Design method and system for improvement and solidification scheme of subgrade soil

Abstract

The present invention provides a method and system for designing an improved solidification solution for subgrade soil, relating to the field of soil solidification, including: obtaining soil solidification data, expanding the soil solidification data through deep learning to obtain a curing agent information database; screening in the curing agent information database according to soil parameters to obtain a basic formula and corresponding strength data; constructing an initial relationship equation based on the basic formula; designing the coded levels of influencing factors through central composite design to obtain corresponding true values, and fitting the initial relationship equation to obtain an early strength equation and a late strength equation; finding the optimal intersection of the solutions of the early strength equation and the late strength equation to obtain a target formula, and obtaining an improved solidification solution according to the target formula. The present invention automatically calculates and outputs the optimal ratio of raw materials through deep learning and central composite design, thereby realizing intelligent design of the soil curing agent formula.

Description

Technical Field

[0001] The present invention relates to the technical field of soil solidification, and in particular, to a method and system for designing an improved solidification scheme for subgrade soil. Background Art

[0002] A soil solidifying agent is a material used to improve the properties of soil, which improves the engineering properties of soil by mixing with the soil and undergoing chemical reactions or physical actions. These soil solidifying agents can improve the properties of soil by increasing the compressive strength and bearing capacity of the soil, improving soil stability, reducing the liquidity index, and enhancing impermeability. At present, most soil solidification constructions select appropriate soil solidifying agents based on different types of subgrade soil in existing implementation cases, but cannot predict or simulate the optimal proportioning scheme of soil solidifying agents that may exist outside the existing implementation cases. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for designing an improved solidification scheme for subgrade soil to solve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows:

[0004] A method for designing an improved solidification scheme for subgrade soil includes:

[0005] Obtain soil solidification data, where each piece of the soil solidification data includes soil parameters, the used solidifying agent formula, and soil compressive strength; expand the soil solidification data through deep learning to obtain a solidifying agent information database;

[0006] Obtain the soil parameters of the construction area, and screen in the solidifying agent information database according to the soil parameters to obtain a basic formula and corresponding strength data, where the basic formula includes the raw materials in the formula and their corresponding contents, and the strength data includes test strength data and predicted strength data;

[0007] Based on the basic formula, construct an initial relationship equation with the soil solidification strength as the response value and the contents of each raw material as influencing factors;

[0008] Based on the basic formula, design the coded levels of the influencing factors through central composite design and obtain the corresponding true values, where the true values represent the contents of each raw material, and find the corresponding strength data according to the true values;

[0009] Fit the initial relationship equation according to the true values and strength data and perform variance analysis, and perform elimination processing on the terms in the initial relationship equation based on the variance analysis results to obtain an early strength equation and a late strength equation;

[0010] Taking the maximization of the solidification strength of the soil mass as the goal, the intersection of the solutions of the early strength equation and the late strength equation is obtained to get the target formula, and the improved solidification scheme is obtained according to the target formula.

[0011] On the other hand, the present application also provides a design system for the improved solidification scheme of subgrade soil masses, including:

[0012] A first acquisition module, used to acquire soil solidification data, each piece of the soil solidification data including soil parameters, the curing agent formula used, and the soil compressive strength; the soil solidification data is expanded through deep learning to obtain a curing agent information database;

[0013] A second acquisition module, used to acquire the soil parameters of the construction area, and screen in the curing agent information database according to the soil parameters to obtain the basic formula and the corresponding strength data, the basic formula including the raw materials in the formula and the corresponding contents, and the strength data including test strength data and predicted strength data;

[0014] A construction module, used to construct an initial relationship equation based on the basic formula, with the soil solidification strength as the response value and the contents of each raw material as the influencing factors;

[0015] A design module, used to design the coded levels of the influencing factors through central composite design based on the basic formula and obtain the corresponding true values, the true values representing the contents of each raw material, and find the corresponding strength data according to the true values;

[0016] A fitting module, used to fit the initial relationship equation according to the true values and the strength data and perform variance analysis, and perform elimination processing on the terms in the initial relationship equation based on the variance analysis results to obtain an early strength equation and a late strength equation;

[0017] A first calculation module, used to take the maximization of the soil solidification strength as the goal, find the intersection of the solutions of the early strength equation and the late strength equation to obtain the target formula, and obtain the improved solidification scheme according to the target formula.

[0018] The beneficial effects of the present invention are as follows:

[0019] The present invention uses a deep learning model to expand the existing soil solidification data, enrich the data volume, and optimally select the most suitable soil formula according to the soil conditions. In order to further determine the optimal ratio of each raw material in the soil formula, the present invention conducts an optimization experiment through central composite design with multiple factors and multiple levels, automatically calculates and outputs the optimal ratio of the raw materials, so as to realize the intelligent design of the soil curing agent formula.

