the isolation piles are determined through the TOPSIS method, the safety can be guaranteed, meanwhile, the construction cost can be remarkably reduced, and the purpose of improving economic benefits is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of building construction, and in particular to a method for determining construction design parameters of isolation piles between a newly built foundation pit and an existing tunnel. Background Art

[0002] With the acceleration of urbanization, high-rise buildings and large-scale infrastructure construction projects are increasing. As a key part of such projects, deep foundation pit engineering is of great importance. Especially in densely populated urban areas close to existing subway tunnels, new foundation pits are extremely challenging. Improper construction may cause irreversible damage to the tunnel and even induce safety accidents such as collapse. How to effectively protect subway tunnels, the arteries of urban transportation, is crucial to urban operations.

[0003] Isolation pile technology is a common practice for dealing with such working conditions. It can effectively block the impact of foundation pit excavation on the tunnel and ensure construction efficiency and economy. However, in current engineering practice, the determination of the construction parameters of isolation piles often relies on empirical judgment or overly conservative design. This may lead to inaccurate parameter selection, increase engineering safety risks and economic costs. Especially in complex geological conditions and dense urban environments, traditional design methods are difficult to cope with the complex interactions and uncertainties between underground structures. Therefore, it is particularly urgent to develop a more scientific and systematic method to determine these construction parameters. Summary of the invention

[0004] In view of the above problems, an embodiment of the present invention provides a method for determining the construction design parameters of isolation piles between a newly built foundation pit and an existing tunnel.

[0005] A method for determining construction design parameters of isolation piles between a newly built foundation pit and an existing tunnel provided by an embodiment of the present invention includes:

[0006] S1. Determine and obtain geological data and engineering information parameters related to the new foundation pit and the existing tunnel, and obtain the physical and mechanical parameters of the soil within the scope of the new foundation pit and the existing tunnel site;

[0007] S2. Using the acquired parameters and combining them with the actual on-site engineering of the new foundation pit and the existing tunnel, the construction design parameters that affect the bearing mechanism of the isolation piles are clarified, and orthogonal design schemes with different isolation pile construction design parameters are set through orthogonal tests; based on three-dimensional finite difference calculations and according to the orthogonal design schemes, a numerical analytical model of the excavation of the new foundation pit under each design scheme is constructed, and simulation calculations of the entire foundation pit construction process are carried out to obtain the performance evaluation indicators of the existing tunnel under different design scheme conditions;

[0008] S3. Using the isolation pile construction design parameters as input and the existing tunnel performance evaluation index corresponding to the isolation pile construction design parameters as output, a sample database is built; using the support vector machine DE-GWO-SVM, the sample database is trained and a DE-GWO-SVM nonlinear mapping regression function is constructed;

[0009] S4. The DE-GWO-SVM nonlinear mapping regression function and the isolation pile cost function are combined to construct a DE-GWO-SVM-NSGA-II multi-objective optimization framework with the goal of minimizing the deformation and cost of the existing tunnel, so as to optimize the construction design parameters of the isolation piles between the new foundation pit and the existing tunnel.

[0010] Optional,

[0011] The geological data and engineering information parameters include: the length, width and depth of the proposed foundation pit, the radius, segment thickness, segment weight, segment elastic modulus and segment Poisson's ratio of the existing tunnel, and the vertical distance between the edge of the new foundation pit and the existing tunnel;

[0012] The physical and mechanical parameters include: soil type and thickness, elastic modulus, Poisson's ratio, bulk density, cohesion and friction angle.

[0013] Optional,

[0014] The construction design parameters of isolation piles include: the distance between the isolation piles and the existing tunnel, the buried depth of the pile top, the pile length, the pile diameter, and the pile spacing.

[0015] Optionally, the specific process of constructing the DE-GWO-SVM nonlinear mapping regression function in S3 includes:

[0016] 1) Preprocessing sample data in the sample database

[0017] In order to improve the convergence speed and accuracy of the model and eliminate the influence of sample data size, the sample data is normalized before model training and mapped to the interval [0, 1];

[0018] 2) Select kernel function

[0019] In order to improve the accuracy of the model, the radial basis kernel function is used, and the expression is:

[0020]

[0021] Where σ is the kernel width parameter, ||xx i || indicates the distance between data x and center point x i distance;

[0022] 3) Determine the optimal parameters of SVM

[0023] In order to make the SVM model have higher prediction accuracy, the differential evolution algorithm and the grey wolf algorithm DE-GWO are used to optimize the hyperparameters of the SVM model and determine the best penalty parameters and kernel function parameters in combination with the preprocessed sample data.

[0024] 4) Establish DE-GWO-SVM nonlinear mapping regression function

[0025] The SVM model is trained based on the optimal penalty parameters, kernel function parameters and sample data determined by DE-GWO, and the DE-GWO-SVM nonlinear mapping regression function is established.

