Roadbed filler and earthwork standard room setting optimization analysis method

Abstract

The invention relates to the field of electrical digital data processing, and provides a roadbed filler and geocell setting optimization analysis method, which comprises the following steps: S1, carrying out joint simulation analysis on three factors to obtain different settlement displacement values under the joint action of the three factors; s2, the settlement displacement value is substituted into response analysis software, and a Box-Behnken response optimization method is used for analysis; s3, analyzing the primary and secondary effect relationship of the influence of the three factors on roadbed settlement according to a settlement regression analysis result obtained through response analysis; and S4, according to the regression equation model, analyzing the response curved surface diagram and the contour map, determining the influence of three factors of experimental design on roadbed settlement, and taking a settlement minimum value as an optimization analysis target to obtain an optimal prediction scheme. According to the method, a test design method based on three levels is designed through a Box-Behnken response optimization method, and through reasonable arrangement of test points, a plurality of factors and the interaction of the factors can be evaluated under the condition that the number of test times is small.

Description

Technical Field

[0001] The invention relates to the field of electric digital data processing, in particular to an optimization analysis method for roadbed filling and geocell setting. Background Art

[0002] With the exploitation of a large number of mineral resources in my country, more and more goafs have appeared. However, due to the unstable geological conditions in the goafs and the insufficient strength of the roadbed, and with the heavy load of my country's roads, the requirements for the roadbed have also increased accordingly. Therefore, the roadbed must have sufficient strength and stability. Due to the insufficient strength of the roadbed, the goaf roads are prone to uneven settlement, which directly leads to cracking of the road surface, which greatly increases the cost of subsequent road maintenance and maintenance. When the disease is serious, it even threatens driving safety. Geocell is a new type of geotechnical reinforcement material with a three-dimensional honeycomb structure. Its reinforcement body is a composite composed of cells and soil. When the cell is filled with fillers such as soil, stone or concrete, the cell wall will exert lateral constraints on the filler, and the friction between the filler and the cell wall will also increase. The combined effect of this constraint and friction makes the cell structure layer have higher stiffness and strength, which can effectively improve the uneven settlement of the roadbed and improve the bearing capacity of the foundation. However, how to reasonably set up geocells, what height of geocells to use, how to use appropriate roadbed fillers, and other issues further affect the stability and bearing capacity of the roadbed. The existing technology does not make clear regulations on the location and height of the roadbed geocell, and a large number of calculations are prone to errors and time-consuming and labor-intensive. The use of numerical simulation methods cannot find the optimal layout method that is actually suitable for the construction site. Therefore, it is necessary to propose an optimization analysis method for the setting of roadbed fillers and geocells, and use simulation analysis software to find the roadbed settlement displacement value under the joint action of various factors. Then, using the response optimization analysis method, by setting 3 levels (-1, 0, 1) tests under each factor, finally obtain the interaction cloud map and contour map of each factor, and through optimization analysis, finally obtain the best prediction plan. This method can quickly and efficiently obtain the optimal setting plan of the roadbed filler and geocell required in the actual project. Summary of the invention

[0003] In order to solve the above-mentioned deficiencies in the prior art, the present invention provides an optimization analysis method for roadbed filling and geocell setting, wherein the roadbed structure used in the optimization analysis method includes: Bedrock 5; Base filler 4, laid on the bedrock 5; Roadbed filler 3, laid on top of the base filler 4; A roadbed 2 is placed above a roadbed filler 3; a geocell is arranged in the roadbed 2; and the roadbed filler 3 is also filled in the geocell; The roadbed 1 is placed on the roadbed 2; The optimization analysis method comprises the following steps: Step S1, performing a joint simulation analysis on three factors to obtain different settlement displacement values ​​under the joint action of the three factors; the three factors include: the arrangement position of the geocell, the height of the geocell and the ratio of the roadbed filler; Step S2, bringing the settlement displacement value into the response analysis software and using the Box-Behnken response optimization method for analysis; Step S3, analyzing the primary and secondary effect relationships of the three factors on the roadbed settlement based on the settlement regression analysis results obtained by the response analysis; Step S4, according to the regression equation model, by analyzing the response surface diagram and the contour diagram, determine the influence of the three factors of the experimental design on the roadbed settlement, take the minimum settlement as the optimization analysis target, and obtain the best prediction solution.