[0020] Other features and advantages of the present invention will be described in the following specification, and in part will be obvious from the specification, or can be understood by implementing the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0022] Figure 1 It is a flowchart of the design method for the improvement and solidification scheme of the subgrade soil body in the embodiments of this application;

[0023] Figure 2 It is a schematic diagram of the three-dimensional calculation model of the foundation in the embodiments of this application;

[0024] Figure 3 It is a schematic diagram of the monitoring point layout in the embodiments of this application;

[0025] Figure 4 It is a schematic diagram of the method model for the shallow solidification double-layer foundation in the embodiments of this application;

[0026] Figure 5 It is a schematic diagram of the method model for the shallow solidification + plain concrete pile composite foundation in the embodiments of this application;

[0027] Figure 6 It is a schematic diagram of the method for the shallow solidification + cement mixing pile composite foundation in the embodiments of this application;

[0028] Figure 7 It is a schematic diagram of the system structure for the improvement and solidification scheme design of the subgrade soil body in the embodiments of this application.

[0029] Reference signs in the figures: 100 - First acquisition module; 110 - Classification unit; 120 - First construction module; 130 - Expansion unit; 140 - Second construction unit; 200 - Second acquisition module; 210 - First acquisition unit; 220 - First screening unit; 230 - Second screening unit; 300 - Construction module; 400 - Design module; 500 - Fitting module; 600 - First calculation module; 700 - Prediction module; 800 - Second calculation module; 1000 - Generation module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and illustrated herein generally may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0031] It should be noted that like reference numerals and letters denote like items in the following figures, and thus, once an item is defined in one figure, it does not require further definition and explanation in subsequent figures.

[0032] See Figure 1 , this application provides a method for designing an improved solidification solution for subgrade soil, including steps S100, S200, S300, S400, S500, and S500;

[0033] Step S100: Obtain soil solidification data. Each piece of the soil solidification data includes soil parameters, the curing agent formula used, and the soil compressive strength; expand the soil solidification data through deep learning to obtain a curing agent information database;

[0034] Specifically, the soil solidification data can use the open-source relational database management system MySQL as the data storage platform. A series of data including complete soil parameters (soil density ρ, water content w, liquid limit W L , plastic limit W P ), the curing agent formula used (the raw materials of the curing agent used and the content of each raw material), and the soil compressive strength (including: 7-day unconfined compressive strength Y7 and 28-day unconfined compressive strength Y 28 ) is used as 1 piece of the recorded soil solidification data. The source of each piece of data can be: ① the results of completed laboratory tests; ② the test results published in journals; ③ the test results of published patents.

[0035] The step of expanding the soil solidification data through deep learning to obtain a curing agent information database includes the steps:

[0036] S110: Traverse the soil solidification data, classify the data with the same soil parameters and the same curing agent raw materials, and obtain the soil solidification data packets for each type of soil;

[0037] S120. Perform deep learning based on each of the soil solidification data packets, using soil parameters and solidifying agent formulations as inputs and soil compressive strength as the output, to construct a first prediction model;

[0038] The specific steps of deep learning are as follows: Classify the data information in each "soil solidification data packet". Specifically, take the complete soil parameters and the solidifying agent formulations used as the input labels of the deep learning neural network. At the same time, take the corresponding soil compressive strength as the output label. After both the input labels and the output labels are classified, randomly divide each "soil solidification data packet" into a training set, a validation set, and a test set according to the ratio of 7:2:1 in terms of the number of items.

[0039] The activation functions considered in the constructed deep learning neural network model can optionally include Sigmoid, ReLU, Tanh, Leaky ReLU; the deep learning neural network model uses dynamic adjustment of the learning rate to improve the optimization effect, which can optionally include learning rate decay and cosine annealing; the optimizers that the deep learning neural network model can choose from include: stochastic gradient descent, Adam, Adagrad, and Adadelta. Moreover, to improve the construction efficiency of the deep learning neural network model, a deep learning neural network model construction program based on AI learning can be used. The deep learning neural network model construction program based on AI learning learns at least 1000 groups of deep learning neural network construction processes with a prediction accuracy rate of over 90%. The AI learning content includes the input labels, the number of output labels, the determination process of the neural network model, the adjustment process of the number of neurons in the hidden layer, the adjustment process of the number of hidden layers, the adjustment process of the weight type, the determination process of the activation function, the adjustment process of the optimization algorithm model, etc. in the process of constructing a deep learning neural network with a prediction accuracy rate of over 90%.

[0040] In this embodiment, a first prediction model is constructed for each soil solidification data packet, that is, each type of soil (with the same soil parameters) has its own exclusive first prediction model.

[0041] Train the deep learning neural network model constructed for each "soil solidification data packet", and use the test set to evaluate the performance of the trained model. It is required that the prediction accuracy of the deep learning neural network model corresponding to each "soil solidification data packet" is greater than 90%. At the same time, in the case where the prediction accuracy of the deep learning neural network model corresponding to the "soil solidification data packet" is lower than 90%, first record the architecture of the deep learning neural network model (including: neural network model, number of neurons in the hidden layer, number of hidden layers, weight type, activation function type, optimization algorithm model, etc.), reconstruct the deep learning neural network model corresponding to the individual "soil solidification data packet", and shield the recorded architecture of the deep learning neural network model until the prediction accuracy of the deep learning neural network model corresponding to the constructed "soil solidification data packet" is greater than 90%.

