A method for optimizing multi-layer support parameters of soft rock tunnel

Abstract

The application discloses a soft rock tunnel multilayer support parameter optimization method, comprising the following steps: S1, obtaining soft rock tunnel basic information; S2, according to the soft rock tunnel basic information, combining engineering regional geological environment and topography and geomorphology, preliminarily selecting construction and support parameters; S3, according to the preliminarily selected construction and support parameters, obtaining support effect information; S4, using a Latin hypercube sampling method, sampling and combining the construction and support parameters, and extracting the support effect corresponding to the combined working condition; S5, using a quadratic polynomial fitting, performing quadratic polynomial regression analysis on the support effect; S6, using a variance analysis method, analyzing the support effect information, and obtaining the contribution rate of each construction and support parameter to the support effect; and S7, using a genetic algorithm to obtain the optimal construction and support parameter combination. According to the actual support effect of construction, the application can select ideal and economic construction and support parameters, and realizes dynamic feedback adjustment of the construction and support parameters.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of tunnel excavation support structure, and particularly relates to a soft rock tunnel multi-layer support parameter optimization method. BACKGROUND

[0002] Soft rock has been a focus in engineering construction for a long time due to its low strength, strong rheology, crushing, easy weathering and other complex characteristics. In the construction of underground caverns in soft rock strata of this type, large vault subsidence and horizontal convergence and other cavern deformations are extremely easy to occur, and even large volume collapse occurs, causing damage to the initial support of the cavern, which is specifically manifested in the deformation characteristics of the initial support steel frame distortion, the surface of the sprayed concrete falling off and the like. Such phenomena seriously threaten the safety of construction equipment and personnel, and therefore large deformation has always been a problem that cannot be ignored in soft rock tunnel construction.

[0003] The starting point of the conventional solution is to improve the self-bearing capacity of the surrounding rock, to shorten the process operation time from the construction technology and construction timing, to reduce the time and space effect of the surrounding rock deformation and to take corresponding measures in other aspects to solve the large deformation problem of such soft rock. However, the actual support effect information is not utilized, which makes it difficult to select the ideal economic construction and support parameters according to the actual engineering conditions. SUMMARY

[0004] The purpose of the present application is to solve the problem of the difficulty in selecting multi-layer support parameters in the prior art and to make up for the lack of support measure optimization technology by providing a soft rock tunnel multi-layer support parameter optimization method.

[0005] To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0006] A soft rock tunnel multi-layer support parameter optimization method, comprising the following steps:

[0007] S1, obtaining soft rock tunnel basic information;

[0008] S2, preliminarily selecting construction and support parameters according to the soft rock tunnel basic information in combination with the engineering regional geological environment and topography and geomorphology;

[0009] S3, obtaining support effect information according to the preliminarily selected construction and support parameters;

[0010] S4, using Latin hypercube sampling method to sample and combine the construction and support parameters, and extracting the support effect corresponding to the combined working condition;

[0011] S5, using quadratic polynomial fitting to perform quadratic polynomial regression analysis on the support effect, and obtaining the functional relationship between the support effect and the corresponding parameter combination;

[0012] S6, using variance analysis method, the supporting effect information is analyzed, and the contribution rate of each construction and supporting parameter to the supporting effect is obtained;

[0013] S7, the optimal construction and supporting parameter combination is calculated by using genetic algorithm.

[0014] Further, the soft rock tunnel basic information in step S1 includes: regional topographic map; topographic map of the project location; regional geological structure plan and longitudinal and transverse section; field geological survey report.

[0015] Further, the construction parameters in step S2 include: excavation footage, bench excavation length, bench excavation height and reserved core soil size;

[0016] The supporting parameters include: multi-layer supporting thickness ratio, multi-layer supporting timing and multi-layer supporting closing timing.

[0017] Further, the supporting effect information in step S3 includes: vault displacement, haunch displacement, supporting structure stress and surrounding rock pressure.

[0018] Further, in step S5, quadratic polynomial fitting is used to perform quadratic polynomial regression analysis on the supporting effect, and the function relationship between the supporting effect and the corresponding parameter combination is:

[0019]

[0020] Wherein, is the supporting effect; i is the construction and supporting parameter number; n is the total number of construction and supporting parameters; a i , b i are the multiple regression coefficients of different construction and supporting parameters; σ i is the construction and supporting parameter; c, d are constant term undetermined coefficients.