[0004] Among them, in step S1, The arrangement positions of the geocells include a first position arranged at the top of the roadbed, a second position arranged in the middle of the roadbed, and a third position arranged at the bottom of the roadbed; The heights of the geocells include: a first height of 100 mm, a second height of 150 mm, and a third height of 200 mm; The ratio of the roadbed filler includes: a first ratio of gravel soil to expansive soil of 1:1, a second ratio of gravel soil to expansive soil of 1:2, and a third ratio of gravel soil to expansive soil of 2:1; Finite element software is used to set the three factors and obtain the settlement displacement values ​​under the combined adjustment of different factors.

[0005] Among them, when conducting a 3-factor level (-1, 0, 1) experiment, set the third ratio to 0, the first ratio to -1, and the second ratio to 1; Set the first position to 0, the third position to -1, and the second position to 1; Set the second height to 0, the first height to -1, and the third height to 1.

[0006] Among them, in step S2, the analysis using the Box-Behnken response optimization method includes a regression equation based on the Box-Behnken design; the regression equation is expressed as follows: ; Where Y is the settlement displacement value; β0, β i , β ii , β ij represents the model regression coefficient; X i , Xj represents the input factor variable; ε represents the observation error; i represents the i-th factor variable, j represents the j-th factor variable; there are k factor variables in total.

[0007] Among them, in step S3, the sedimentation regression analysis results include obtaining the F value by obtaining the sum of squares of deviations, which is used for the significance test of the sedimentation model corresponding to different factors: Get the sum of squared deviations, including: Get the total sum of squared deviations: ; Among them, y i represents the actual observed value of the i-th factor variable; k is the total number of factor variables; is the average of all observed values ​​of the factor variables,

[0008] Get the regression sum of squares: ; represents the predicted value of the i-th factor variable; Get the residual sum of squares: ; Obtaining the F value includes: ; n is the total number of samples.

[0009] Among them, in step S3, the sedimentation regression analysis results also include obtaining the model regression coefficient: .

[0010] Among them, in step S4, analyzing the response surface diagram and the contour diagram includes: according to the response surface diagram and the contour diagram of the geocell position and height to the roadbed settlement, it is obtained that when the geocell position is 0 and the geocell height is between 140 mm and 160 mm, the minimum roadbed settlement can be obtained, which is between 610 mm and 615 mm.

[0011] Among them, in step S4, analyzing the response surface diagram and the contour diagram includes: according to the response surface diagram and the contour diagram of the geocell position and the filler ratio to the roadbed settlement, it is obtained that: when the geocell position is 0 and the geocell filler ratio is 0, the minimum roadbed settlement can be obtained, which is within 615mm.

[0012] Among them, in step S4, analyzing the response surface diagram and contour diagram includes: according to the response surface diagram and contour diagram of the geocell height and the filler ratio to the roadbed settlement, it is obtained that: when the geocell height is 150mm and the geocell filler ratio is 0, the roadbed settlement is the smallest, which is between 610mm-620mm.

[0013] Among them, in step S4, the minimum settlement is used as the optimization analysis target to obtain the best prediction solution, including: the geocell is located on the upper part of the roadbed; the height of the geocell is 150 mm, and the roadbed settlement is minimized when the ratio of gravel soil to expansive soil used in the geocell is 2:1.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This application uses the Box-Behnken response optimization method to analyze the primary and secondary effects of three factors, namely, the geocell layout position, the geocell height, and the ratio of the subgrade filler, on the subgrade settlement through an optimization analysis method for subgrade filler and geocell setting. The Box-Behnken response optimization method design is a three-level experimental design method that can evaluate multiple factors and their interactions with a small number of tests by reasonably arranging the test points.

[0015] In-depth analysis of the contour map shows that when the geocell is arranged on the upper part of the roadbed with a height of 150 mm and the filler ratio is 2:1 of gravel soil and expansive soil, the roadbed settlement displacement value reaches a relative minimum value, significantly improving the stability of the roadbed. This law provides direct data support and visualization basis for optimizing the roadbed structure design and selecting the best construction parameters, and is one of the core technical paths for realizing the present invention to reduce the risk of roadbed settlement and increase the service life of the road.