[0042] S130. Multiply the soil parameters and the curing agent formula by different preset coefficients to obtain extended parameters, and arrange and combine the extended parameters in sequence to obtain an extended parameter group. Input the extended parameter group into the first prediction model to obtain the predicted soil compressive strength:

[0043] As an example, identify the soil parameters and the curing agent formula used, and independently magnify each data by coefficients of 50%, 60%, 70%, 80%, 90%, 100%, 110%, 120%, 130%, 140%, and 150% to obtain extended parameters. At the same time, perform different permutations and combinations on each obtained extended parameter, list all permutation and combination cases, and obtain an extended parameter group (for example: 50%×ρ, 70%×w, 100%×W L 、120%×W P 、70%×P.O 42.5 grade cement content, 130%×fly ash content, 50%×basalt fiber content, 90%×polyvinyl alcohol resin content);

[0044] Input each extended parameter group into the first prediction model respectively to obtain the soil compressive strength under each parameter permutation and combination.

[0045] S140. Store all extended parameter groups and the corresponding predicted soil compressive strength to construct a curing agent information database.

[0046] S200. Obtain the soil parameters of the construction area, and screen in the curing agent information database according to the soil parameters to obtain the basic formula and the corresponding strength data. The basic formula includes the raw materials in the formula and the corresponding contents, and the strength data includes test strength data and predicted strength data; specifically includes the steps:

[0047] S210. Obtain the material information within the preset range of the construction area and determine the priority raw materials;

[0048] The construction sites, factories and mines within 5 kilometers of the construction area can be captured in real time through UAV aerial photography technology and online map tools, so as to obtain information on available raw materials nearby and use them as priority raw materials. Considering factors such as resource conservation, environmental protection, and reduction of project cost, natural soil and construction waste (mainly inorganic non-metallic construction solid waste that can be obtained nearby) can be selected as the raw materials for the curing agent.

[0049] S220. Screen in the curing agent information database based on the soil parameters to obtain the preliminary screened curing agent formula data;

[0050] Obtain the soil parameters input by the user, match them in the curing agent information database, and select the data in the database where the gap between each value of the soil parameters and the input soil parameters is not greater than 90% to obtain the preliminary screened curing agent formula data;

[0051] S230. Screen out the curing agent formula data including priority raw materials from the preliminary screened curing agent formula data, and then screen the curing agent formula data according to the corresponding soil compressive strength to obtain the basic formula and the corresponding strength data.

[0052] After screening out the curing agent formula data including priority raw materials, sort them in descending order according to the soil compressive strength corresponding to each data, and select the top 20% of the data; further, if the soil compressive strength of the selected data all meets the requirements, the information on the market price and transportation cost of each curing agent raw material obtained by calling a web crawler can be used to calculate the cost corresponding to each screened data, and the data with the lowest cost is selected to obtain the basic formula and the corresponding strength data. The screening results of this embodiment are: P.O42.5 grade cement: 2% - 8%; fly ash: 2% - 6%; basalt fiber: 0.1% - 0.5%; polyvinyl alcohol resin (PVA): 0.1% - 0.5% (mass percentage).

[0053] S300. Based on the basic formula, construct an initial relationship equation with the soil curing strength as the response value and the content of each raw material as the influencing factor;

[0054] The initial relationship equation between the influencing factor and the response value is established as shown in Equation (1):

[0055]

[0056] In the formula, Y is the equation prediction value (set the 7-day unconfined compressive strength Y7 and the 28-day unconfined compressive strength Y 28 as 2 groups of prediction values), k is the number of experimental influencing factors, β0 is the equation intercept, β iis the linear coefficient, β ij is the interaction coefficient, β ii is the squared coefficient, where i and j represent two different influencing factors, i = 1, 2, 3…k, j = 1, 2, 3…k.

[0057] S400. Based on the basic formula, perform the coded level design of the influencing factors through central composite design and obtain the corresponding true values, where the true values represent the contents of each raw material, and find the corresponding strength data according to the true values; including the steps:

[0058] Determine the number of influencing factors for central composite design according to the number of types of raw materials in the basic formula; there are as many influencing factors as there are raw materials;

[0059] Determine the central value, high value, and low value of each influencing factor according to the content ranges of the raw materials in the basic formula; the high value and low value are the highest and lowest contents of the raw material, and the central value is the middle value of the content range of the raw material;

[0060] Determine the step size x of the level change of each influencing factor according to the preset coded level and the high value and low value of the influencing factor b :

[0061]

[0062] where x 低 is the low value, x 高 is the high value; the number of steps b 步数 is determined by the coded level, with a one-step interval between every two adjacent coded levels;

[0063] Calculate the true values corresponding to the coded levels according to the coded levels, step sizes of level change, and central values of each influencing factor.

