Roadbed vehicle-mounted modulus detection method and system based on continuous compaction technology

Abstract

The invention provides a roadbed vehicle-mounted modulus detection method and system based on a continuous compaction technology, and the method comprises the steps: obtaining first stress characteristics of different preset depths of a roadbed under a vibration real load based on the continuous compaction technology; based on finite element numerical simulation, obtaining second stress characteristics of different preset depths of the roadbed under the vehicle load; based on the first stress feature and the second stress feature, a cyclic dynamic triaxial experiment is carried out, and a roadbed modulus constitutive equation is obtained; and completing continuous detection of the vehicle-mounted modulus of the roadbed based on the constitutive equation of the roadbed modulus. According to the technical scheme, the roadbed vehicle-mounted modulus can be continuously detected in real time in the vibration compaction process, and the roadbed modulus detection efficiency and accuracy are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of subgrade engineering, and particularly relates to a method and system for detecting the vehicle-borne modulus of a subgrade based on continuous compaction technology. Background Art

[0002] Compared with the compaction degree, the resilient modulus of the subgrade can more reasonably reflect the anti-deformation ability of the structure. However, the subgrade modulus has a significant strain dependence, that is, the elastic modulus it exhibits is highly correlated with the strain state it is in. In road engineering, the resilient modulus under the action of vehicle loads, namely the vehicle-borne modulus, is more in line with the state of the subgrade during service and can better reflect the true bearing performance and mechanical characteristics of the subgrade during road operation. However, due to the limitations of many factors such as on-site detection equipment and costs, it is difficult to directly use the vehicle-borne modulus of the subgrade as an acceptance index.

[0003] On the other hand, in continuous compaction technology, the vibration modulus calculated using the vibration wheel response signal characterizes the mechanical properties of the subgrade under vibration compaction loads, and its calculation principle is similar to that of the vehicle-borne modulus of the subgrade. If continuous detection of the vehicle-borne modulus of the subgrade can be carried out through continuous compaction technology, it can not only meet the on-site test requirements of dynamic mechanical design parameters for the new generation of subgrade design methods, but also give play to the advantages of digital and full-domain detection of continuous compaction technology.

[0004] Due to the strong non-linear characteristics of the strain-dependent behavior of the subgrade modulus and the many influencing factors, the vibration modulus and the vehicle-borne modulus do not obey a simple proportional relationship. If continuous detection of the vehicle-borne modulus of the subgrade is to be achieved, it is necessary to comprehensively consider the above factors and propose a method and system for detecting the vehicle-borne modulus of the subgrade based on continuous compaction technology.

[0005] At the present stage, the research in the field of continuous compaction focuses on the continuous detection of compaction degree and the detection devices and platforms for continuous compaction, and no relevant technologies for continuous detection of the vehicle-borne modulus of the subgrade have been published. Summary of the Invention

[0006] In order to solve the problems existing in the prior art, the present invention provides a method and system for detecting the vehicle-borne modulus of a subgrade based on continuous compaction technology, which continuously and real-timely detects the vehicle-borne modulus of the subgrade during the vibration compaction process, greatly improving the detection efficiency and accuracy of the subgrade modulus.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A method for detecting the vehicle-borne modulus of a subgrade based on continuous compaction technology, the method comprising:

[0009] Based on continuous compaction technology, obtaining the first stress characteristics of the subgrade at different preset depths under vibration compaction loads;

[0010] Based on finite element numerical simulation, obtain the second stress characteristics at different preset depths of the subgrade under vehicle loads;

[0011] Based on the first stress characteristics and the second stress characteristics, conduct cyclic dynamic triaxial tests to obtain the constitutive equation of the subgrade modulus;

[0012] Based on the constitutive equation of the subgrade modulus, complete the continuous detection of the vehicle-borne modulus of the subgrade.

[0013] Preferably, both the first stress characteristics and the second stress characteristics include the stress amplitude along the depth direction of the subgrade, the stress amplitude along the driving direction, and the stress amplitude along the direction perpendicular to the driving direction.