[0016] Compared with similar charts in the prior art, the contour map presented in this application can not only simultaneously display the comprehensive influence of multiple key factors (geocell layout position, height and roadbed filler ratio) on roadbed settlement and displacement, but also through precise scale markings, clear contour line divisions and intuitive legends, it enables technicians to obtain key information more quickly and accurately, thereby more efficiently adjusting construction plans, optimizing design parameters, etc., greatly improving the overall efficiency of road engineering design and construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a cross-sectional view of the roadbed geocell arrangement in the present invention; Figure 2 It is a settlement cloud diagram simulated by the roadbed geocell in the present invention; Figure 3-1 is a surface diagram showing the response of the geocell position and height to the roadbed settlement in the present invention; Figure 3-2 It is a contour map of the geocell position and height versus roadbed settlement in the present invention; Figure 4-1 is a response surface diagram of the geocell position and filler ratio to the roadbed settlement in the present invention; Figure 4-2 It is a contour map of the geocell position and filler ratio versus roadbed settlement in the present invention; Figure 5-1 is a response surface diagram of geocell height and filler ratio to roadbed settlement in the present invention; Figure 5-2 This is a contour map of the geocell height and filler ratio versus roadbed settlement in the present invention. Figure numerals: 1—roadbed, 2—roadbed, 3—roadbed filling, 4—base filling, 5—bedrock. DETAILED DESCRIPTION

[0018] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and examples.

[0019] An optimization analysis method for roadbed filling and geocell setting, characterized in that the roadbed structure used in the optimization analysis method includes: Bedrock 5; Base filler 4, laid on the bedrock 5; Roadbed filler 3, laid on top of the base filler 4; A roadbed 2 is placed above a roadbed filler 3; a geocell is arranged in the roadbed 2; and the roadbed filler 3 is also filled in the geocell; The roadbed 1 is placed on the top of the roadbed 2.

[0020] Specifically, the roadbed geocell setting system involved in the present application includes a top-layer roadbed 1, and geocells are arranged in the roadbed 2 below it to improve the bearing capacity of the roadbed. Roadbed filler 3 is laid inside the geocell, and a layer of roadbed filler 3 is also laid below it to increase the road surface height. A layer of base filler 4 is laid at the bottom of the roadbed, and finally there is bedrock 5 at the bottom of the roadbed.

[0021] The optimization analysis method comprises the following steps: Step S1, performing a joint simulation analysis on the arrangement position of the geocell, the height of the geocell and the ratio of the roadbed filler to obtain the settlement displacement value under the joint action of the arrangement position of the geocell, the height of the geocell and the ratio of the roadbed filler; Step S2, bringing the settlement displacement value into the response analysis software, and using the Box-Behnken response optimization method for analysis, so as to design a three-factor level test; Step S3, analyzing the primary and secondary effect relationships of the three factors on the roadbed settlement based on the settlement regression analysis results obtained by the response analysis; Step S4, according to the regression equation model, by analyzing the response surface diagram and the contour diagram, determine the influence of the three factors of the experimental design on the roadbed settlement, take the minimum settlement as the optimization analysis target, and obtain the best prediction solution.

[0022] The geocell is a mesh cell structure formed by strong welding or riveting of high-strength HDPE or PP copolymer wide bands. When the geocell is unfolded, it presents a three-dimensional mesh structure. Each cell is an independent spatial unit, which is connected to each other to form an integral mesh structure. Among them, the arrangement positions of the geocell include a first position set at the top of the roadbed, a second position set in the middle of the roadbed, and a third position set at the bottom of the roadbed. Among them, the geocell at the top of the roadbed is set 15cm below the top of the roadbed. In the field of roadbed technology, the roadbed is the foundation of the pavement, which refers to the part of the roadbed within a certain depth range below the bottom of the pavement. Generally speaking, the depth of the top of the roadbed from the ground is about 0.8 meters to 1.5 meters, and the specific depth varies according to the design standards and grades of the road. The main function of the top of the roadbed is to provide stable support for the pavement structure layer, bear the vehicle load from the road surface, and evenly transfer the load to the lower roadbed soil layer. It needs to have certain strength, rigidity and stability to ensure the performance and service life of the pavement. The middle of the roadbed is determined by the laying thickness of the roadbed, and the geocell at the second position can be in the middle of the roadbed. The geocell at the bottom of the roadbed refers to the geocell laid on the filler at a height of about 15cm at the bottom of the roadbed. Among them, when using simulation analysis software, the geocell is laid in three forms: at the top of the roadbed, in the middle of the roadbed, and at the bottom of the roadbed. At the same time, the geocell modulus is uniformly 300MPa.