[0064] The logical design of the central composite design optimization experiment with multiple factors and multiple levels includes the cube points, axial points, and central points of the full factorial or fractional factorial design;

[0065] The cube points (corner points) are the basis of the design and are usually a 2 k full factorial design, where k is the number of influencing factors. In more complex cases, a fractional factorial design may be used to reduce the number of experiments required. In this embodiment, the influencing factor is the number of raw materials in the formula, and based on the basic formula screened in S230, k = 4;

[0066] The axial points (star points) are located on each factor axis at a certain distance from the design center and are usually represented by α. This distance can be fixed or optimized according to the needs of the experiment.

[0067] To meet the requirement of rotation, the value of α should be calculated according to Equation (2):

[0068]

[0069] The center point is at the central level of all factors. The main function of the center point is to estimate the experimental error and detect the curvature of the curve. Usually, the experiment will be repeated multiple times to improve the accuracy of the estimation. The number of center points is recorded as C0. The number of center points is taken according to Table 1:

[0070] Table 1 Relationship between the number of influencing factors and the number of center points

[0071]

[0072] As can be seen from the above, the total number of designed experiments is calculated according to Equation (3):

[0073] Total number of experiments = 2 k + 2k + C0(3)

[0074] X i is the coded value parameter (i = 1, 2, 3... k), and each curing agent component (influencing factor) corresponds to an X i , in this embodiment, the curing agent raw materials determined by step S230 are: P.O42.5 grade cement (X1): fly ash (X2): basalt fiber (X3): polyvinyl alcohol resin (X4).

[0075] The X during design i The specific value is determined according to Equation (4):

[0076] X i = (x i - x0) / Δx i (4)

[0077] In the formula, x i represents the true value of each influencing factor, x0 represents the central value of each influencing factor, and Δx i represents the step size of the change in the level of each influencing factor.

[0078] In this embodiment, P.O42.5 cement, fly ash, basalt fiber, and polyvinyl alcohol resin (PVA) are initially determined as the curing agent materials for highway projects. To obtain the optimal mixing ratio under the interaction of the above four materials, an optimization experiment with 4 factors and 5 levels is designed. Substituting into formula (2) for calculation, the number of central points is taken as α = 2. Therefore, the four influencing factors, namely P.O42.5 cement (X1), fly ash (X2), basalt fiber (X3), and polyvinyl alcohol resin (X4), are coded according to formula (4), and the coding levels are shown in Table 2. A test model is constructed, the names, units, low values, and high values of each influencing factor are input, and C0 = 6 is set. At the same time, two groups of prediction results (Y7 and Y 28 ) are set. Specifically, taking P.O42.5 cement as an example:

[0079] The true value range of P.O42.5 cement is x 低 = 2% to x 高 = 8%; x 低 is the lowest content, and x 高 is the highest content;

[0080] Calculate the central value x0 as:

[0081]

[0082] Calculate the step size of the level change x b :

[0083] The coding levels -2, -1, 0, 1, 2 are given. So, from -2 to -1 is 1 step, from -1 to 0 is 1 step, and so on. In the case of 5 coding levels, the number of steps b 步数 = 4;

[0084]

[0085] The coding levels -2, -1, 0, 1, 2 are given. According to formula (4), the true values corresponding to the coding levels are calculated by inverse calculation:

[0086] When X1 = -2: x1 = 5% + (-2)·1.5% = 2%;

[0087] When X1 = -1: x1 = 5% + (-1)·1.5% = 3.5%;

[0088] When X1 = 0: x1 = 5% + 0·1.5% = 5%;

[0089] When X1 = 1: x1 = 5% + 1·1.5% = 6.5%;

[0090] When X1 = 2: x1 = 5% + 2·1.5% = 8%;

[0091] Table 2 Experimental influencing factors and coding levels

[0092]

[0093] The coding levels of each influencing factor are arranged and combined, and the soil compressive strength corresponding to different combinations is obtained. The results are shown in Table 3:

[0094] Table 3 Experimental plan and results

[0095]

[0096]

[0097] The coding levels of schemes No. 25-30 are all 0, but the corresponding curing strengths are different. This is because the curing agent information database contains data from multiple real parallel tests under the same ratio (in line with probability theory to avoid contingency), that is, test strength data, and also includes the data predicted by the first prediction model based on the ratio, that is, predicted strength data.

[0098] S500, fitting the initial relationship equation according to the true value and the strength data and performing variance analysis, eliminating the items in the initial relationship equation based on the variance analysis result, and obtaining an early strength equation and a late strength equation;

[0099] Based on the test scheme and results in Table 3, existing analysis tools can be used to automatically derive the relationship equation and perform variance analysis. The variance analysis results of the second-order model of the solidified soil for 7 days and the variance analysis results of the second-order model of the solidified soil for 28 days in this embodiment are shown in Tables 4 and 5, where the P value is an indicator used to evaluate the statistical significance of the differences between the data obtained from the experiment. It means whether the differences between different treatment levels are caused by random changes or by real influencing factors. Specifically, the P value represents the probability of a more extreme situation than the sample result when the null hypothesis is true. Therefore, a significance level d needs to be given in the test of models and factors. When P < d, the corresponding items are considered to have significant differences, and when P is greater than d, the corresponding items are considered to have no significant differences.