[0014] Preferably, the method for obtaining the constitutive equation of the subgrade modulus includes:

[0015] Based on the first stress characteristics and the second stress characteristics, conduct cyclic dynamic triaxial tests to obtain the bulk modulus of the subgrade under different load levels;

[0016] Based on the bulk modulus of the subgrade under different load levels and taking stress as the characterization parameter, fit the initial constitutive equation of the subgrade modulus;

[0017] Establish the relationship between the soil influence variables and the fitting parameters of the initial constitutive equation of the path modulus to obtain the constitutive equation of the path modulus under different soil influence variable conditions; wherein, the soil influence variables include water content, gradation, confining pressure, density, and loading frequency.

[0018] Preferably, the method for continuously detecting the vehicle-borne modulus of the subgrade includes:

[0019] Based on the constitutive equation of the path modulus under different soil influence variable conditions, establish a subgrade-vibrating wheel simulation model and calculate the vibrating modulus of the subgrade based on the subgrade-vibrating wheel simulation model;

[0020] Based on the constitutive equation of the path modulus under different soil influence variable conditions, establish a subgrade simulation model under vehicle loads and obtain the vehicle-borne modulus of the subgrade based on the subgrade simulation model;

[0021] Based on the vibrating modulus of the subgrade and the vehicle-borne modulus of the subgrade, establish a vehicle-borne modulus prediction model and conduct continuous detection of the vehicle-borne modulus of the subgrade based on the vehicle-borne modulus prediction model.

[0022] Preferably, the method for establishing the vehicle-borne modulus prediction model includes:

[0023] Taking the subgrade vibration modulus and other characteristics as input features and the vehicle load modulus of the subgrade as the output index, a vehicle load modulus prediction database is established; wherein, the other characteristics include vibration compaction frequency, vibration compaction amplitude, soil moisture content, roller travel speed, vertical vibration acceleration of the vibration wheel and derivative indexes;

[0024] Based on the vehicle load modulus prediction database, a conversion relationship between the subgrade vibration modulus and the vehicle load modulus of the subgrade is established by using a deep learning algorithm;

[0025] Based on the conversion relationship between the subgrade vibration modulus and the vehicle load modulus of the subgrade, the vehicle load modulus prediction model is obtained.

[0026] The present invention also provides a vehicle load modulus detection system for subgrade based on continuous compaction technology for implementing the method, including:

[0027] A first stress characteristic acquisition module for acquiring the first stress characteristics of the subgrade at different preset depths under vibration compaction load based on continuous compaction technology;

[0028] A second stress characteristic acquisition module for acquiring the second stress characteristics of the subgrade at different preset depths under vehicle load based on finite element numerical simulation;

[0029] A constitutive equation construction module for performing cyclic dynamic triaxial tests based on the first stress characteristics and the second stress characteristics to obtain a subgrade modulus constitutive equation;

[0030] A detection module for completing continuous detection of the vehicle load modulus of the subgrade based on the subgrade modulus constitutive equation.

[0031] Preferably, the constitutive equation construction module includes:

[0032] A triaxial test unit for performing cyclic dynamic triaxial tests based on the first stress characteristics and the second stress characteristics to obtain the subgrade body modulus at different load levels;

[0033] An initial equation fitting unit for fitting an initial subgrade modulus constitutive equation based on the subgrade body modulus at different load levels and taking stress as a characterization parameter;

[0034] A constitutive equation acquisition unit for establishing the relationship between the soil influence variables and the fitting parameters of the initial path modulus constitutive equation to obtain the path modulus constitutive equation under different soil influence variable conditions; wherein, the soil influence variables include moisture content, gradation, confining pressure, density and loading frequency.