[0026] Optionally, in the process of establishing the DE-GWO-SVM nonlinear mapping regression function, the mean absolute error MAE, mean square error MSE, and mean absolute percentage error MAPE are used as evaluation criteria for the model. The formula is as follows:

[0027]

[0028]

[0029]

[0030] Among them, N is the number of sample data, y i,pre is the predicted value of the model, y i,act is the corresponding actual value;

[0031] The smaller the numerical MAE, MSE, and MAPE during the training process, the better the prediction fit of the nonlinear mapping regression function.

[0032] Optionally, the process of constructing the DE-GWO-SVM-NSGA-II multi-objective optimization framework in S4 specifically includes:

[0033] 1) Establish the objective function

[0034] Define the objective function f i (X), in order to meet the tunnel deformation requirements and optimize the isolation pile cost, two objective functions f1(X) and f2(X) are defined:

[0035] f i (X)=(f1(X),f2(X))

[0036] The DE-GWO-SVM nonlinear mapping regression function replaces the traditional mathematical function as the first objective function f1(X) of NSGA-Ⅱ, which represents the minimization of tunnel deformation and is expressed as:

[0037] f1(X)=min[DE-GWO-SVM(X1,X2,X3,X4,X5)]

[0038] Among them, f1(X) is the minimum value of the horizontal position of the existing tunnel, DE-GWO-SVM(X1,X2,X3,X4,X5) is the nonlinear mapping regression function, X1 is the length of the isolation pile, X2 is the pile diameter, X3 is the pile spacing, X4 is the pile top burial depth, and X5 is the distance between the pile and the tunnel;

[0039] The isolation piles after construction are regarded as walls, and the total cost of the wall materials per unit length is taken as the second objective function f2(X), which represents cost minimization:

[0040]

[0041] Among them, f2(X) represents the total cost, C con is the unit volume concrete cost of the isolation pile, C reb V is the cost of steel bars per unit mass of isolation piles, con is the concrete volume of a single isolation pile, M lr and M sr are the mass of longitudinal reinforcement and stirrups of a single isolation pile respectively;

[0042] The total volume of a single isolation pile is approximately equal to the concrete volume V con , then:

[0043]

[0044] 2) Determine the constraints of isolation pile construction design parameters

[0045] In order to prevent the optimized structure of isolation pile construction design parameters from being too different from the actual parameters of the project, the constraint range of isolation pile construction design parameters is set according to relevant construction specifications and actual projects:

[0046]

[0047] Among them, X i represents the construction design parameters of the i-th isolation pile, and are the upper and lower limits of the construction design parameters of the i-th isolation pile respectively;

[0048] Specifically, the constraint range of the objective function is as follows:

[0049]

[0050] Among them, st represents the constraint condition.

[0051] Optionally, the process of optimizing the construction design parameters of the isolation piles between the new foundation pit and the existing tunnel in S4 includes:

[0052] The NSGA-Ⅱ algorithm is used to perform multi-objective optimization on the isolation pile construction design parameters, and the Pareto optimal solution set is obtained, including:

[0053] Step 1: Determine basic parameters

[0054] Determine the basic parameters that affect the operating efficiency and solution quality of the NSGA-Ⅱ algorithm, including population size, crossover rate, mutation rate, and number of iterations;

[0055] Step 2: Population initialization

[0056] An initial population is randomly generated, wherein each individual in the initial population represents a possible isolation pile construction design parameter;

[0057] Step 3: Fitness evaluation and non-dominated sorting

[0058] The objective function value is calculated for each individual in the population, and non-dominated sorting is performed based on the objective function value to distinguish different optimization levels and obtain the Pareto frontier layers of multiple isolation pile construction design parameters. The individuals in each layer represent the optimal solution that is not dominated by other individuals among all objective functions.

[0059] Step 4: Calculate the congestion degree

[0060] For each individual in the Pareto frontier layer, the crowding degree of the individual in the objective function space is calculated to ensure the diversity of the solution set. The crowding degree d i The calculation formula is as follows:

[0061]

[0062] Among them, d i represents the crowding degree of the i-th individual, Represents the value of the i-th individual on the j-th objective function;

[0063] Step 5: Genetic Operation

[0064] Execute a crowding-based selection strategy and generate a new generation of population through crossover and mutation operations;

[0065] Step 6: Iterative Update

[0066] Repeat steps 1-5 until the number of iterations is met. When the algorithm terminates, a Pareto optimal solution set is formed. The Pareto optimal solution set contains a series of design parameter combinations that achieve the best balance on the objective function. The final isolation pile construction design parameters are determined based on the Pareto optimal solution set.