[0100] The F value is a statistic, F = between-group variance / within-group variance, which is used to test whether there are significant differences in the means between different groups.

[0101] Table 4 Results of variance analysis of the second-order model for 7-day stabilized soil

[0102]

[0103] From Table 4, we can see that the P value of the 7d strength model of the solidified soil is less than 0.0001 and R 2 =0.9873, indicating that the model fit is high; R2 Refers to the coefficient of determination, which is the square of the correlation coefficient. The coefficient of determination represents the proportion of the variance of the dependent variable that can be explained by the independent variable.

[0104] Analyzing the test results of each influencing factor again, it is found that the P-values corresponding to X1X3, X2X3, X2X4, and X3X4 are 0.7588, 0.6751, 0.7665, and 0.9734 respectively, all > b = 0.1. Therefore, it is considered that the influence of these four items on the 7-day strength of the solidified soil is not significant, and they are removed. Finally, the 7-day strength equation of the solidified soil, that is, the early strength equation, is given as shown in the formula:

[0105]

[0106] Table 5 Analysis of variance results of the second-order model of the 28-day strength of solidified soil

[0107]

[0108] As can be seen from Table 5, the P-value of the 28-day strength model of the solidified soil < 0.0001 < b, and R 2 = 0.9524, indicating that the model has a high fitting degree; analyzing the test results of each influencing factor again, it is found that the P-values corresponding to X3, X1X3, X2X3, X2X4, and X3X4 are 0.5159, 0.3745, 0.6973, 0.6263, and 0.9050 respectively, all > b = 0.1. Therefore, it is considered that the influence of these four items on the 7-day strength of the solidified soil is not significant, and they are removed. Finally, the 28-day strength equation of the solidified soil, that is, the late strength equation, is given as shown in the formula:

[0109]

[0110] Taking S600 and aiming at maximizing the solidification strength of the soil body, the intersection of the solutions of the early strength equation and the late strength equation is obtained to get the target formula, and the improved solidification scheme is obtained according to the target formula. The calculated target formula of the soil solidifying agent in this embodiment is: P.O42.5 grade cement: fly ash: basalt fiber: polyvinyl alcohol resin = 8.00: 4.91: 0.34: 0.46.

[0111] Through the above steps, the optimal solidifying agent formula for a specific soil body is determined. In addition, the regulation of soil body deformation needs to be considered to determine the specific solidification construction method. Therefore, the present invention also includes step S700: determining the soil solidification construction process based on numerical simulation, specifically as follows:

[0112] S710. Obtain the curing performance information based on the target formula through a pre-established second prediction model. The curing performance information includes the early curing strength, early elastic modulus, late curing strength, and late elastic modulus of the soil under different confining pressure conditions.

[0113] The confining pressure conditions can be 0 kPa (unconfined), 50 kPa, 100 kPa, 200 kPa, 300 kPa, 400 kPa, and 500 kPa.

[0114] S720. Obtain the cohesion parameter and internal friction angle parameter of the solidified soil based on the curing performance information. The cohesion parameter and internal friction angle parameter can be calculated through the Mohr-Coulomb strength criterion.

[0115] In this embodiment, the model parameters in Table 6 are calculated:

[0116] Table 6 Model Parameters

[0117]

[0118] S730. Construct a three-dimensional model of the foundation in the construction area based on the curing performance information, cohesion parameter, and internal friction angle parameter, and obtain the settlement displacement of the foundation under different curing construction methods through numerical simulation. Determine the target curing construction method based on the settlement displacement.

[0119] The different curing construction methods generally include: shallow curing double-layer foundation method, shallow curing + plain concrete pile composite foundation method, shallow curing + cement mixing pile composite foundation method.

[0120] S740. Obtain the improved curing plan according to the target formula and the target curing construction method.

[0121] Obtain the settlement displacements under three curing construction methods and compare them with the national specifications, such as the "Code for Design of Highway Subgrades", as shown in Table 7.

[0122] Table 7 Allowable Post-construction Settlement of Soft Soil Subgrade (unit: cm)

[0123]

[0124] Select the curing construction process with the lowest cost and meeting the design requirements.

[0125] The following is an illustration with specific embodiments: According to the on-site survey report, the location of a certain section in a region was originally a lotus root planting field with weeds distributed in the field. The surface was covered with silty clay with a thickness of 3 - 5 m, and the underlying layer was sandy mudstone intercalated with sandstone. The groundwater level in this section was relatively high, and the soil mass was in a soft to firm plastic state, being a highly compressible soil mass. The maximum filling height of the embankment was approximately 10 m, and uneven settlement was likely to occur. The soil mass was subjected to improvement and solidification treatment with an average treatment depth of 3.0 m. The overall width of the integral subgrade of the highway project in this area was 34.5 m, the width of the separated subgrade was 2×17.0 m, and the filling height of the embankment was 10 m. A platform with a width of 2 m was set at the middle height of 5 m to ensure the stability of the embankment. The slope gradient above the platform was 1:1.5, and the slope gradient below the platform was 1:1.75. The toe of the slope extended 0.675H to weaken the influence of the boundary range on the calculation results. Since the lower part of the solidified layer was a rock layer and the compression deformation could be ignored, the model boundary conditions were set as follows: XY-direction constraints were adopted on all sides, the top was free, and 3-direction constraints were adopted at the bottom. The constitutive model used was the Mohr-Coulomb model. A calculation model was established based on the above parameters as shown in Figure 2 .