[0035] Preferably, the detection module includes:

[0036] The subgrade vibration modulus acquisition unit is used to establish a subgrade-vibrating wheel simulation model based on the path modulus constitutive equation under different soil influence variable conditions, and calculate the subgrade vibration modulus based on the subgrade-vibrating wheel simulation model;

[0037] The subgrade vehicle-borne modulus acquisition unit is used to establish a subgrade simulation model under vehicle load based on the path modulus constitutive equation under different soil influence variable conditions, and obtain the subgrade vehicle-borne modulus based on the subgrade simulation model;

[0038] The vehicle-borne modulus prediction model construction unit is used to establish a vehicle-borne modulus prediction model based on the subgrade vibration modulus and the subgrade vehicle-borne modulus, and continuously detect the subgrade vehicle-borne modulus based on the vehicle-borne modulus prediction model.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a method for continuously detecting the subgrade vehicle-borne modulus based on the continuous compaction technology, which can continuously and real-time detect the subgrade vehicle-borne modulus during the vibration compaction process. At present, the compaction degree is used as the evaluation index for the compaction quality of the subgrade. Even if the continuous compaction technology is adopted, only the fitting relationship between the continuous compaction index and the compaction degree is established. However, compared with the compaction degree, the subgrade modulus can better reflect the anti-deformation ability of the subgrade, and it is often used as the design index of the road. Using the traditional method to detect the subgrade modulus has a series of problems such as shortage of equipment and high time and economic costs. Therefore, based on the continuous compaction technology, the present invention realizes the continuous detection of the subgrade vehicle-borne modulus by establishing the conversion relationship between the subgrade vibration modulus and the subgrade vehicle-borne modulus, and can continuously and real-time detect the subgrade vehicle-borne modulus during the vibration compaction process, greatly improving the detection efficiency and accuracy of the subgrade modulus. Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a flowchart of the method for detecting the subgrade vehicle-borne modulus based on the continuous compaction technology according to the embodiment of the present invention;

[0042] Figure 2 It is a schematic diagram of the subgrade-vibrating wheel simulation model according to the embodiment of the present invention;

[0043] Figure 3 It is the subgrade simulation model under vehicle load according to the embodiment of the present invention. Detailed Embodiments

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] The following explanations are made for the terms appearing in the present invention:

[0047] The cyclic dynamic triaxial test is a laboratory test method used to study the mechanical behavior of soil under dynamic loads. This test is usually used to simulate the effects of periodic dynamic actions such as earthquakes and traffic loads on soil. During the test, the specimen is placed in a triaxial pressure chamber and subjected to axial cyclic loads, and at the same time, hydrostatic pressure (confining pressure) can be applied to simulate the influence of formation depth. The main purposes of the test are: 1. Evaluate the dynamic characteristics of soil: such as dynamic elastic modulus, damping ratio, etc., which are very important for predicting the response of soil under dynamic actions such as earthquakes. 2. Study the liquefaction potential of soil: By changing the test conditions (such as loading frequency, amplitude, confining pressure, etc.), the liquefaction tendency of soil under different conditions can be evaluated. 3. Analyze the cumulative deformation of soil: Understand the change of soil over time under cyclic loads, which is crucial for predicting long-term engineering performance.

[0048] Embodiment 1

[0049] As Figure 1 shown, a method for detecting the modulus of a roadbed vehicle based on continuous compaction technology, the method includes:

[0050] S1: Based on continuous compaction technology, obtain the first stress characteristics of the roadbed at different preset depths under vibration compaction loads; in this embodiment, the optimal solution is to obtain the stress characteristics of the roadbed at different depths under vibration compaction by burying triaxial earth pressure gauges in the roadbed soil. An alternative solution is to obtain the stress characteristics of the roadbed at different depths under vibration compaction through research on relevant materials. The relevant materials include, but are not limited to, other engineering experiences in the area where the present invention is applied, instruction texts of the compaction equipment used, and research literatures with similar working conditions.

[0051] S2: Based on finite element numerical simulation, obtain the second stress characteristics of the subgrade at different preset depths under vehicle load. In this embodiment, the vehicle load is preferably the standard axle load BZZ-100. The optimal solution is to use finite element numerical simulation software for calculation to obtain the stress characteristics of the subgrade at different depths under vehicle load. An alternative solution is to use BISAR software for calculation to obtain the stress characteristics of the subgrade at different depths under vehicle load; another alternative solution is to obtain the stress characteristics of the subgrade at different depths under vehicle load through research on relevant materials. The relevant materials include, but are not limited to, other engineering experiences in the area where the present invention is applied and research literatures with consistent or similar working conditions.

