An optimization analysis method for the setting of subgrade filler and geocell

Abstract

The present invention relates to the field of electrical digital data processing, and provides an optimization analysis method for the setting of subgrade fillers and geocells. Step S1: perform a combined simulation analysis on three factors to obtain different settlement displacement values under the combined action of the three factors; Step S2: input the settlement displacement values into response analysis software and perform analysis using the Box-Behnken response optimization method; Step S3: analyze the primary and secondary effect relationships of the three factors on subgrade settlement through the settlement regression analysis results obtained from the response analysis; Step S4: based on the regression equation model, determine the influence of the three factors in the experimental design on subgrade settlement by analyzing the response surface diagram and the contour diagram, and take the minimum settlement as the optimization analysis target to obtain the predicted optimal solution. The present application designs a three-level experimental design method through the Box-Behnken response optimization method. By reasonably arranging the experimental points, it can evaluate multiple factors and their interactions with fewer experimental times.

Description

Technical Field

[0001] The present invention relates to the field of electrical digital data processing, and specifically to an optimization analysis method for the setting of subgrade fillers and geocells. Background Art

[0002] With the exploitation of a large number of mineral resources in China, more and more goafs have emerged one after another. However, due to the unstable geological conditions of the goafs and insufficient subgrade strength, and with the heavy load of roads in China, the requirements for the subgrade have also increased accordingly. Therefore, the subgrade bed must have sufficient strength and stability. Due to insufficient subgrade strength, the roads in goaf areas are prone to uneven settlement, and in severe cases, it will directly lead to pavement cracking, which greatly increases the subsequent road maintenance and repair costs, and even threatens driving safety when the diseases are serious. A geocell is a new type of geosynthetic reinforcement material with a three-dimensional honeycomb structure, and its reinforcement body is a composite body composed of the geocell and the soil. When the geocell is filled with fillers such as earth and stone or concrete, the geocell wall will generate lateral restraint on the filler, and at the same time, the friction between the filler and the geocell wall will also increase. The combined action of this restraint and friction makes the geocell structure layer have higher stiffness and strength, and can effectively improve the uneven settlement of the subgrade and increase the bearing capacity of the foundation. However, problems such as how to reasonably set the geocell, what height of geocell to use, and how to use appropriate subgrade fillers further affect the stability and bearing capacity of the subgrade. In the prior art, there are no clear regulations on the setting position and height of the subgrade geocell, and a large amount of calculation is prone to errors and time-consuming and laborious. Using numerical simulation methods, it is also impossible to find the optimal layout method suitable for the actual construction site. Therefore, it is necessary to propose an optimization analysis method for the setting of subgrade fillers and geocells. By using simulation analysis software, find the subgrade settlement displacement value under the combined action of various factors, and then use the response optimization analysis method. Through setting 3-level (-1, 0, 1) tests under various factors, finally obtain the interaction cloud diagram and contour map of various factors. Through optimization analysis, finally obtain the best prediction plan, and use this method to quickly and efficiently obtain the optimal setting plan of subgrade fillers and geocells required in actual projects. Summary of the Invention

[0003] In order to solve the above deficiencies in the prior art, the present invention provides an optimization analysis method for the setting of subgrade fillers and geocells. The improvement lies in that the subgrade structure used in the optimization analysis method includes:

[0004] Bedrock 5;

[0005] Base filler 4, laid above the bedrock 5;

[0006] Subgrade filler 3, laid above the base filler 4;

[0007] Subgrade 2, placed above the subgrade filler 3; a geocell is arranged in the subgrade 2; at the same time, the subgrade filler 3 is also filled in the geocell;

[0008] Roadbed surface 1, placed on the upper part of the subgrade 2;

[0009] The optimization analysis method includes the following steps:

[0010] Step S1, conduct a combined simulation analysis on 3 factors to obtain different settlement displacement values under the combined action of the 3 factors; the 3 factors include: the arrangement position of the geocell, the height of the geocell, and the ratio of the subgrade filler;

[0011] Step S2, input the settlement displacement values into the response analysis software and conduct analysis using the Box-Behnken response optimization method;

[0012] Step S3, through the settlement regression analysis results obtained by the response analysis, analyze the primary and secondary effect relationships of the 3 factors on the subgrade settlement;

[0013] Step S4, according to the regression equation model, by analyzing the response surface diagram and the contour diagram, determine the influence of the 3 factors of the experimental design on the subgrade settlement, and take the minimum settlement as the optimization analysis target to obtain the predicted optimal plan.