[0067] Optionally, the process of determining the final isolation pile construction design parameters based on the Pareto optimal solution set includes:

[0068] The TOPSIS method is used to select and calculate the best solution of the Pareto optimal solution set to determine the final isolation pile construction design parameters, including:

[0069] 1) Determine the ideal solution and the inferior solution

[0070] The ideal solution X+ is the solution that makes each objective function value reach the best. For the existing tunnel deformation minimization and isolation pile cost minimization objectives, it is the solution with the smallest deformation and the lowest cost. The inferior solution X- is the opposite, and it chooses the solution with the largest deformation and the highest cost.

[0071] 2) Calculate the distance between the ideal solution and the inferior solution

[0072] For each Pareto solution in the Pareto optimal solution set, calculate its Euclidean distance to the ideal solution and the inferior solution. The calculation formulas are:

[0073]

[0074]

[0075] Among them, d + (X k )The distance from the kth Pareto solution to the ideal solution. The smaller the distance, the closer the Pareto solution is to the optimal state in all objective functions; d-(X k ) The distance from the kth Pareto solution to the inferior solution. The larger the distance, the farther the Pareto solution is from the worst state in all objective functions. k represents the kth Pareto solution; f i (X k ) represents the value of the kth Pareto solution on the i-th objective function; m is the number of objective functions;

[0076] 3) Calculate relative closeness

[0077] Calculate the relative closeness CC of each Pareto solution k :

[0078]

[0079] Among them, CC k is the relative closeness of the kth Pareto solution. The closer the value is to 1, the closer the Pareto solution is to the ideal solution.

[0080] 4) Select the optimal solution

[0081] According to the relative proximity value, the solutions are sorted from large to small, and the Pareto solution with the highest relative proximity is selected as the final isolation pile construction design parameter.

[0082] Compared with the prior art, the present invention has the following beneficial effects:

[0083] (1) The present invention combines the actual site engineering practice and uses orthogonal test design and three-dimensional finite difference calculation to accurately determine the construction design parameters of the isolation piles. This method not only takes into account key parameters such as the distance between the pile and the tunnel, the pile top burial depth, the pile length, the pile diameter and the pile spacing, but also simulates the entire process of foundation pit construction through a numerical analytical model. Compared with the traditional design method that relies on empirical judgment, the present invention can significantly improve the scientificity and accuracy of parameter selection and ensure the rationality of the design.

[0084] (2) The present invention provides a safer construction parameter optimization method for the impact that may be caused to the existing tunnel during the foundation pit excavation process. Through three-dimensional numerical simulation and DE-GWO-SVM nonlinear mapping regression model, the horizontal displacement and other performance indicators of the tunnel can be accurately predicted. This prediction capability enables the isolation pile design parameters to be adjusted in real time during the construction process, thereby effectively controlling the deformation of the tunnel, avoiding structural damage, ensuring the safety of construction, and reducing the risks during the project implementation process.

[0085] (3) The present invention further optimizes the design parameters of the isolation piles by introducing a multi-objective optimization framework based on DE-GWO-SVM-NSGA-II while meeting the tunnel safety requirements. The optimization objectives include not only minimizing tunnel deformation, but also minimizing the construction cost of the isolation piles. The isolation pile construction parameters finally determined by the TOPSIS method can significantly reduce the project cost while ensuring safety, thereby achieving the purpose of improving economic benefits.

[0086] (4) The method of the present invention is particularly suitable for complex urban underground space development needs. By optimizing the construction parameters of isolation piles under various geological conditions, the present invention demonstrates its adaptability in different geological environments. Whether there are differences in parameters such as soil thickness, elastic modulus, Poisson's ratio, or in the case of different distances and depths between the foundation pit and the tunnel, the present invention can provide optimal construction design parameters to ensure the smooth implementation of the project under complex geological conditions, and can improve the level of refined management of urban infrastructure construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:

[0088] Figure 1 A flow chart of a method for determining construction design parameters of isolation piles between a newly built foundation pit and an existing tunnel provided by an embodiment of the present invention;

[0089] Figure 2A three-dimensional model diagram for determining construction design parameters of isolation piles between a new foundation pit and an existing tunnel provided by an embodiment of the present invention;

[0090] Figure 3 A schematic diagram of the locations of an isolation pile, an existing tunnel, and a newly built foundation pit provided in an embodiment of the present invention;

[0091] Figure 4 A principle block diagram of a method for determining construction design parameters of isolation piles between a newly built foundation pit and an existing tunnel provided in an embodiment of the present invention.

[0092] Among them, 1. Existing tunnel; 2. New foundation pit; 3. Isolation piles; 4. Site soil; 5. Isolation piles. DETAILED DESCRIPTION

[0093] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0094] See also Figure 1 , a method for determining construction design parameters of isolation piles between a newly built foundation pit and an existing tunnel provided by an embodiment of the present invention comprises:

[0095] S1. Determine and obtain geological data and engineering information parameters related to the new foundation pit and existing tunnel, and obtain the physical and mechanical parameters of the soil within the scope of the new foundation pit and existing tunnel site.