[0023] The heights of the geocells include: a first height of 100 mm, a second height of 150 mm, and a third height of 200 mm. Specifically, the geocells have three different heights, namely, 100 mm, 150 mm, and 200 mm.

[0024] The roadbed filler is a mixture of gravel soil and expansive soil; the ratio of the roadbed filler includes: a first ratio of gravel soil to expansive soil of 1:1, a second ratio of gravel soil to expansive soil of 1:2, and a third ratio of gravel soil to expansive soil of 2:1. Specifically, the filler selects a mixed filler combination of gravel soil and expansive soil, and the ratios are divided into three combinations of 1:1, 1:2, and 2:1. The basic parameters for establishing the numerical simulation model are shown in Table 1. The parameters in Table 1 are input into the finite element analysis software for use in finite element analysis.

[0025] Table 1 Basic parameters for model establishment

[0026]

[0027] The optimization analysis method comprises the following steps: Step S1, conduct a joint simulation analysis on the arrangement position of the geocell, the height of the geocell and the ratio of the roadbed filler to obtain the settlement displacement value under the joint action of the arrangement position of the geocell, the height of the geocell and the ratio of the roadbed filler. Specifically, L9(3 4 ) orthogonal array, where “L” represents the orthogonal array, “9” represents the number of experiments, “3” represents the number of levels of each factor, and “4” represents the maximum number of factors that can be arranged (including interactions, etc.).

[0028] Among them, when performing response analysis in a 3-level (-1, 0, 1) experiment, the 3-factor combination is as follows: Let the third ratio be 0, let the first ratio be -1, let the second ratio be 1; Set the first position to 0, the third position to -1, and the second position to 1; Set the second height to 0, the first height to -1, and the third height to 1.

[0029] Specifically, in the response analysis of the 3-level (-1, 0, 1) test, the gravel soil and expansive soil ratio of 2:1 was set to 0, the gravel soil and expansive soil ratio of 1:1 was set to -1, and the gravel soil and expansive soil ratio of 1:2 was set to 1; in the 3-level (-1, 0, 1) test, the geocell was set to 0 at the top of the roadbed, -1 at the bottom of the roadbed, and 1 at the middle of the roadbed; in the 3-level (-1, 0, 1) test, the geocell height of 150 mm was set to 0, the geocell height of 100 mm was set to -1, and the geocell height of 200 mm was set to 1, as shown in Table 2.

[0030] Table 2 Experimental design table of three-factor level response surface analysis

[0031] There are 9 groups of simulated working conditions in total. Each group is calculated 3 times and the average is taken to reduce random errors. The response surface analysis software will automatically perform 3-level (-1, 0, 1) test analysis. Finite element software is used to set 3 factors (geocell layout position, geocell height and roadbed filler ratio) to obtain the settlement displacement value under the combined conditioning of different factors. At the same time, the following are obtained: Figure 2 The settlement cloud map shows the settlement displacement value. Figure 2 Obtain the settlement displacement values ​​required for response analysis in subsequent steps.

[0032] Step S2, bringing each of the settlement displacement values ​​into the response analysis software, and using the Box-Behnken response optimization method for analysis. Specifically, after bringing each of the settlement displacement values ​​into the response analysis software, the response analysis software automatically analyzes each of the settlement displacement values. Among them, the Box-Behnken response optimization method is a three-level experimental design method, which can evaluate multiple factors and their interactions with a small number of tests by reasonably arranging the test points. This method is based on the second-order response surface model, assuming that there is a quadratic function relationship between the response variable and the factor, and fitting the response surface equation through the test data to find the optimal factor combination.

[0033] The regression equation based on the Box-Behnken design is a quadratic polynomial equation, and its general form is as follows:

[0034] In the formula, Y represents the response variable, that is, the settlement displacement value (or roadbed settlement), β0, β i , β ii , β ij represents the model regression coefficient; X i , X j represents the input factor variables, i.e., the layout position of the geocell, the height of the geocell, and the ratio of the roadbed filler; ε represents the observation error. i represents the i-th factor variable, and j represents the j-th factor variable. There are k factor variables in total.