[0014] Among them, in step S1,

[0015] The arrangement position of the geocell includes a first position set at the top of the subgrade, a second position set in the middle of the subgrade, and a third position set at the bottom of the subgrade;

[0016] The height of the geocell includes: a first height of 100 mm, a second height of 150 mm, and a third height of 200 mm;

[0017] The ratio of the subgrade 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;

[0018] Use finite element software to set the 3 factors to obtain the settlement displacement values under the combined adjustment of different factors.

[0019] Among them, when conducting the 3-factor level (-1, 0, 1) test, set the third ratio to 0, set the first ratio to -1, and set the second ratio to 1;

[0020] Set the first position to 0, set the third position to -1, and set the second position to 1;

[0021] Set the second height to 0, set the first height to -1, and set the third height to 1.

[0022] Among them, in step S2, the analysis using the Box-Behnken response surface optimization method includes the regression equation based on the Box-Behnken design; the expression of the regression equation is as follows:

[0023] ;

[0024] In the formula, Y is the settlement displacement value; β0, β i , β ii , β ij represent the model regression coefficients; X i , X j represent the input factor variables; ε represents the observation error; i represents the i-th factor variable, and j represents the j-th factor variable; there are a total of k factor variables.

[0025] Among them, in step S3, the settlement regression analysis results include obtaining the sum of squared deviations to get the F value for the significance test of the settlement models corresponding to different factors:

[0026] Obtaining the sum of squared deviations includes:

[0027] Obtaining the total sum of squared deviations:

[0028] ; where y i represents the actual observed value of the i-th factor variable; k is the total number of factor variables;

[0029] is the average value of the observed values of all factor variables, .

[0030] Obtaining the regression sum of squares:

[0031] ; represents the predicted value of the i-th factor variable;

[0032] Obtaining the residual sum of squares:

[0033] ;

[0034] Obtaining the F value includes:

[0035] ; n is the total number of samples.

[0036] Among them, in step S3, the settlement regression analysis results also include obtaining the model regression coefficients:

[0037] .

[0038] Among them, in step S4, the analysis of the response surface diagram and the contour diagram includes: based on the response surface diagram and the contour diagram of the subgrade settlement with respect to the position and height of the geocell, it is obtained that when the position of the geocell is 0 and the height of the geocell is between 140 mm and 160 mm, the minimum value of the subgrade settlement can be obtained, which is between 610 mm and 615 mm.

[0039] Among them, in step S4, the analysis of the response surface diagram and the contour diagram includes: based on the response surface diagram and the contour diagram of the subgrade settlement with respect to the position of the geocell and the filler ratio, it is obtained that when the position of the geocell is 0 and the filler ratio of the geocell is 0, the minimum value of the subgrade settlement can be obtained, which is within 615 mm.

[0040] Among them, in step S4, the analysis of the response surface diagram and the contour diagram includes: based on the response surface diagram and the contour diagram of the subgrade settlement with respect to the height of the geocell and the filler ratio, it is obtained that when the height of the geocell is 150 mm and the filler ratio of the geocell is 0, the minimum subgrade settlement can be obtained, which is between 610 mm and 620 mm.

[0041] Among them, in step S4, taking the minimum settlement value as the optimization analysis target, the predicted optimal solution is obtained, including: the geocell is located at the top of the subgrade; when the height of the geocell is 150 mm and the ratio of gravel soil to expansive soil used in the geocell is 2:1, the subgrade settlement is the smallest.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] Through the optimization analysis method of the subgrade filler and the geocell setting, the present application uses the Box-Behnken response optimization method for analysis to analyze the primary and secondary effect relationships of the three factors of the geocell layout position, the geocell height, and the subgrade filler ratio on the subgrade settlement. The Box-Behnken response optimization method design is a three-level experimental design method. By reasonably arranging the test points, it can evaluate multiple factors and their interactions with fewer test times.

[0044] In the in-depth analysis of the contour diagram, the present application finds that when the geocell is arranged at the top of the subgrade with a height of 150 mm and the filler ratio is 2:1 for gravel soil to expansive soil, the subgrade settlement displacement value reaches the relative minimum value, significantly improving the stability of the subgrade. This law provides direct data support and visual basis for optimizing the subgrade structure design, selecting the best construction parameters, etc., and is one of the core technical paths to realize the present invention to reduce the subgrade settlement risk and improve the service life of the road.