[0096] During the implementation, the project background of the new foundation pit adjacent to the existing tunnel was determined, and the geological conditions of the construction site were surveyed to obtain geological data and engineering information parameters related to the new foundation pit and the existing tunnel. Indoor mechanical tests of various types of soil in the new foundation pit and the existing tunnel site were carried out to obtain the physical and mechanical parameters of the soil within the scope of the new foundation pit and the existing tunnel site. Specifically,

[0097] The geological data and engineering information parameters include: the length, width and depth of the proposed foundation pit, the radius, segment thickness, segment weight, segment elastic modulus and segment Poisson's ratio of the existing tunnel, and the vertical distance between the edge of the new foundation pit and the existing tunnel;

[0098] The physical and mechanical parameters include: soil type and thickness, elastic modulus, Poisson's ratio, bulk density, cohesion and friction angle.

[0099] S2. Utilize the acquired parameters and combine them with the actual on-site engineering of the new foundation pit and the existing tunnel to clarify the construction design parameters that affect the bearing mechanism of the isolation piles, and set up orthogonal design schemes with different isolation pile construction design parameters through orthogonal experiments; based on three-dimensional finite difference calculations and according to the orthogonal design schemes, construct numerical analytical models of the excavation of the new foundation pit under each design scheme, and carry out simulation calculations of the entire foundation pit construction process to obtain the performance evaluation indicators of the existing tunnel under different design scheme conditions.

[0100] During implementation, the three-dimensional finite difference calculation program refers to the FLAC3D program, which uses the program's built-in FISH language to model isolation piles, existing tunnels, and new foundation pits and simulate the construction of each process.

[0101] The construction design parameters of isolation piles include: the distance between the isolation piles and the existing tunnel, the buried depth of the pile top, the pile length, the pile diameter, and the pile spacing.

[0102] S3. Using the isolation pile construction design parameters as input and the existing tunnel performance evaluation indicators corresponding to the isolation pile construction design parameters as output, a sample database is built; using the support vector machine DE-GWO-SVM, the sample database is trained and the DE-GWO-SVM nonlinear mapping regression function is constructed.

[0103] In the implementation, the nonlinear mapping model of the support vector machine is the support vector machine DE-GWO-SVM optimized by the differential evolution-grey wolf algorithm. 80% of the sample database is used as the training data set, and the remaining 20% ​​of the data is used as the test data set;

[0104] Specifically, the process of constructing the DE-GWO-SVM nonlinear mapping regression function includes:

[0105] 1) Preprocessing sample data in the sample database

[0106] In order to improve the convergence speed and accuracy of the model and eliminate the influence of sample data size, the sample data is normalized before model training and mapped to the interval [0, 1];

[0107] 2) Select kernel function

[0108] In order to improve the accuracy of the model, the radial basis kernel function is used, and the expression is:

[0109]

[0110] Where σ is the kernel width parameter, ||xx i || indicates the distance between data x and center point x i distance;

[0111] 3) Determine the optimal parameters of SVM

[0112] In order to make the SVM model have higher prediction accuracy, the differential evolution algorithm and the grey wolf algorithm DE-GWO are used to optimize the hyperparameters of the SVM model and determine the best penalty parameters and kernel function parameters in combination with the preprocessed sample data.

[0113] 4) Establish DE-GWO-SVM nonlinear mapping regression function

[0114] Based on the optimal penalty parameters, kernel function parameters and sample data determined by DE-GWO, the SVM model is trained to establish the DE-GWO-SVM nonlinear mapping regression function. The training sample data is used to help simulate the establishment of the mathematical model, and the test sample data is used to test the prediction effect of the model.

[0115] In the implementation, when establishing the DE-GWO-SVM nonlinear mapping regression function, the mean absolute error MAE, mean square error MSE, and mean absolute percentage error MAPE are used as the evaluation criteria of the model. The formula is as follows:

[0116]

[0117]

[0118]

[0119] Among them, N is the number of sample data, y i,pre is the predicted value of the model, y i,act is the corresponding actual value;

[0120] The smaller the numerical MAE, MSE, and MAPE during the training process, the better the prediction fit of the nonlinear mapping regression function.

[0121] S4. The DE-GWO-SVM nonlinear mapping regression function and the isolation pile cost function are combined to construct a DE-GWO-SVM-NSGA-II multi-objective optimization framework with the goal of minimizing the deformation and cost of the existing tunnel, so as to optimize the construction design parameters of the isolation piles between the new foundation pit and the existing tunnel.