[0035] Step S3, analyzing the primary and secondary effect relationship of the three factors on the roadbed settlement through the settlement regression analysis results obtained by the response analysis. Specifically, the regression analysis result table of the settlement model and regression coefficient is obtained through the response analysis, as shown in Table 3, and the primary and secondary effect relationship of the three factors on the roadbed settlement is analyzed, that is, by observing the P value in the regression analysis result table of the settlement model and regression coefficient, the influence of each factor on the experimental demand settlement value is viewed.

[0036] The regression analysis result table of the settlement model and regression coefficient is described as shown in Table 3. In the response surface analysis, the data table of the regression analysis results of the settlement model and regression coefficient is automatically generated, and the table in Table 3 can be obtained by inputting the settlement displacement value obtained by the finite element analysis.

[0037] Table 3 Regression analysis results of sedimentation model and regression coefficient

[0038]

[0039] Note: P < 0.01 is extremely significant, indicated by **, P < 0.05 is significant, indicated by *, P > 0.05 is not significant, indicated by ns.

[0040] Among them, the "sum of squares of deviations" is the sum of the squares of the deviations of the dependent variable (such as settlement displacement value) from its mean, reflecting the overall degree of variation in the data.

[0041] In regression analysis, the sum of squared deviations is decomposed into three parts: First, the total sum of squares (SST), meaning: the total deviation of all observations from the mean of the dependent variable, indicating the total fluctuation range of the data. The total sum of squares formula is: .

[0042] Among them, y i represents the actual observed value of the i-th factor variable. k is the total number of factor variables. In the settlement model, it is the measured settlement value under a certain set of test conditions (such as geocell height, filler ratio, etc.).

[0043] is the average of all observed values ​​of the factor variables, .

[0044] Second, the regression sum of squares (SSR) means the sum of squares of the deviations between the model prediction value and the mean of the dependent variable, which indicates the amount of variation that the model can explain. The regression sum of squares formula is: . It represents the predicted value (or fitted value) of the i-th factor variable, that is, the theoretical settlement value calculated by the regression equation.

[0045] Third, the residual sum of squares (SSE) means: the sum of squares of the deviations between the observed values ​​and the model predicted values, indicating the random errors not explained by the model. The formula for the residual sum of squares is:

[0046] The degrees of freedom in Table 3 represent the number of independent variables in the calculation of the statistics.

[0047] In the regression analysis of the sedimentation model, the mean square (MS) is the ratio of the sum of squares (Sum of Squares) to the corresponding degrees of freedom (Degrees of Freedom, df), which is used to measure the degree of variation in the data. Its core function is to eliminate the influence of sample size differences by standardizing the variation, thereby supporting model significance tests (such as F tests).

[0048] The regression equation of the sedimentation model was subjected to variance analysis and significance test. The F value was used for model significance test.

[0049] The size of the F value is an important indicator for evaluating the degree of influence of each variable on the response value. The larger the F value, the higher the contribution of the relevant model to the response. The P value is the data obtained after the significance evaluation, and the significance of the factor is obtained by comparing with 0.01 and 0.05. P < 0.01 is extremely significant, P < 0.05 is significant, and P > 0.05 is not significant.

[0050] The AB model combines factors A and B; the AC model combines factors A and C; and the BC model combines factors B and C, reflecting their joint impact on the required settlement value of the experimental results.

[0051] A 2 The model is: the three parallel factors set in A are combined with factor A itself; B 2 The model is: the three parallel factors set in B are combined with the B factor itself; C 2 The model is: the three parallel factors set in C are combined with the C factor itself to obtain the impact on the experimental sedimentation value.

[0052] The F value formula of the settlement model is as follows: , k is the total number of factor variables in the model, including main factors (A model, B model and C model), interaction terms (AB model, AC model and BC model) and quadratic terms (A 2 Model, B 2 Model and C 2 model); n is the total number of samples.

[0053] The size of the F value is an important indicator for evaluating the influence of each variable on the response value. The larger the F value, the higher the contribution of the relevant model components to the response. As can be seen from Table 3, the F value of the settlement model is 9.08, the P value of its regression model is less than 0.0001 (extremely significant), and the lack of fit term is P = 0.3010> 0.05 (not significant), indicating that the model has a good degree of fit and can predict the corresponding regression value of the regression equation.

[0054] Evaluate model fit using R 2 value (coefficient of determination).

[0055] , which represents the proportion of variation explained by the model.