[0045] The contour map presented in this application, compared with similar charts in the prior art, can not only simultaneously display the comprehensive influence of multiple key factors (the layout position, height of geocells, and the ratio of subgrade filling materials) on the settlement displacement of the subgrade, but also enable technicians to obtain key information more quickly and accurately through precise scale markings, clear contour line divisions, and intuitive legend explanations, thereby more efficiently adjusting the construction plan, optimizing design parameters, etc., greatly improving the overall efficiency of road engineering design and construction. Brief Description of the Drawings

[0046] Figure 1 is a cross-sectional view of the geocell setting in the subgrade of the present invention;

[0047] Figure 2 is the settlement nephogram simulated by the geocell in the subgrade of the present invention;

[0048] Figure 3-1 is the response surface diagram of the geocell position and height on the subgrade settlement in the present invention;

[0049] Figure 3-2 is the contour map of the geocell position and height on the subgrade settlement in the present invention;

[0050] Figure 4-1 is the response surface diagram of the geocell position and filling ratio on the subgrade settlement in the present invention;

[0051] Figure 4-2 is the contour map of the geocell position and filling ratio on the subgrade settlement in the present invention;

[0052] Figure 5-1 is the response surface diagram of the geocell height and filling ratio on the subgrade settlement in the present invention;

[0053] Figure 5-2 is the contour map of the geocell height and filling ratio on the subgrade settlement in the present invention.

[0054] Reference Signs: 1 - roadbed, 2 - subgrade, 3 - subgrade filling material, 4 - base filling material, 5 - bedrock. Detailed Description of the Invention

[0055] To better understand the present invention, the content of the present invention will be further described below in conjunction with the drawings in the specification and examples.

[0056] An optimization analysis method for the setting of subgrade filling materials and geocells, characterized in that the subgrade structure used in the optimization analysis method includes:

[0057] Bedrock 5;

[0058] Base filling material 4, laid above the bedrock 5;

[0059] The subgrade filler 3 is laid above the base filler 4;

[0060] The subgrade 2 is placed above the subgrade filler 3; a geocell is arranged in the subgrade 2; meanwhile, the subgrade filler 3 is also filled in the geocell;

[0061] The roadbed 1 is placed on the upper part of the subgrade 2.

[0062] Specifically, the subgrade geocell setting system involved in this application includes the roadbed 1 at the top layer. A geocell is arranged in the subgrade 2 below it to improve the anti-bearing capacity of the roadbed. The subgrade filler 3 is laid inside the geocell, and there is also a layer of subgrade filler 3 below it to increase the road surface height. A layer of base filler 4 is laid at the bottom of the subgrade, and finally, the bedrock 5 at the bottom of the subgrade.

[0063] The optimization analysis method includes the following steps:

[0064] Step S1, perform a combined simulation analysis on the arrangement position of the geocell, the height of the geocell, and the ratio of the subgrade filler to obtain the settlement displacement value under the combined action of the arrangement position of the geocell, the height of the geocell, and the ratio of the subgrade filler;

[0065] Step S2, input the settlement displacement value into the response analysis software and perform analysis using the Box-Behnken response optimization method to conduct a 3-factor level experiment for design;

[0066] Step S3, analyze the primary and secondary effect relationships of the 3 factors on the subgrade settlement through the settlement regression analysis results obtained from the response analysis;

[0067] Step S4, according to the regression equation model, determine the influence of the 3 factors of the experimental design on the subgrade settlement by analyzing the response surface diagram and the contour diagram, and take the minimum settlement as the optimization analysis target to obtain the predicted optimal solution.