[0126] The calculation parameters in Table 6 above were input into FLAC3D, and finite element calculation models based on three solidification construction methods were established in sequence (as shown in Figures 3 to 6 ). At the same time, for the shallow solidification double-layer foundation method and the shallow solidification + plain concrete pile composite foundation method, the pile spacing was determined by the user in combination with the actual site and local specifications. In this embodiment, the pile spacing of the plain concrete pile composite foundation method was 2 m, and the pile spacing of the cement mixing pile composite foundation method was 1.5 m.

[0127] Thus, the obtained model structure had symmetry, so a half-structure was taken for analysis. It was assumed that the 10-m embankment was filled in 4 times, that is, 2.5 m was filled each time, as shown in Figure 3 . Monitoring points were arranged at the center of the top of the subgrade, the upper interface of the solidified layer, the lower interface of the solidified layer, and at positions 16 m on both the left and right sides of the center line to monitor the settlement and earth pressure of the subgrade when filling 2.5 m, 5 m, 7.5 m, and 10 m.

[0128] The maximum subgrade settlement of the foundation under different filling heights under the conditions of three curing methods is calculated and returned to the improvement and curing device for deep soft foundation of high-fill subgrade for comparison and analysis. At the 10m position of the monitoring location, the maximum subgrade settlement values simulated by the unimproved and uncured foundation and the 7-day shallow curing double-layer foundation method are 34.60m and 30.05m respectively. According to the "Code for Design of Highway Subgrade", the post-construction settlement value of the soft soil subgrade should meet the requirements of Table 8. However, it is obvious that the post-construction settlement values of the unimproved and uncured foundation and the 7-day shallow curing double-layer foundation method are > 30cm, which do not meet the specification requirements. While the shallow curing + plain concrete pile composite foundation method and the shallow curing + cement mixing pile composite foundation method both meet the requirements. Considering the project cost, the shallow curing + plain concrete pile composite foundation method is finally determined for this area.

[0129] In this step, the construction method of the second prediction model is similar to that of the first prediction model, including:

[0130] Obtain the curing performance information, which includes soil parameters, confining pressure, the compressive strength and elastic modulus of the soil in the early and late stages of curing;

[0131] Taking the soil parameters and confining pressure as inputs, and the compressive strength and elastic modulus of the soil as outputs, construct a prediction model and evaluate the prediction accuracy of the prediction model;

[0132] If the prediction accuracy of the prediction model is lower than the preset expected accuracy, record the architecture of the prediction model, reconstruct the prediction model using the curing performance information, and mask the recorded architecture until the prediction accuracy of the constructed prediction model reaches the expected accuracy to obtain the second prediction model.

[0133] Embodiment 2

[0134] The present application also provides a design system for the improvement and curing scheme of subgrade soil, including:

[0135] The first acquisition module is used to acquire soil curing data. Each piece of the soil curing data includes soil parameters, the curing agent formula used, and the compressive strength of the soil; expand the soil curing data through deep learning to obtain a curing agent information database;

[0136] The second acquisition module is used to acquire the soil parameters of the construction area, and screen in the curing agent information database according to the soil parameters to obtain the basic formula and the corresponding strength data. The basic formula includes the raw materials in the formula and the corresponding contents, and the strength data includes test strength data and predicted strength data;

[0137] The construction module is used to construct an initial relationship equation based on the basic formula, with the soil curing strength as the response value and the content of each raw material as the influencing factor;

[0138] A design module, configured to perform coded level design of influencing factors through central composite design based on the basic formula and obtain corresponding true values, where the true values represent the contents of various raw materials, and find corresponding strength data according to the true values;

[0139] A fitting module, configured to fit an initial relationship equation according to the true values and strength data and perform variance analysis, and perform elimination processing on the terms in the initial relationship equation based on the variance analysis results to obtain an early strength equation and a late strength equation;

[0140] A first calculation module, configured to take the maximization of the soil solidification strength as the goal, find the intersection of the solutions of the early strength equation and the late strength equation to obtain a target formula, and obtain a modified solidification scheme according to the target formula.