[0056] Table 3 shows the model regression coefficient R 2 =0.9681, which means that about 97% (rounded) of the change in settlement displacement can be explained by the combination of geocell position, height and filler ratio. 2The smaller the corresponding P value is, the more significant the geocell height is on the settlement, and the more significant the filler ratio is on the settlement. That is, model B (geocell height) and model C (roadbed filler ratio) have significant effects on settlement.

[0057] Adj 2 Also called Adjusted R 2 , that is, a correction index that takes into account the number of independent variables to avoid overfitting of the model. Table 3 shows the adjusted R 2 =0.9572 (greater than 0.8000), indicating that 95.72% of the data can be explained by this model, indicating that the equation has high reliability.

[0058] Step S4, according to the regression equation model, by analyzing the response surface map and the contour map, determine the influence of the three factors of the experimental design on the roadbed settlement, take the minimum settlement as the optimization analysis target, and obtain the best prediction scheme. Specifically, by analyzing the response surface map and the contour map, determine the influence of the three factors of the experimental design on the roadbed settlement, and finally find the best prediction scheme, the geocell position is the upper part of the roadbed, the geocell height is 150mm, and the filler ratio is 2:1 of gravel soil and expansive soil.

[0059] In the analysis of the influence of the three factors (geocell position, geocell height and roadbed filler ratio) involved in this application on roadbed settlement, the contour map generated by finite element analysis software such as ABAQUS clearly and intuitively shows the influence of the interaction of various factors on the response value (roadbed settlement), providing a key basis for understanding and optimizing the stability of the roadbed structure.

[0060] In response analysis, the influence of each factor on the response value can be determined by observing the shape of the response surface and contour lines. The steeper the response surface, the more obvious the interaction of each factor. As can be seen from the attached figure, the position, height and filler ratio of the geocell all have an impact on the roadbed settlement. Figure 3-1 as well as Figure 3-2 The following are the response surface diagram and contour diagram of the geocell position and height to the roadbed settlement in the present invention. The cloud diagram and contour diagram both have fluctuations, indicating that the interaction between the geocell position and height has an impact on the roadbed settlement. Specifically, from Figure 3-1 as well as Figure 3-2 It can be seen that when the geocell position is 0 and the geocell height is between 140mm and 160mm, the roadbed settlement is the smallest, between 610mm and 615mm.

[0061] Figure 4-1 and Figure 4-2The following are the response surface diagrams and contour diagrams of the geocell position and filler ratio to the roadbed settlement in the present invention. When the geocell is arranged at positions -1, 0, and 1, the roadbed settlement first decreases and then increases with the change of the ratio, indicating that the interaction between the geocell position and the filler ratio is relatively obvious. Specifically, from Figure 4-1 as well as Figure 4-2 It can be seen that when the geocell position is 0 and the geocell filler ratio is 0, the roadbed settlement is the smallest, within 615mm.

[0062] Figure 5-1 and Figure 5-2 The following are the response surface diagrams and contour diagrams of the geocell height and filler ratio to the roadbed settlement in the present invention. When the geocell height is 100mm, 150mm, and 200mm, the roadbed settlement shows a trend of first slowly decreasing and then rapidly increasing as the filler ratio changes, which also shows that the interaction between the geocell height and the filler ratio is relatively obvious. Specifically, from Figure 5-1 as well as Figure 5-2 It can be seen that when the geocell height is 150mm and the geocell filler ratio is 0, the roadbed settlement is the smallest, between 610mm-620mm.

[0063] Therefore, the geocell position is 0, and its corresponding position is the upper part of the roadbed; the geocell height is 150mm, the geocell filler ratio is 0, and the corresponding ratio is gravel soil and expansive soil ratio of 2:1, the roadbed settlement is the smallest, and finally the best prediction scheme is obtained with the geocell position at the upper part of the roadbed, the geocell height is 150mm, and the filler ratio is gravel soil and expansive soil ratio of 2:1. Later technical engineering personnel can carry out actual construction according to this scheme.