[0052] A further implementation manner is that both the first stress characteristics and the second stress characteristics include the stress amplitude along the depth direction of the subgrade, the stress amplitude along the driving direction, and the stress amplitude along the direction perpendicular to the driving direction.

[0053] S3: Based on the first stress characteristics and the second stress characteristics, conduct cyclic dynamic triaxial tests to obtain the constitutive equation of the subgrade modulus. A further implementation manner is that the method for obtaining the constitutive equation of the subgrade modulus includes:

[0054] S31: Based on the first stress characteristics and the second stress characteristics, conduct cyclic dynamic triaxial tests to obtain the bulk modulus of the subgrade body at different load levels;

[0055] S32: Based on the bulk modulus of the subgrade body at different load levels and using stress as the characterization parameter, fit the initial constitutive equation of the subgrade modulus; In this embodiment, through cyclic dynamic triaxial tests, obtain the modulus of the subgrade soil at different load levels and use strain as the characterization parameter to fit the constitutive equation of the subgrade modulus; The constitutive equation of the subgrade modulus is the Kondner equation or the Hardin equation or the Davidenkov equation or the Ramberg-Osgood equation. An alternative solution is to obtain the modulus of the subgrade soil at different load levels through cyclic dynamic triaxial tests and use stress as the characterization parameter to fit the constitutive equation of the subgrade modulus; The constitutive equation of the subgrade modulus is the Uzan equation or the NCHRP-28A three-parameter equation.

[0056] The optional constitutive equations of the subgrade modulus in the present invention include:

[0057]

[0058] In the formula, E is the elastic modulus of the soil, a and b are the equation fitting parameters, and ε is the axial strain. Among them, 1 / a is the slope of the tangent line of the soil stress-strain curve at the origin, that is, the maximum elastic modulus E max ; 1 / b is the intercept of the horizontal asymptote of the soil stress-strain curve and the stress axis.

[0059]

[0060] In the formula, G is the shear modulus of the soil mass; G max is the maximum shear modulus, which can be calculated through E in the Kondner equation max ; γ is the shear strain; γ ref is the reference shear strain, which is the strain corresponding to the intersection point of the tangent line passing through the origin and the horizontal asymptote in the stress-strain curve of the soil mass.

[0061]

[0062] In the formula, G max is the maximum shear modulus; γ is the shear strain; γ 0 is the nominal shear strain, which can be used as a model parameter and fitted by the test results; A and B are respectively the model fitting parameters.

[0063]

[0064] In the formula, G max is the maximum shear modulus; f(τ) is the characterization equation of the nonlinear degree of the soil mass under dynamic action.

[0065]

[0066] In the formula, M r is the resilient modulus of the soil mass; θ is the volumetric stress of the soil mass; σ d is the deviator stress of the soil mass; k 1 、k 2 、k 3 are the equation fitting parameters.

[0067]

[0068] In the formula, M r is the resilient modulus of the soil mass; θ is the volumetric stress of the soil mass; τ oct is the octahedral shear stress of the soil mass; p a is the atmospheric pressure; k 1 、k 2 、k 3 are the equation fitting parameters.

[0069] S33: Establish the relationship between the soil influencing variables and the fitting parameters of the constitutive equation of the initial path modulus, and obtain the constitutive equation of the path modulus under different working conditions of the soil influencing variables. Among them, the soil influencing variables include water content, gradation, confining pressure, density, and loading frequency. In particular, by the test results, the relationship between the soil influencing variables and the fitting parameters of the constitutive equation of the subgrade modulus is established, and the physical meaning of the fitting parameters of the constitutive equation is given in the form of an empirical formula. Its advantage is that the constitutive equation can be deduced through the conventional physical properties of the soil (such as water content, gradation, etc.), and there is no need to conduct cyclic dynamic triaxial tests anymore.