[0122] During implementation, the process of constructing the DE-GWO-SVM-NSGA-II multi-objective optimization framework specifically includes:

[0123] 1) Establish the objective function

[0124] Define the objective function f i (X), in order to meet the tunnel deformation requirements and optimize the isolation pile cost, two objective functions f1(X) and f2(X) are defined:

[0125] fi (X)=(f1(X),f2(X))

[0126] The DE-GWO-SVM nonlinear mapping regression function replaces the traditional mathematical function as the first objective function f1(X) of NSGA-Ⅱ, which represents the minimization of tunnel deformation and is expressed as:

[0127] f1(X)=min[DE-GWO-SVM(X1,X2,X3,X4,X5)]

[0128] Among them, f1(X) is the minimum value of the horizontal position of the existing tunnel, DE-GWO-SVM(X1,X2,X3,X4,X5) is the nonlinear mapping regression function, X1 is the length of the isolation pile, X2 is the pile diameter, X3 is the pile spacing, X4 is the pile top burial depth, and X5 is the distance between the pile and the tunnel;

[0129] The isolation piles after construction are regarded as walls, and the total cost of the wall materials per unit length is taken as the second objective function f2(X), which represents cost minimization:

[0130]

[0131] Among them, f2(X) represents the total cost, C con is the unit volume concrete cost of the isolation pile, C reb V is the cost of steel bars per unit mass of isolation piles, con is the concrete volume of a single isolation pile, M lr and M sr are the mass of longitudinal reinforcement and stirrups of a single isolation pile respectively;

[0132] The total volume of a single isolation pile is approximately equal to the concrete volume V con , then:

[0133]

[0134] 2) Determine the constraints of isolation pile construction design parameters

[0135] In order to prevent the optimized structure of isolation pile construction design parameters from being too different from the actual parameters of the project, the constraint range of isolation pile construction design parameters is set according to relevant construction specifications and actual projects:

[0136]

[0137] Among them, X i represents the construction design parameters of the i-th isolation pile, and are the upper and lower limits of the construction design parameters of the i-th isolation pile respectively;

[0138] Specifically, the constraint range of the objective function is as follows:

[0139]

[0140] Among them, st represents the constraint condition.

[0141] During implementation, the process of optimizing the construction design parameters of the isolation piles between the new foundation pit and the existing tunnel includes:

[0142] The NSGA-Ⅱ algorithm is used to perform multi-objective optimization on the isolation pile construction design parameters, and the Pareto optimal solution set is obtained, including:

[0143] Step 1: Determine basic parameters

[0144] Determine the basic parameters that affect the operating efficiency and solution quality of the NSGA-Ⅱ algorithm, including population size, crossover rate, mutation rate, and number of iterations;

[0145] Step 2: Population initialization

[0146] The initial population is randomly generated, where each individual in the initial population represents a possible isolation pile construction design parameter X k ;

[0147] Step 3: Fitness evaluation and non-dominated sorting

[0148] The objective function value is calculated for each individual in the population, and non-dominated sorting is performed based on the objective function value to distinguish different optimization levels and obtain the Pareto frontier layers of multiple isolation pile construction design parameters. The individuals in each layer represent the optimal solution that is not dominated by other individuals among all objective functions.

[0149] Step 4: Calculate the congestion degree

[0150] For each individual in the Pareto frontier layer, the crowding degree of the individual in the objective function space is calculated to ensure the diversity of the solution set. The crowding degree d i The calculation formula is as follows:

[0151]

[0152] Among them, d i represents the crowding degree of the i-th individual, Represents the value of the i-th individual on the j-th objective function;

[0153] Step 5: Genetic Operation

[0154] Execute a crowding-based selection strategy and generate a new generation of population through crossover and mutation operations;

[0155] Step 6: Iterative Update

[0156] Repeat steps 1-5 until the number of iterations is met. When the algorithm terminates, a Pareto optimal solution set is formed. The Pareto optimal solution set contains a series of design parameter combinations that achieve the best balance on the objective function. The final isolation pile construction design parameters are determined based on the Pareto optimal solution set.

[0157] In implementation, the process of determining the final isolation pile construction design parameters based on the Pareto optimal solution set includes:

[0158] The TOPSIS method is used to select and calculate the best solution of the Pareto optimal solution set to determine the final isolation pile construction design parameters, including:

[0159] 1) Determine the ideal solution and the inferior solution

[0160] The ideal solution X+ is the solution that makes each objective function value reach the best. For the existing tunnel deformation minimization and isolation pile cost minimization objectives, it is the solution with the smallest deformation and the lowest cost. The inferior solution X- is the opposite, and it chooses the solution with the largest deformation and the highest cost.

[0161] 2) Calculate the distance between the ideal solution and the inferior solution

[0162] For each Pareto solution in the Pareto optimal solution set, calculate its Euclidean distance to the ideal solution and the inferior solution. The calculation formulas are:

[0163]

[0164] Among them, d + (X k ) The distance from the kth Pareto solution to the ideal solution. The smaller the distance, the closer the Pareto solution is to the optimal state in all objective functions; d - (X k ) The distance from the kth Pareto solution to the inferior solution. The larger the distance, the farther the Pareto solution is from the worst state in all objective functions. k represents the kth Pareto solution; f i (X k ) represents the value of the kth Pareto solution on the i-th objective function; m is the number of objective functions;

[0165] 3) Calculate relative closeness

[0166] Calculate the relative closeness CC of each Pareto solution k :

[0167]

[0168] Among them, CC kis the relative closeness of the kth Pareto solution. The closer the value is to 1, the closer the Pareto solution is to the ideal solution.