[0068] The geocell is a net-like geocell structure formed by welding or riveting high-strength HDPE or PP copolymer broadband forcefully. After the geocell is unfolded, it presents a three-dimensional net-like structure. Each cell is an independent spatial unit, which are interconnected to form an integral net-like 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 15 cm below the top of the roadbed surface. In the field of roadbed technology, the roadbed surface is the foundation of the road surface, which refers to the roadbed part within a certain depth range below the bottom surface of the road surface. Generally speaking, the depth from the top of the roadbed surface to the ground is about 0.8 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 surface is to provide stable support for the road surface structure layer, bear the vehicle load transmitted from the road surface, and evenly transfer the load to the underlying roadbed soil layer. It needs to have a certain strength, stiffness and stability to ensure the service performance and service life of the road surface. The middle of the roadbed is determined by the laying thickness of the roadbed. The geocell at the second position is in the middle position of the roadbed. The geocell at the bottom of the roadbed refers to the geocell laid on the filler with a laying height of about 15 cm at the bottom of the roadbed. Among them, when using the simulation analysis software, the geocell has three laying forms, namely, laid at the top of the roadbed, laid in the middle of the roadbed and laid at the bottom of the roadbed. At the same time, the modulus of the geocell is uniformly adopted as 300 MPa.

[0069] The heights of the geocell include: a first height of 100 mm, a second height of 150 mm, and a third height of 200 mm. Specifically, the geocell adopts three different heights, namely 100 mm, 150 mm and 200 mm.

[0070] The roadbed filler is a mixture of gravel soil and expansive soil; the ratios of the roadbed filler include: 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 its 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 the finite element analysis.

[0071] Table 1 Basic parameters for model establishment

[0072]

[0073] The optimization analysis method includes the following steps:

[0074] Step S1, perform a combined simulation analysis on the layout position of the geocell, the height of the geocell, and the ratio of the subgrade filler to obtain the settlement displacement value under the combined action of the layout position of the geocell, the height of the geocell, and the ratio of the subgrade filler. Specifically, use the L9(3 4 ) orthogonal table, where "L" represents the orthogonal table, "9" represents the number of tests, "3" represents the number of levels of each factor, and "4" represents the maximum number of factors that can be arranged (including interaction effects, etc.).

[0075] Among them, when performing response analysis in the 3-level (-1, 0, 1) test, the 3-factor combinations are as follows:

[0076] Set the third ratio to 0, set the first ratio to -1, and set the second ratio to 1;

[0077] Set the first position to 0, set the third position to -1, and set the second position to 1;

[0078] Set the second height to 0, set the first height to -1, and set the third height to 1.

[0079] Specifically, when performing response analysis in the 3-level (-1, 0, 1) test, set the ratio of gravel soil to expansive soil of 2:1 to 0, set the ratio of gravel soil to expansive soil of 1:1 to -1, and set the ratio of gravel soil to expansive soil of 1:2 to 1; in the 3-level (-1, 0, 1) test, set the geocell laid on the top of the subgrade to 0, set the geocell laid at the bottom of the subgrade to -1, and set the geocell laid in the middle of the subgrade to 1; in the 3-level (-1, 0, 1) test, set the geocell height of 150 mm to 0, set the geocell height of 100 mm to -1, and set the geocell height of 200 mm to 1, as shown in Table 2.

[0080] Table 2 Experimental design table for three-factor level response surface analysis

[0081]

[0082] There are a total of 9 groups of simulation conditions. Each group is calculated 3 times and the mean value is taken to reduce random errors. The 3-level (-1, 0, 1) test analysis will be automatically performed in the response surface analysis software. Use the finite element software to set 3 factors (the layout position of the geocell, the height of the geocell, and the ratio of the subgrade filler) to obtain the settlement displacement values under the combined adjustment of different factors. At the same time, obtain a settlement contour map such as Figure 2 . The values in the settlement contour map represent the settlement displacement values, that is, obtain the settlement displacement values required for the response analysis in the subsequent steps through Figure 2 .

[0083] Step S2: Substitute each of the settlement displacement values into the response analysis software and perform analysis using the Box-Behnken response optimization method. Specifically, after substituting each of the settlement displacement values into the response analysis software, the response analysis software automatically analyzes each settlement displacement value. Among them, the Box-Behnken response optimization method is a three-level experimental design method. By reasonably arranging the experimental points, it can evaluate multiple factors and their interactions with fewer experimental times. This method is based on a second-order response surface model, assuming a quadratic function relationship between the response variable and the factors. By fitting the response surface equation with experimental data, the optimal factor combination can be found.

[0084] The regression equation based on the Box-Behnken design is a quadratic polynomial equation, and its general form is as follows:

[0085]

[0086] In the formula, Y represents the response variable, that is, the settlement displacement value (or called the subgrade settlement amount), β0, β i , β ii , β ij represent the model regression coefficients; X i , X j represent the input factor variables, that is, factors such as the layout position of the geocell, the height of the geocell, and the mixture ratio of the subgrade filler; ε represents the observation error. i represents the i-th factor variable, and j represents the j-th factor variable. There are a total of k factor variables.