[0070] S4: Based on the constitutive equation of the subgrade modulus, complete the continuous detection of the vehicle-borne modulus of the subgrade.

[0071] A further implementation method is that the method for continuous detection of the vehicle-borne modulus of the subgrade includes:

[0072] S41: Based on the constitutive equation of the path modulus under different working conditions of the soil influencing variables, establish a subgrade-vibrating wheel simulation model, and calculate the vibration modulus of the subgrade based on the subgrade-vibrating wheel simulation model. Specifically, as Figure 2 shown, the subgrade-vibrating wheel simulation model is established through the ABAQUS platform or the ANSYS platform; the subgrade-vibrating wheel simulation model includes a vibrating wheel model and a subgrade model. The vibrating wheel model can be regarded as a rigid body material, and the material constitutive equation of the subgrade model needs to be input through user-defined materials developed by secondary development. In particular, in step S41 of this embodiment, when establishing the subgrade-vibrating wheel simulation model, the constitutive of the non-linear dynamic modulus is innovatively used for the subgrade, rather than the linear elastic-plastic constitutive used in existing research. Its advantage is that it is more in line with the actual situation that the modulus of the subgrade soil will have a large difference under different levels of load, while the linear elastic-plastic model completely does not conform to this law.

[0073] Calculation method of the subgrade vibration modulus:

[0074]

[0075] In the formula, v is the Poisson's ratio of the soil; b is the contact width between the vibrating wheel and the soil (along the traveling direction of the roller), which can be solved by the Lundberg equation; L is the width of the rolling wheel (perpendicular to the traveling direction of the roller); R is the radius of the vibrating wheel of the roller; F s is the contact force between the vibrating wheel and the soil, F s = F 0 cosωt, F 0 is the amplitude of the excitation force of the roller, ω is the angle between the excitation force and the vertical direction (measured in real time); z d is the soil deformation, which can be obtained by double integration of the measured vertical vibration acceleration of the vibrating wheel of the roller.

[0076] S42: Based on the path modulus constitutive equation under different soil influence variable conditions, establish a subgrade simulation model under vehicle load, and obtain the subgrade vehicle load modulus based on the subgrade simulation model; as Figure 3 shown, specifically, the subgrade simulation model under vehicle load is established through the ABAQUS platform or the ANSYS platform; the material constitutive equation of the subgrade model and the movable vehicle load are input through user-defined materials developed through secondary development; through the user-defined subroutine of the ABAQUS platform or the ANSYS platform, the subgrade vehicle load modulus is extracted. Specifically, the method for extracting the subgrade vehicle load modulus includes: 1. In the user-defined subroutine, the software automatically calculates the strain; 2. Calculate the stress in the current state through the stiffness matrix of the material and the automatically calculated strain; 3. Divide the stress by the strain to obtain the subgrade vehicle load modulus in the user-defined subroutine and use it as the output variable; 4. In the analysis step of the ABAQUS or ANSYS main program, set the subgrade vehicle load modulus in the subroutine as the output variable.

[0077] S43: Establish a vehicle load modulus prediction model based on the subgrade vibration modulus and the subgrade vehicle load modulus, and conduct continuous detection of the subgrade vehicle load modulus based on the vehicle load modulus prediction model.

[0078] A further implementation method is that the method for establishing the vehicle load modulus prediction model includes:

[0079] Take the subgrade vibration modulus and other characteristics as input features, and take the subgrade vehicle load modulus as the output index to establish a vehicle load modulus prediction database, and the data volume in the vehicle load modulus prediction database is more than 2000; among them, other characteristics include vibration compaction frequency, vibration compaction amplitude, soil moisture content, roller driving speed, vertical vibration acceleration of the vibration wheel and derivative indexes;

[0080] Based on the vehicle load modulus prediction database, use a deep learning algorithm to establish the conversion relationship between the subgrade vibration modulus and the subgrade vehicle load modulus; in this embodiment, the deep learning algorithm includes artificial neural network, random forest and other regression algorithms. When establishing, 80% of the samples in the vehicle load modulus prediction database are used as the training set, and the remaining 20% of the samples are used as the verification set; using the conversion relationship, the average error between the subgrade vehicle load modulus predicted by the input features of the verification set and the true value of the subgrade vehicle load modulus of the verification set is not greater than 15%, otherwise it is necessary to adjust the hyperparameters of the deep learning algorithm and retrain.