[0021] Further, step S7 specifically includes the following steps:

[0022] S7.1, based on the function relationship between the supporting effect and the corresponding parameter combination, the optimization target of the function is determined to obtain the mathematical optimization model between the supporting effect and the corresponding parameter combination;

[0023] S7.2, setting the constraint condition of the mathematical optimization model;

[0024] S7.3, judging the individual fitness according to the genetic algorithm, and outputting the optimal solution to obtain the optimal construction and supporting parameters.

[0025] Further, the mathematical optimization model and its constraint condition are:

[0026] minf1(X)=S

[0027]

[0028] Wherein, f1(X) represents an optimization objective function; f2(X) represents a constraint condition function; S represents a tunnel vault settlement; X represents construction and supporting parameters; X1, X2 and X3 represent step excavation time, supporting time of the second layer supporting structure and closing time of the first layer supporting structure respectively; R h1 , R h2 Respectively represent the maximum compressive stress of the first and second layer supporting structures.

[0029] The soft rock tunnel multi-layer supporting parameter optimization method provided by the application has the following beneficial effects:

[0030] In the soft rock geological area, the construction and supporting parameters are selected through preliminary analysis of geological survey information; the combined working conditions are obtained by simplifying the Latin hypercube sampling method; the corresponding function of the supporting effect and the construction and supporting parameters is obtained by using quadratic polynomial regression analysis; finally, the supporting effect is optimized by the genetic algorithm to obtain the optimal construction method and supporting parameters for a specific project, so that the finally determined construction and supporting parameters are more suitable for the specific project requirements and more economical and reasonable; meanwhile, the application can also select more ideal and economical construction and supporting parameters according to the actual supporting effect of the construction, so as to realize dynamic feedback adjustment of the construction and supporting parameters. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 It is a flow chart of the soft rock tunnel multi-layer supporting parameter optimization method.

[0032] Figure 2 It is a specific optimization scheme step chart of the soft rock tunnel multi-layer supporting parameter optimization method. DETAILED DESCRIPTION

[0033] The specific embodiments of the application are described below to facilitate the understanding of the application by those skilled in the art, but it should be clear that the application is not limited to the scope of the specific embodiments, and for those skilled in the art, any changes within the spirit and scope of the application defined and determined by the appended claims are obvious, and all the application and creation utilizing the concept of the application are included in the protection.

[0034] Example 1, refer to Figure 1The soft rock tunnel multi-layer support parameter optimization method of the scheme studies the cross-influence of different construction and support parameters through Latin hypercube sampling, obtains the relationship between the support effect and the parameters by using quadratic polynomial fitting, measures the sensitivity of the construction and support parameters to the support effect by using variance analysis, and finally optimizes the multi-layer support control technology of the argillaceous soft rock tunnel by using a genetic algorithm to obtain the optimal parameter combination, which specifically includes:

[0035] Step S1, obtaining basic information of the soft rock tunnel;

[0036] The basic information includes regional topographic map, topographic map of the project site, regional geological structure plan and longitudinal and transverse section maps, field geological exploration report and related documents.

[0037] The scope contained in the regional geological map and the topographic map of the project site can provide a reliable basis for the selection of construction and support parameters. The topographic map of the project site generally has a smaller scale to facilitate the overall macro planning of the project. The geological map provides the distribution, trend and tendency of various rock layers and faults, the geometric position of the project, typical stratum structure, and especially the distribution of discontinuous surfaces such as faults and joints. The engineering geological exploration documents should include the description of the distribution of strata and faults, the degree of fault activity and special adverse geology that may pose a threat to the project. In addition, the physical and mechanical parameters of the rock mass must be provided as the basis for quantitative analysis.

[0038] Step S2, preliminarily selecting construction and support parameters according to the basic information of the soft rock tunnel in combination with the engineering regional geological environment and topography and geomorphology;

[0039] The construction parameters can be excavation footage, bench excavation length, bench excavation height, and reserved core soil size, etc. The support parameters can be multi-layer support thickness ratio, multi-layer support timing, and multi-layer support closure timing, etc.

[0040] The selection of construction and support parameters is the basis for the original data of Latin hypercube sampling in step S4. In order to improve the reliability of the results of Latin hypercube sampling, reasonable original data is essential. The parameters need to meet the following principles:

[0041] (1) The selected construction and support parameters should be typical and representative to avoid redundant and tedious basic data.

[0042] (2) The selected construction and support parameters should be comprehensive and related to the relevant data files such as geological maps and topographic maps, and have engineering pertinence and implementability.

[0043] (3) The parameters should be recorded and annotated in time for reference during analysis.