[0087] Step S3: Analyze the primary and secondary effect relationships of the three factors on the subgrade settlement through the settlement regression analysis results obtained from the response analysis. Specifically, through the settlement regression analysis results obtained from the response analysis, the regression analysis result table of the settlement model and regression coefficients is obtained, as shown in Table 3. Analyze the primary and secondary effect relationships of the three factors on the subgrade settlement, that is, by observing the P values in the regression analysis result table of the settlement model and regression coefficients to see the influence of each factor on the required settlement value of the experiment.

[0088] As shown in Table 3, the regression analysis result table of the settlement model and regression coefficients is explained. In the response surface analysis, a data table of the regression analysis results of the settlement model and regression coefficients is automatically generated. By inputting the settlement displacement values obtained from the finite element analysis, Table 3 can be obtained.

[0089] Table 3 Regression analysis results of the settlement model and regression coefficients

[0090]

[0091] Note: P < 0.01 is highly significant, denoted by **; P < 0.05 is significant, denoted by *; P > 0.05 is not significant, denoted by ns.

[0092] Among them, the "sum of squared deviations" is the sum of the squares of the deviations of the dependent variable (such as the settlement displacement value) from its mean, reflecting the total variation degree of the data.

[0093] In regression analysis, the sum of squared deviations is decomposed into three parts:

[0094] First, the total sum of squares (SST), meaning: the total deviation of all observed values from the mean of the dependent variable, representing the total fluctuation range of the data. Total sum of squares formula: .

[0095] 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.).

[0096] is the average of all factor variable observed values, .

[0097] Second, the regression sum of squares (SSR), meaning: the sum of the squares of the deviations between the model predicted values and the mean of the dependent variable, representing the amount of variation that the model can explain. Regression sum of squares formula: . represents the predicted value (or fitted value) of the i-th factor variable, that is, the theoretical settlement value calculated through the regression equation.

[0098] Third, the residual sum of squares (SSE), meaning: the sum of the squares of the deviations between the observed values and the model predicted values, representing the random error not explained by the model. Residual sum of squares formula:

[0099]

[0100] The degrees of freedom in Table 3 represent the number of independent variables in the calculation of the statistic.

[0101] In the regression analysis of the settlement model, the mean square (MS) is the ratio of the sum of squares to the corresponding degrees of freedom (df), used to measure the variation degree of the data. Its core role is to eliminate the influence of the sample size difference by standardizing the variation amount, thereby supporting the model significance test (such as the F test).

[0102] Perform an analysis of variance and significance test on the regression equation of the settlement model. The F value is used for the significance test of the model.

[0103] The magnitude of the F value is an important indicator for evaluating the influence degree of each variable on the response value. The larger the F value, the higher the contribution degree of the relevant model to the response. The P value is the data obtained after its significance evaluation, and the significance of the factor is obtained by comparing it with 0.01 and 0.05. P < 0.01 is extremely significant, P < 0.05 is significant, and P > 0.05 is not significant.

[0104] The AB model is: combine factor A and factor B; the AC model is: combine factor A and factor C; the BC model is: combine factor B and factor C, reflecting their combined influence on the required settlement value of the experimental results.

[0105] A 2 The model is: that is, the 3 parallel factors set in A are combined with factor A itself; B 2 The model is: that is, the 3 parallel factors set in B are combined with factor B itself; C 2 The model is: that is, the 3 parallel factors set in C are combined with factor C itself, obtaining the influence on the experimental settlement value.

[0106] The F value formula of the settlement model is as follows:

[0107] , where k is the total number of factor variables in the model, including the 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.

[0108] The magnitude of the F value is an important indicator for evaluating the influence degree of each variable on the response value. The larger the F value, the higher the contribution degree of the relevant model component to the response. It can be seen from Table 3 that the F value of the settlement model is 9.08, the P of its regression model < 0.0001 (extremely significant), and the P of its lack-of-fit term = 0.3010 > 0.05 (not significant), indicating that the model has a good fitting degree and can predict the corresponding regression value of the regression equation.

[0109] Evaluating the model fitting effect is determined by the R 2 value (coefficient of determination).

[0110] , indicating the proportion of the variation explained by the model.