[0169] 4) Select the optimal solution

[0170] According to the relative proximity value, the solutions are sorted from large to small, and the Pareto solution with the highest relative proximity is selected as the final isolation pile construction design parameter.

[0171] Example:

[0172] (1) Through on-site investigation and research of the project, the geological profile data and engineering parameter information related to the new foundation pit and existing tunnel are determined.

[0173] (2) Obtain the physical and mechanical parameters of various soil types at the construction site through on-site sampling and indoor testing.

[0174] (3) Combined with the mechanical action mechanism of isolation piles, the pile length X1, pile diameter X2, pile spacing X3, pile top burial depth X4, and pile-tunnel distance X5 are selected as the isolation pile construction design parameters. According to the orthogonal test principle, the pile length X1, pile diameter X2, pile spacing X3, pile top burial depth X4, and pile-tunnel distance X5 are selected as influencing factors. According to relevant specifications and engineering experience, the upper and lower limits of the isolation pile design parameters are determined. The parameters are divided into 5 levels according to the established range, as shown in Table 1. The numerical simulation scheme is determined by the orthogonal table L25 (5^6) with six factors and five levels, with a total of 25 groups of numerical simulation schemes. The orthogonal test table of isolation pile construction parameters is shown in Table 2.

[0175] Table 1 Isolation pile design parameter factors and levels

[0176]

[0177]

[0178] Table 2 Orthogonal test plan

[0179]

[0180] (4) Based on the above orthogonal test scheme, a numerical simulation analysis of the excavation of the new foundation pit was carried out to obtain the lateral deformation values ​​of the existing tunnel under different test schemes. The numerical analysis model is as follows: Figure 2 As shown in the figure, the isolation pile construction parameters are used as input variables, and the existing tunnel displacement values ​​are used as output variables to construct a sample database with a total number of 25 samples.

[0181] The samples were normalized, and the 25 data in the sample database were randomly divided into two groups, 80% as training sets and 20% as test sets, that is, 20 data were used to train the model, and 5 data were used to test the prediction performance of the model. Five parameters, namely pile length, pile diameter, pile spacing, pile top burial depth, and pile-tunnel distance, were selected as input variables of the DE-GWO-SVM nonlinear prediction model, and the lateral displacement of the existing tunnel was used as the output variable. The DE-GWO algorithm was used to globally optimize the two key parameters in the SVM model, the penalty parameter C and the kernel function parameter g. In order to improve the accuracy of the model, the allowable error of the final result was assigned to 0.001. A deformation prediction model for existing tunnels based on DE-GWO-SVM was constructed. The specific process of the algorithm is as follows: Figure 4 shown.

[0182] According to the DE-GWO optimization results, the training set was used for nonlinear learning, and the test set was used for verification to establish a horizontal displacement prediction model for existing tunnels. DE-GWO-SVM can well predict the displacement changes of existing tunnels when new foundation pits are built. The evaluation index values ​​of the DE-GWO-SVM model were calculated, among which the mean absolute error MAE was 0.219, the mean square error MSE was 0.135, and the MAPE values ​​were 1.047, respectively, indicating that the model has good prediction ability and good stability.

[0183] (5) Based on the DE-GWO-SVM model, NSGA-II is used to perform multi-objective optimization on the isolation pile construction design parameters. The optimization objectives are to minimize the horizontal displacement of the existing tunnel and the economic cost of the isolation piles. The corresponding objective functions are:

[0184] f1(X)=min[DE-GWO-SVM(X1,X2,X3,X4,X5)]

[0185]

[0186] (6) According to relevant specifications and actual project conditions, the constraints of isolation pile construction parameters are as follows:

[0187]

[0188] (7) Multi-objective optimization based on NSGA-II. After obtaining the multi-objective optimization function and determining the constraint range, the NSGA-II algorithm is used to perform multi-objective optimization on the isolation pile construction parameters. In this embodiment, the population size of the NSGA-II algorithm is set to 100, the number of iterations is 200, the crossover distribution index is 20, the mutation probability is 0.1, the crossover probability is 0.9, and the mutation distribution index is 20. After determining the above algorithm parameters, a global optimization is performed according to the NSGA-II algorithm steps to obtain the Pareto optimal solution set.

[0189] (8) In order to select the best solution from multiple decision-making options, the TOPSIS method is used to calculate the solution with the highest relative closeness order in the Pareto optimal solution set as the optimal solution for the isolation pile construction parameters.