[0044] Step S3, obtaining support effect information according to the preliminary selected construction and support parameters;

[0045] The support effect information should be easy to obtain and typical and representative, such as vault displacement, haunch displacement, support structure stress, surrounding rock pressure, etc. The selected support effect information should be obtained by means of field measurement or numerical simulation, and can objectively reflect the support effect.

[0046] Step S4, using Latin hypercube sampling method to sample and combine the construction and support parameters, and extracting the support effect corresponding to the combined working condition;

[0047] The construction and support parameters are sampled and combined by the matlab Latin hypercube sampling (LHS) code. By using the LHS sampling method with memory, it is ensured that the number of different parameters appearing is the same, and the collocation is balanced. From the complex and numerous parameters, reasonable and appropriate sampling working condition results are obtained. While ensuring the accuracy of the fitting results, the complexity of different parameter arrangement combinations is reasonably simplified.

[0048] Further, the support effect information corresponding to the sampling working condition can be extracted and functionally fitted, and the relationship between the support effect and the parameters is analyzed, as in step S5.

[0049] Step S5, using quadratic polynomial fitting to perform quadratic polynomial regression analysis on the support effect, to obtain the functional relationship between the support effect and the corresponding parameter combination;

[0050] This step uses quadratic polynomial regression analysis to obtain the support effect function, and calculates the correlation coefficient R 2 . The regression effect is tested by calculating the correlation coefficient R 2 , the significance test of the regression model, and the significance test of the regression coefficient.

[0051] Step S6, using analysis of variance method to analyze the support effect information, to obtain the contribution rate of each construction and support parameter to the support effect;

[0052] By using the analysis of variance (ANOVA) method, the influence of each construction and support parameter on the support effect is studied. The support effect information such as tunnel hole displacement and support stress is analyzed by using SPSS software, and the influence significance of each parameter on the support effect is obtained.

[0053] Step S7, using genetic algorithm to calculate the optimal construction and support parameter combination, which specifically includes:

[0054] Performing quadratic polynomial regression analysis on the support effect to obtain the corresponding support effect function;

[0055] Determine the optimization goal of the function, such as taking the tunnel vault displacement as the optimization goal, requiring the tunnel vault displacement to be as small as possible.

[0056] Set the constraint conditions of the function, such as the stress of each layer of supporting structure, construction process, etc.

[0057] According to the genetic algorithm, the individual fitness is judged to determine whether it meets the optimization criteria, and finally the optimal solution is output to obtain the optimal construction and supporting parameters.

[0058] Example 2, reference Figure 2 This embodiment is based on the method in Example 1, and specifically includes the following steps:

[0059] Step S1, basic data collection;

[0060] The basic data mainly includes: regional topographic map, topographic map of the project site; regional geological structure plan and longitudinal and transverse section, field geological survey report and related documents.

[0061] Step S2, preliminary selection of construction and supporting parameters;

[0062] Among them, the construction parameters can be excavation footage, bench excavation length, bench excavation height, reserved core soil size, etc.; the supporting parameters can be multi-layer supporting thickness ratio, multi-layer supporting timing, multi-layer supporting closing timing, etc.

[0063] Step S3, supporting effect information acquisition;

[0064] The supporting effect information should be easy to obtain and have typicality and representativeness, such as vault displacement, waist displacement, supporting structure stress, surrounding rock pressure, etc. The selected supporting effect information should be obtained through field measurement or numerical simulation, etc., and can objectively reflect the supporting effect.

[0065] Step S4, Latin hypercube sampling;

[0066] Through the matlab Latin hypercube sampling (LHS), the construction and supporting parameters are sampled and combined, and through the LHS sampling method with memory, it is ensured that the number of different parameters appearing is the same, and the collocation is balanced. From the complex and numerous parameters, reasonable and appropriate sampling working condition results are obtained. While ensuring the accuracy of the fitting results, the complexity of different parameter arrangement combinations is reasonably simplified.

[0067] Step S5, quadratic polynomial regression analysis;

[0068] According to the principle of multiple regression method, the supporting effect is taken as the dependent variable, and the construction and supporting parameters σ iAs independent variables, the regression equation is in the form of:

[0069]

[0070] wherein, is the supporting effect; i is the construction and supporting parameter number; n is the total number of construction and supporting parameters; a i , b i are the multiple regression coefficients of different construction and supporting parameters; σ i is the construction and supporting parameter; c, d are constant terms to be determined.