[0141] As an optional implementation manner, the first acquisition module includes:

[0142] A classification unit, configured to traverse the soil solidification data, classify the data with the same soil parameters and the same curing agent raw materials, and obtain a soil solidification data packet for each type of soil;

[0143] A first construction unit, configured to perform deep learning based on each soil solidification data packet, use the soil parameters and the curing agent formula as inputs, and use the soil compressive strength as an output to construct a first prediction model;

[0144] An expansion unit, configured to multiply the soil parameters and the curing agent formula by different preset coefficients to obtain expansion parameters, and perform permutation and combination on the expansion parameters in sequence to obtain an expansion parameter group, and input the expansion parameter group into the first prediction model to obtain a predicted soil compressive strength;

[0145] A second construction unit, configured to store all the expansion parameter groups and the corresponding predicted soil compressive strengths, and construct a curing agent information database.

[0146] As an optional implementation manner, the second acquisition module includes:

[0147] A first acquisition unit, configured to acquire material information within a preset range of a construction area and determine priority raw materials;

[0148] A first screening unit, configured to screen in the curing agent information database based on the soil parameters to obtain initially screened curing agent formula data;

[0149] A second screening unit, configured to screen out the curing agent formula data including the priority raw materials from the initially screened curing agent formula data, and then screen the curing agent formula data according to the corresponding soil compressive strength to obtain a basic formula and the corresponding strength data.

[0150] As an alternative implementation manner, it further includes:

[0151] A prediction module, configured to obtain curing performance information based on the target formula through a pre-established second prediction model, where the curing performance information includes the early curing strength, early elastic modulus, late curing strength, and late elastic modulus of the soil mass under different confining pressure conditions;

[0152] A second calculation module, configured to obtain the cohesion parameter and internal friction angle parameter of the soil mass after curing based on the curing performance information;

[0153] A simulation module, configured to construct a three-dimensional model of the foundation of the construction area based on the curing performance information, cohesion parameter, and internal friction angle parameter, and obtain the settlement displacement of the foundation under different curing construction methods through numerical simulation, and determine the target curing construction method based on the settlement displacement;

[0154] A generation module, configured to obtain an improved curing plan according to the target formula and the target curing construction method.

[0155] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A design method for the improvement and solidification scheme of subgrade soil, characterized in that, Including: Obtain soil solidification data, each piece of the soil solidification data including soil parameters, the curing agent formula used, and the soil compressive strength; Expand the soil solidification data through deep learning to obtain a curing agent information database; Obtain the soil parameters of the construction area, and screen in the curing agent information database according to the soil parameters to obtain a basic formula and corresponding strength data, the basic formula including the raw materials in the formula and their corresponding contents, and the strength data including test strength data and predicted strength data; Based on the basic formula, with the soil solidification strength as the response value and the contents of each raw material as influencing factors, construct an initial relationship equation; Based on the basic formula, conduct an encoding level design of the influencing factors through central composite design and obtain corresponding true values, the true values representing the contents of each raw material, and look up the corresponding strength data according to the true values; Fit the initial relationship equation according to the true values and strength data and conduct variance analysis, and perform elimination processing on the terms in the initial relationship equation based on the variance analysis results to obtain an early strength equation and a late strength equation; With the maximization of the soil solidification strength as the goal, find the intersection of the solutions of the early strength equation and the late strength equation to obtain a target formula, and obtain an improved solidification plan according to the target formula.

2. The design method for the improvement and solidification scheme of the subgrade soil mass according to claim 1, characterized in that, The expanding the soil solidification data through deep learning to obtain a curing agent information database includes: Traverse the soil solidification data, classify the data with the same soil parameters and the same curing agent raw materials to obtain a soil solidification data packet for each type of soil; Based on each soil solidification data packet, conduct deep learning, with the soil parameters and the curing agent formula as inputs and the soil compressive strength as the output, to construct a first prediction model; Multiply the soil parameters and the curing agent formula by different preset coefficients to obtain expansion parameters, and arrange and combine the expansion parameters in sequence to obtain an expansion parameter group, and input the expansion parameter group into the first prediction model to obtain the predicted soil compressive strength; Store all the expansion parameter groups and the corresponding predicted soil compressive strengths to construct a curing agent information database.

3. The design method for the improvement and solidification scheme of the subgrade soil mass according to claim 1, characterized in that, The obtaining the soil parameters of the construction area and screening in the curing agent information database according to the soil parameters to obtain a basic formula and corresponding strength data includes: Obtain the material information within a preset range of the construction area and determine the priority raw materials; Screen in the curing agent information database based on the soil parameters to obtain initially screened curing agent formula data; Screen out the curing agent formula data including the priority raw materials from the initially screened curing agent formula data, and then screen the curing agent formula data according to the corresponding soil compressive strength to obtain a basic formula and corresponding strength data.

4. The design method for the improvement and solidification scheme of subgrade soil mass according to claim 1, characterized in that, The method further includes: Based on the target formula, obtain curing performance information through a pre-established second prediction model, the curing performance information including the early curing strength, early elastic modulus, late curing strength, and late elastic modulus of the soil under different confining pressure conditions; Obtain the cohesion parameter and internal friction angle parameter of the soil after solidification based on the curing performance information; Based on the curing performance information, cohesion parameters, and internal friction angle parameters, a three-dimensional model of the foundation in the construction area is constructed, and the settlement displacement of the foundation under different curing construction methods is obtained through numerical simulation. Based on the settlement displacement, the target curing construction method is determined; According to the target formula and the target curing construction method, an improved curing plan is obtained.