[0190] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for determining the construction design parameters of isolation piles between a newly built foundation pit and an existing tunnel, characterized in that: include: S1. Determine and obtain geological data and engineering information parameters related to the new foundation pit and the existing tunnel, and obtain the physical and mechanical parameters of the soil within the scope of the new foundation pit and the existing tunnel site; S2. Using the acquired parameters and combining them with the actual site engineering of the new foundation pit and the existing tunnel, the construction design parameters that affect the bearing mechanism of the isolation piles are clarified, and orthogonal design schemes with different isolation pile construction design parameters are set through orthogonal tests; Based on three-dimensional finite difference calculation and orthogonal design scheme, a numerical analytical model of new foundation pit excavation under each design scheme is constructed, and simulation calculation of the whole process of foundation pit construction is carried out to obtain the performance evaluation index of existing tunnels under different design scheme working conditions; S3. Using the isolation pile construction design parameters as input and the existing tunnel performance evaluation index corresponding to the isolation pile construction design parameters as output, a sample database is built; using the support vector machine DE-GWO-SVM, the sample database is trained and a DE-GWO-SVM nonlinear mapping regression function is constructed; S4. The DE-GWO-SVM nonlinear mapping regression function and the isolation pile cost function are combined to construct a DE-GWO-SVM-NSGA-II multi-objective optimization framework with the goal of minimizing the deformation and cost of the existing tunnel, so as to optimize the construction design parameters of the isolation piles between the new foundation pit and the existing tunnel.

2. The method for determining the construction design parameters of the isolation piles between the newly built foundation pit and the existing tunnel according to claim 1, characterized in that: The geological data and engineering information parameters include: the length, width and depth of the proposed foundation pit, the radius, segment thickness, segment weight, segment elastic modulus and segment Poisson's ratio of the existing tunnel, and the vertical distance between the edge of the new foundation pit and the existing tunnel; The physical and mechanical parameters include: soil type and thickness, elastic modulus, Poisson's ratio, bulk density, cohesion and friction angle.

3. The method for determining the construction design parameters of the isolation piles between the newly built foundation pit and the existing tunnel according to claim 1, characterized in that: The construction design parameters of isolation piles include: the distance between the isolation piles and the existing tunnel, the buried depth of the pile top, the pile length, the pile diameter, and the pile spacing.

4. The method for determining the construction design parameters of the isolation piles between the newly built foundation pit and the existing tunnel according to claim 1, characterized in that: The specific process of constructing the DE-GWO-SVM nonlinear mapping regression function in S3 includes: 1) Preprocessing sample data in the sample database In order to improve the convergence speed and accuracy of the model and eliminate the influence of sample data size, the sample data is normalized before model training and mapped to the interval [0, 1]; 2) Select kernel function In order to improve the accuracy of the model, the radial basis kernel function is used, and the expression is: Where σ is the kernel width parameter, ||xx i || indicates the distance between data x and center point x i distance; 3) Determine the optimal parameters of SVM In order to make the SVM model have higher prediction accuracy, the differential evolution algorithm and the grey wolf algorithm DE-GWO are used to optimize the hyperparameters of the SVM model and determine the best penalty parameters and kernel function parameters in combination with the preprocessed sample data. 4) Establish DE-GWO-SVM nonlinear mapping regression function The SVM model is trained based on the optimal penalty parameters, kernel function parameters and sample data determined by DE-GWO, and the DE-GWO-SVM nonlinear mapping regression function is established.

5. The method for determining the construction design parameters of the isolation piles between the newly built foundation pit and the existing tunnel according to claim 4, characterized in that: In the process of establishing the DE-GWO-SVM nonlinear mapping regression function, the mean absolute error MAE, mean square error MSE, and mean absolute percentage error MAPE are used as the evaluation criteria of the model. The formula is as follows: Among them, N is the number of sample data, y i,pre is the predicted value of the model, y i,act is the corresponding actual value; The smaller the numerical MAE, MSE, and MAPE during the training process, the better the prediction fit of the nonlinear mapping regression function.