[0064] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0065] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0068] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. An optimization analysis method for roadbed filling and geocell setting, characterized in that: The roadbed structure used in the optimization analysis method includes: Bedrock (5); A base filler (4) is laid on top of the bedrock (5); A roadbed filler (3) is laid on top of the base filler (4); A roadbed (2) is placed above the roadbed filler (3); a geocell is arranged in the roadbed (2); and the roadbed filler (3) is also filled in the geocell; A roadbed (1) is placed on top of a roadbed (2); The optimization analysis method comprises the following steps: Step S1, performing a joint simulation analysis on three factors to obtain different settlement displacement values ​​under the joint action of the three factors; the three factors include: the arrangement position of the geocell, the height of the geocell and the ratio of the roadbed filler; Step S2, bringing the settlement displacement value into the response analysis software and using the Box-Behnken response optimization method for analysis; Step S3, analyzing the primary and secondary effect relationships of the three factors on the roadbed settlement based on the settlement regression analysis results obtained by the response analysis; Step S4, according to the regression equation model, by analyzing the response surface diagram and the contour diagram, determine the influence of the three factors of the experimental design on the roadbed settlement, take the minimum settlement as the optimization analysis target, and obtain the best prediction solution.

2. The optimization analysis method according to claim 1, characterized in that: In step S1, The arrangement positions of the geocells include a first position arranged at the top of the roadbed, a second position arranged in the middle of the roadbed, and a third position arranged at the bottom of the roadbed; The heights of the geocells include: a first height of 100 mm, a second height of 150 mm, and a third height of 200 mm; The ratio of the roadbed filler includes: a first ratio of gravel soil to expansive soil of 1:1, a second ratio of gravel soil to expansive soil of 1:2, and a third ratio of gravel soil to expansive soil of 2:1; Finite element software is used to set the three factors and obtain the settlement displacement values ​​under the combined adjustment of different factors.

3. The optimization analysis method according to claim 2, characterized in that: When conducting a 3-factor level (-1, 0, 1) experiment, set the third ratio to 0, the first ratio to -1, and the second ratio to 1; Set the first position to 0, the third position to -1, and the second position to 1; Set the second height to 0, set the first height to -1, and set the third height to 1.

4. The optimization analysis method according to claim 1, characterized in that: In step S2, the Box-Behnken response optimization method is used for analysis, including a regression equation based on Box-Behnken design; the regression equation is expressed as follows: ； Where Y is the settlement displacement value; β0, β i , β ii , β ij represents the model regression coefficient; X i , X j represents the input factor variable; ε represents the observation error; i represents the i-th factor variable, j represents the j-th factor variable; there are k factor variables in total.

5. The optimization analysis method according to claim 1, characterized in that: In step S3, the sedimentation regression analysis results include obtaining the F value by obtaining the sum of squared deviations, which is used for significance testing of the sedimentation model corresponding to different factors: The obtaining of the sum of squared deviations comprises: Get the total sum of squared deviations: ; Among them, y i represents the actual observed value of the i-th factor variable; k is the total number of factor variables; is the average of all observed values ​​of the factor variables, ; Get the regression sum of squares: ; represents the predicted value of the i-th factor variable; And get the residual sum of squares: ； Obtaining the F value includes: ; n is the total number of samples.

6. The optimization analysis method according to claim 5, characterized in that: In step S3, the sedimentation regression analysis results also include obtaining the model regression coefficient: 。 7. The optimization analysis method according to claim 1, characterized in that: In step S4, analyzing the response surface diagram and the contour diagram includes: according to the response surface diagram and the contour diagram of the geocell position and height to the roadbed settlement, it is obtained that when the geocell position is 0 and the geocell height is between 140 mm and 160 mm, the minimum roadbed settlement can be obtained, which is between 610 mm and 615 mm.

8. The optimization analysis method according to claim 1, characterized in that: In step S4, analyzing the response surface diagram and contour diagram includes: according to the response surface diagram and contour diagram of the geocell position and the filler ratio to the roadbed settlement, it is obtained that: when the geocell position is 0 and the geocell filler ratio is 0, the minimum roadbed settlement can be obtained, which is within 615 mm.

9. The optimization analysis method according to claim 1, characterized in that: In step S4, analyzing the response surface diagram and contour diagram includes: according to the response surface diagram and contour diagram of the geocell height and the filler ratio to the roadbed settlement, it is obtained that when the geocell height is 150mm and the geocell filler ratio is 0, the roadbed settlement is the smallest, which is between 610mm and 620mm.

10. The optimization analysis method according to claim 1, characterized in that: In step S4, the minimum settlement is taken as the optimization analysis target to obtain the best prediction scheme, including: the geocell is located on the upper part of the roadbed; the height of the geocell is 150 mm, and the roadbed settlement is minimized when the ratio of gravel soil to expansive soil used in the geocell is 2:1.

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