[0111] Table 3 shows the model regression coefficient R 2= 0.9681, which indicates that the change in about 97% (rounded) of the settlement displacement values can be explained by the combination of the geocell position, height, and filler ratio. At the same time, due to Model B and Model C 2 The corresponding P-value is smaller, indicating that the single factor of the geocell height has a significant impact on the settlement result; and it also shows that the filler ratio can also have a significant effect on the settlement under its own interaction with itself. That is, Model B (geocell height) and Model C (subgrade filler ratio) have a significant effect on the settlement.

[0112] AdjR 2 Also known as adjusted R 2 , that is, a correction index considering the number of independent variables to avoid overfitting of the model. The adjusted R shown in Table 3 2 = 0.9572 (greater than 0.8000), indicating that 95.72% of the data can be explained by this model, showing that the equation has a high reliability.

[0113] Step S4, according to the regression equation model, by analyzing the response surface diagram and contour diagram, determine the influence of the three factors in the experimental design on the subgrade settlement, and take the minimum settlement as the optimization analysis target to obtain the predicted optimal solution. Specifically, by analyzing the response surface diagram and contour diagram, determine the influence of the three factors in the experimental design on the subgrade settlement, and finally find the predicted optimal solution, where the geocell position is at the top of the subgrade, the geocell height is 150 mm, and the best filler ratio is 2:1 for gravel soil and expansive soil.

[0114] In the analysis of the influence of the three factors (geocell position, geocell height, and subgrade filler ratio) involved in this application on the subgrade settlement, the contour diagrams generated by finite element analysis software such as ABAQUS clearly and intuitively show the influence degree of the interaction of each factor on the response value (subgrade settlement), providing a key basis for understanding and optimizing the subgrade structure stability.

[0115] In the response analysis, the influence degree of each factor on the response value can be determined by observing the shape of the response surface and contour. The steeper the response surface, the more obvious the interaction of each factor. It can be seen from the attached drawings that the position, height, and filler ratio of the geocell all have an impact on the subgrade settlement,

[0116] Figure 3-1 and Figure 3-2 are the response surface diagram and contour diagram of the influence of the geocell position and height on the subgrade settlement in the present invention. The cloud diagram and contour both have undulating changes, indicating that the interaction between the geocell position and height has an impact on the subgrade settlement. Specifically, from Figure 3-1 and Figure 3-2It can be seen that when the position of the geocell is 0 and the height of the geocell is between 140 mm and 160 mm, the subgrade settlement is the smallest, between 610 mm and 615 mm.

[0117] Figure 4-1 and Figure 4-2 are the response surface diagram and contour diagram of the position of the geocell and the filler ratio on the subgrade settlement in the present invention. In its nephogram, when the geocell is laid at positions -1, 0, 1, the subgrade settlement shows a trend of first decreasing and then increasing with the change of the ratio, indicating that the interaction between the position of the geocell 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 position of the geocell is 0 and the filler ratio of the geocell is 0, the subgrade settlement is the smallest, within 615 mm.

[0118] Figure 5-1 and Figure 5-2 are the response surface diagram and contour diagram of the height of the geocell and the filler ratio on the subgrade settlement in the present invention. In its nephogram, when the height of the geocell is 100 mm, 150 mm, 200 mm, the subgrade settlement shows a trend of first slowly decreasing and then rapidly increasing with the change of the filler ratio, which also indicates that the interaction between the height of the geocell 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 height of the geocell is 150 mm and the filler ratio of the geocell is 0, the subgrade settlement is the smallest, between 610 mm and 620 mm.

[0119] Therefore, when the position of the geocell is 0, its corresponding position is the top of the subgrade; when the height of the geocell is 150 mm and the filler ratio of the geocell is 0, and the corresponding ratio is that the ratio of gravel soil to expansive soil is 2:1, the subgrade settlement is the smallest. Finally, the best prediction scheme with the position of the geocell at the top of the subgrade, the height of the geocell being 150 mm, and the filler ratio being the ratio of gravel soil to expansive soil being 2:1 is obtained. In the later stage, technical engineers can carry out actual construction according to this scheme.

[0120] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take 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 code.

[0121] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0122] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0124] The above are only embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention pending approval.

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, the first height to -1, and 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 used as the optimization analysis target to obtain the best prediction solution, including: the geocell is located on the top 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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