5. The method for designing the improvement and solidification scheme of the subgrade soil mass according to claim 4, characterized in that, The construction method of the second prediction model includes: Obtain curing performance information, which includes soil parameters, confining pressure, soil compressive strength, and soil elastic modulus in the early and late stages of curing; Using soil parameters and confining pressure as inputs, and soil compressive strength and soil elastic modulus as outputs, a prediction model is constructed and the prediction accuracy of the prediction model is evaluated; If the prediction accuracy of the prediction model is lower than the preset expected accuracy, record the architecture of the prediction model, reconstruct the prediction model using the curing performance information again, and mask the recorded architecture until the prediction accuracy of the constructed prediction model reaches the expected accuracy to obtain the second prediction model.

6. The design method for the improvement and solidification scheme of the subgrade soil mass according to claim 1, characterized in that, Based on the basic formula, the coding levels of influencing factors are designed through central composite design and the corresponding true values are obtained, including: Determine the number of influencing factors for central composite design according to the number of types of raw materials in the basic formula; Determine the central value, high value, and low value of each influencing factor according to the content range of each raw material in the basic formula; Determine the step size of the level change of each influencing factor according to the preset coding level and the high and low values of the influencing factor; Calculate the true value corresponding to the coding level according to the coding level, step size of the level change, and central value of each influencing factor.

7. An improved solidification scheme design system for subgrade soil, characterized in that, Including: The first acquisition module is used to acquire soil curing data. Each piece of soil curing data includes soil parameters, the curing agent formula used, and soil compressive strength; Expand the soil curing data through deep learning to obtain a curing agent information database; The second acquisition module is used to acquire the soil parameters of the construction area and screen in the curing agent information database according to the soil parameters to obtain the basic formula and the corresponding strength data. The basic formula includes the raw materials in the formula and their corresponding contents, and the strength data includes experimental strength data and predicted strength data; The construction module is used to construct an initial relationship equation based on the basic formula, with the soil curing strength as the response value and the contents of each raw material as the influencing factors; The design module is used to design the coding levels of the influencing factors through central composite design based on the basic formula and obtain the corresponding true values. The true values represent the contents of each raw material, and find the corresponding strength data according to the true values; The fitting module is used to fit the initial relationship equation according to the true values and strength data and perform variance analysis, and perform elimination processing on the terms in the initial relationship equation based on the variance analysis results to obtain the early strength equation and the late strength equation; The first calculation module is used to take the maximization of the soil curing strength as the goal, find the intersection of the solutions of the early strength equation and the late strength equation to obtain the target formula, and obtain an improved curing plan according to the target formula.

8. The improved solidification scheme design system for subgrade soil mass according to claim 7, characterized in that, The first acquisition module includes: A classification unit for traversing the soil solidification data, classifying the data with the same soil parameters and the same curing agent raw materials, and obtaining the soil solidification data packet for each type of soil; A first construction unit for performing deep learning based on each soil solidification data packet, using the soil parameters and the curing agent formula as inputs and the soil compressive strength as the output, and constructing a first prediction model; An expansion unit for multiplying the soil parameters and the curing agent formula by different preset coefficients to obtain expansion parameters, arranging and combining the expansion parameters in sequence to obtain an expansion parameter group, and inputting the expansion parameter group into the first prediction model to obtain the predicted soil compressive strength; A second construction unit for storing all the expansion parameter groups and the corresponding predicted soil compressive strengths and constructing a curing agent information database; 9. The design system for the improvement and solidification scheme of subgrade soil mass according to claim 7, characterized in that, The second acquisition module includes: A first acquisition unit for acquiring the material information within a preset range of the construction area and determining the priority raw materials; A first screening unit for screening based on the soil parameters in the curing agent information database to obtain the initially screened curing agent formula data; A second screening unit for screening out the curing agent formula data including the priority raw materials from the initially screened curing agent formula data, and then screening the curing agent formula data according to the corresponding soil compressive strength to obtain the basic formula and the corresponding strength data; 10. The design system for the improvement and solidification scheme of subgrade soil mass according to claim 7, characterized in that, It further includes: A prediction module for obtaining the curing performance information based on the target formula through a pre-established second prediction model, where the curing performance information includes the early curing strength, early elastic modulus, late curing strength, and late elastic modulus of the soil under different confining pressure conditions; A second calculation module for obtaining the cohesion parameter and internal friction angle parameter of the soil after solidification based on the curing performance information; A simulation module for constructing a three-dimensional model of the foundation of the construction area based on the curing performance information, cohesion parameter, and internal friction angle parameter, and obtaining the settlement displacement of the foundation under different curing construction methods through numerical simulation, and determining the target curing construction method based on the settlement displacement; A generation module for obtaining the improved curing scheme according to the target formula and the target curing construction method.

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