[0071] This step can test the regression effect by calculating the correlation coefficient R 2 , the significance test of linear regression model, and the significance test of regression coefficient. The calculation of the correlation coefficient R 2 , the significance test of linear regression model, and the significance test of regression coefficient are all prior art, so they will not be described in detail in this step.

[0072] Step S6, analysis of variance;

[0073] Through the analysis of variance (ANOVA) method, the influence of each construction and supporting parameter on the supporting effect is studied. The information of the supporting effect such as the displacement of the tunnel hole, the stress of the supporting structure, etc. is analyzed by using the SPSS software, and the significance of the influence of each parameter on the supporting effect is obtained.

[0074] Step S7, genetic algorithm to find the optimal construction and supporting parameters, which specifically includes the following:

[0075] Based on step S5, a quadratic polynomial regression analysis is performed on the supporting effect to obtain the corresponding supporting effect function;

[0076] The optimization objective of the function is determined, for example, taking the displacement of the tunnel vault as the optimization objective, and requiring the displacement of the tunnel vault to be as small as possible;

[0077] The constraint conditions of the function are set, for example, taking the stress of each layer of supporting structure and the construction process as the constraint conditions;

[0078] According to the individual fitness judgment of the genetic algorithm matlab, it is judged whether it meets the optimization criteria, and finally the optimal solution is output to obtain the optimal construction and supporting parameters.

[0079] The mathematical optimization model is:

[0080] min f1(X)=S (2)

[0081]

[0082] Wherein, f1(X) represents the optimization objective function; f2(X) represents the constraint condition function; S represents the tunnel vault settlement; X represents the construction and support parameters; X1, X2, X3 respectively represent the bench excavation time, the support time of the second layer support structure, and the closing time of the first layer support structure; R h1 , R h2 respectively represent the maximum compressive stress of the first and second layer support structures.

[0083] Although the specific embodiments of the invention have been described in detail with reference to the accompanying drawings, it should not be understood as limiting the scope of protection of the patent. Various modifications and variations that can be made by those skilled in the art within the scope described in the claims are still within the scope of protection of the patent.

Claims

1. A method for optimizing multi-layer support parameters in soft rock tunnels, characterized in that, Includes the following steps: S1. Obtain basic information about the soft rock tunnel, including: regional topographic map; topographic map of the project site; regional geological structure plan and longitudinal and transverse profiles; and on-site geological survey report; S2. Based on the basic information of the soft rock tunnel, and combined with the geological environment and topography of the project area, the construction and support parameters were initially selected. S3. Based on the initially selected construction and support parameters, obtain support effect information. The construction parameters include: excavation advance, bench excavation length, bench excavation height, and reserved core soil size. The support parameters include: multi-layer support thickness ratio, multi-layer support timing, and multi-layer support closure timing. The support effect information includes: arch crown displacement, arch waist displacement, support structure stress, and surrounding rock pressure. S4. Using the Latin hypercube sampling method, the construction and support parameters are sampled and combined, and the support effect corresponding to the combined working condition is extracted. S5. Using quadratic polynomial fitting, a quadratic polynomial regression analysis was performed on the support effect to obtain the functional relationship between the support effect and the corresponding parameter combination. The functional relationship between the support effect and the corresponding parameter combination is as follows: in, For support effect; i is the construction and support parameter number; n is the total number of construction and support parameters; a i b i These are the multiple regression coefficients for different construction and support parameters; These are the construction and support parameters; c and d are undetermined coefficients for constant terms. S6. Using the analysis of variance method, the support effect information is analyzed to obtain the contribution rate of each construction and support parameter to the support effect. S7. The optimal combination of construction and support parameters is calculated using a genetic algorithm. Step S7 specifically includes the following steps: S7.

1. Based on the functional relationship between the support effect and the corresponding parameter combination, determine the optimization objective of the function to obtain a mathematical optimization model between the support effect and the corresponding parameter combination. S7.2 Set the constraints for the mathematical optimization model; S7.

3. Determine the fitness of individuals based on the genetic algorithm and output the optimal solution to obtain the optimal construction and support parameters.

2. The method for optimizing multi-layer support parameters for soft rock tunnels according to claim 1, characterized in that, The mathematical optimization model and its constraints are as follows: in, f 1( X ) represents the objective function to be optimized; f 2( X ) represents the constraint function; S represents the tunnel arch settlement; X represents the construction and support parameters; X1, X2, and X3 represent the bench excavation time, the support time of the second-layer support structure, and the closure time of the first-layer support structure, respectively; R h1 R h2 These represent the maximum compressive stresses of the first and second layer support structures, respectively.

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