6. The method for determining the construction design parameters of the isolation piles between the newly built foundation pit and the existing tunnel according to claim 1, characterized in that: The process of constructing the DE-GWO-SVM-NSGA-II multi-objective optimization framework in S4 specifically includes: 1) Establish the objective function Define the objective function f i (X), in order to meet the tunnel deformation requirements and optimize the isolation pile cost, two objective functions f1(X) and f2(X) are defined: f i (X)=(f1(X),f2(X)) The DE-GWO-SVM nonlinear mapping regression function replaces the traditional mathematical function as the first objective function f1(X) of NSGA-Ⅱ, which represents the minimization of tunnel deformation and is expressed as: f1(X)=min[DE-GWO-SVM(X1,X2,X3,X4,X5)] Among them, f1(X) is the minimum value of the horizontal position of the existing tunnel, DE-GWO-SVM(X1,X2,X3,X4,X5) is the nonlinear mapping regression function, X1 is the length of the isolation pile, X2 is the pile diameter, X3 is the pile spacing, X4 is the pile top burial depth, and X5 is the distance between the pile and the tunnel; The isolation piles after construction are regarded as walls, and the total cost of the wall materials per unit length is taken as the second objective function f2(X), which represents cost minimization: Among them, f2(X) represents the total cost, C con is the unit volume concrete cost of the isolation pile, C reb V is the cost of steel bars per unit mass of isolation piles, con is the concrete volume of a single isolation pile, M lr and M sr are the mass of longitudinal reinforcement and stirrups of a single isolation pile respectively; The total volume of a single isolation pile is approximately equal to the concrete volume V con , then: 2) Determine the constraints of isolation pile construction design parameters In order to prevent the optimized structure of isolation pile construction design parameters from being too different from the actual parameters of the project, the constraint range of isolation pile construction design parameters is set according to relevant construction specifications and actual projects: Among them, X i represents the construction design parameters of the i-th isolation pile, and are the upper and lower limits of the construction design parameters of the i-th isolation pile respectively; Specifically, the constraints of the objective function are as follows: Among them, st represents the constraint condition.

7. The method for determining the construction design parameters of the isolation piles between the newly built foundation pit and the existing tunnel according to claim 6, characterized in that: The process of optimizing the construction design parameters of the isolation piles between the new foundation pit and the existing tunnel in S4 includes: The NSGA-Ⅱ algorithm is used to perform multi-objective optimization on the isolation pile construction design parameters, and the Pareto optimal solution set is obtained, including: Step 1: Determine basic parameters Determine the basic parameters that affect the operating efficiency and solution quality of the NSGA-Ⅱ algorithm, including population size, crossover rate, mutation rate, and number of iterations; Step 2: Population initialization An initial population is randomly generated, wherein each individual in the initial population represents a possible isolation pile construction design parameter; Step 3: Fitness evaluation and non-dominated sorting The objective function value is calculated for each individual in the population, and non-dominated sorting is performed based on the objective function value to distinguish different optimization levels and obtain the Pareto frontier layers of multiple isolation pile construction design parameters. The individuals in each layer represent the optimal solution that is not dominated by other individuals among all objective functions. Step 4: Calculate the congestion degree For each individual in the Pareto frontier layer, the crowding degree of the individual in the objective function space is calculated to ensure the diversity of the solution set. The crowding degree d i The calculation formula is as follows: Among them, d i represents the crowding degree of the i-th individual, Represents the value of the i-th individual on the j-th objective function; Step 5: Genetic Operation Execute a crowding-based selection strategy and generate a new generation of population through crossover and mutation operations; Step 6: Iterative update Repeat steps 1-5 until the number of iterations is met. When the algorithm terminates, a Pareto optimal solution set is formed. The Pareto optimal solution set contains a series of design parameter combinations that achieve the best balance on the objective function. The final isolation pile construction design parameters are determined based on the Pareto optimal solution set.

8. The method for determining the construction design parameters of the isolation piles between the newly built foundation pit and the existing tunnel according to claim 7, characterized in that: The process of determining the final isolation pile construction design parameters based on the Pareto optimal solution set includes: The TOPSIS method is used to select and calculate the best solution of the Pareto optimal solution set to determine the final isolation pile construction design parameters, including: 1) Determine the ideal solution and the inferior solution The ideal solution X+ is the solution that makes each objective function value reach the best. For the existing tunnel deformation minimization and isolation pile cost minimization objectives, it is the solution with the smallest deformation and the lowest cost. The inferior solution X- is the opposite, and it chooses the solution with the largest deformation and the highest cost. 2) Calculate the distance between the ideal solution and the inferior solution For each Pareto solution in the Pareto optimal solution set, calculate its Euclidean distance to the ideal solution and the inferior solution. The calculation formulas are: Among them, d + (X k ) The distance from the kth Pareto solution to the ideal solution. The smaller the distance, the closer the Pareto solution is to the optimal state in all objective functions; d - (X k ) The distance from the kth Pareto solution to the inferior solution. The larger the distance, the farther the Pareto solution is from the worst state in all objective functions. k represents the kth Pareto solution; f i (X k ) represents the value of the kth Pareto solution on the i-th objective function; m is the number of objective functions; 3) Calculate relative closeness Calculate the relative closeness CC of each Pareto solution k : Among them, CC k is the relative closeness of the kth Pareto solution. The closer the value is to 1, the closer the Pareto solution is to the ideal solution. 4) Select the optimal solution According to the relative proximity value, the solutions are sorted from large to small, and the Pareto solution with the highest relative proximity is selected as the final isolation pile construction design parameter.

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