[0081] The ANN (Artificial Neural Network) model used in this embodiment is constructed based on Pytorch 1.10.2, and its architecture is input layer → hidden layer 1 → hidden layer 2 → output layer. The input layer contains 4 eigenvalue, namely vibration modulus, subgrade moisture content, vibratory excitation frequency of roller, and amplitude value of roller vibration; hidden layer 1 contains 32 neurons, and hidden layer 2 contains 128 neurons; the output layer is the vehicle load modulus of subgrade.

[0082] When the data is output from the first and second hidden layers, it will pass through an activation function ReLu, which is used to establish a non-linear relationship between the input and output and reduce the problem of gradient disappearance in training. The formula of the activation function is as follows:

[0083] ReLu(x) = max(0, x)

[0084] Design the loss function L(θ) of the model, which is used to measure the predicted value y i,pre and the true value y i The error between them. At the same time, the Adaptive Moment Estimation (Adam) algorithm is used as the optimization algorithm to accelerate the convergence speed of the model.

[0085]

[0086] The Root Mean Square Error (RMSE) is used to evaluate the prediction results of the random forest, and the calculation equation is shown as follows. The smaller the RMSE, the closer the prediction result of the model is to the true value, and the better the performance of the model.

[0087]

[0088] In the formula, n is the number of samples in the test set; y i is a single true value on the test set; y i,pre is a single predicted value output by the random forest model.

[0089] In this embodiment, when using the random forest algorithm, a single CART algorithm decision tree is established, and the mean value of the output results of all decision trees is used as the final value, that is:

[0090]

[0091] In the formula, f t (x) is the regression result of the t-th decision tree; T is the number of decision trees in the random forest.

[0092] In the training process of the random forest, GridsearchCV is used to optimize the hyperparameters of the model grid by grid, and RMSE is also used as the evaluation index of the model.

[0093] Based on the conversion relationship between the subgrade vibration modulus and the vehicle load modulus of the subgrade, a vehicle load modulus prediction model is obtained.

[0094] Specifically, the target value (predicted value) of the vehicle load modulus prediction database is the modulus under vehicle load, rather than the compaction degree or PFWD modulus commonly found in existing research. Its advantage is that it is the most concerned index during the actual use of the subgrade. However, this modulus is not easy to detect, and it is even more difficult to obtain a large amount of sample data for training. The present invention reasonably obtains a large amount of data of this modulus through the model in step S42. Both the vibration modulus and the vehicle load modulus in the constructed vehicle load modulus prediction database are actually extracted under the same constitutive relationship, rather than the preset values considered in existing research. Its advantage is that the two can maintain good consistency and better conform to the actual situation. At the same time, in the field of continuous subgrade compaction detection, the database volume of this prediction model is huge (more than 6000), while the existing research is generally about 200 data, and its prediction result is more accurate.

[0095] Example 2

[0096] Based on the method described in Example 1, this embodiment provides two specific implementation process examples for detecting the vehicle load modulus of the subgrade:

[0097] Example 1:

[0098] Step 1: By means of burying earth pressure cells, obtain the three-dimensional stress amplitudes at different depths of the subgrade during the vibration compaction process. The maximum burial depth along the subgrade depth direction is 160 cm; the maximum burial depths along the driving direction and the direction perpendicular to the driving direction are 130 cm;

[0099] Step 2: Use the numerical simulation method to obtain the three-dimensional stress amplitudes at different depths of the subgrade under vehicle load;

[0100] Step 3: According to the obtained subgrade stress characteristics, design and implement a cyclic dynamic triaxial test. Use the Hardin constitutive equation to fit the results of the cyclic dynamic triaxial test to obtain the constitutive equation of the subgrade modulus.

[0101] Step 4: Conduct cyclic dynamic triaxial tests under different working conditions, and establish the relationships between soil moisture content, compaction degree, confining pressure, loading frequency and the parameters of the Hardin constitutive equation respectively. According to the established relationships, the Hardin equation of the subgrade soil under any variable within the test range can be obtained.

[0102] Step 5: Establish a subgrade-vibrating wheel simulation model. Among them, the subgrade is divided into an upper layer with a height of 1.5 m and a lower layer with a height of 3.5 m, and the constitutive equation of the modulus is introduced through a self-written program; the vibrating wheel is regarded as a rigid body; the specific model material parameters are shown in Table 1. Through the numerical simulation model, obtain the vibration response of the vibrating wheel and calculate the vibration modulus of the subgrade.

[0103] Table 1 Material properties of each material in the subgrade soil-vibrating wheel system simulation model

[0104]

[0105] Step 6: Adopt the same subgrade material properties and geometric dimensions, overlay an asphalt concrete surface layer, and establish a subgrade simulation model under vehicle load. The material parameters of the surface layer are shown in Table 2. Calculate the finite element model and extract the vehicle load modulus of the subgrade.

[0106] Table 2 Material properties of the surface layer in the simulation model under vehicle load

[0107]

[0108] Step 7: Batch-calculate the models in Step 5 and Step 6 to obtain 3000 groups of vibration modulus and vehicle load modulus data, and establish a data set of the vehicle load modulus of the subgrade. Use the neural network ANN algorithm to establish a prediction model of the vehicle load modulus.

[0109] In the project, use the established model to predict the vehicle load modulus of the subgrade, and compare it with the true value measured by the bearing plate method. The average relative error is about 14%, and the average absolute error is about 10 MPa.

[0110] Example 2:

[0111] Step 1: Use the method of burying earth pressure cells to obtain the three-dimensional stress amplitudes at different depths of the subgrade during the vibration compaction process. The maximum burial depth along the depth direction of the subgrade is 100 cm; the maximum burial depths along the driving direction and the direction perpendicular to the driving direction are 120 cm.

[0112] Step 2: Use BISAR software to obtain the three-dimensional stress amplitudes at different depths of the subgrade under vehicle load.

[0113] Step 3: Based on the obtained three-dimensional stress amplitudes of the subgrade, design and implement cyclic dynamic triaxial tests. Use the Hardin constitutive equation to fit the results of the cyclic dynamic triaxial tests to obtain the constitutive equation of the subgrade modulus.

[0114] Step 4: Conduct cyclic dynamic triaxial tests under different working conditions, and establish the relationships between soil moisture content, compaction degree, confining pressure, loading frequency and the parameters of the Hardin constitutive equation respectively. According to the established relationships, the Hardin equation of the subgrade soil under any variable within the test range can be obtained.

[0115] Step 5: Establish a subgrade-vibrating wheel simulation model (for the schematic diagram, see Figure 2 ). Among them, the subgrade is divided into an upper layer with a height of 1.5 m and a lower layer with a height of 3.5 m. The constitutive equation of the modulus is introduced through a self-written program; the vibrating wheel is regarded as a rigid body; the specific model material parameters are shown in Table 3. Calculate the finite element model, obtain the vibration response of the vibrating wheel, and deduce the vibration modulus of the subgrade;

[0116] Table 3 Material properties of each material in the subgrade soil-vibrating wheel system simulation model

[0117]

[0118] Step 6: Adopt the same subgrade material properties and geometric dimensions, overlay an asphalt concrete surface layer, and establish a subgrade simulation model under vehicle load. Calculate the finite element model and extract the vehicle-borne modulus of the subgrade.

[0119] Step 7: Batch-calculate the models in Step 5 and Step 6, obtain 6300 groups of vibration modulus and vehicle-borne modulus data, and establish a data set of the vehicle-borne modulus of the subgrade. Use the random forest model to establish a prediction model for the vehicle-borne modulus of the subgrade.

[0120] In the project, use the established model to predict the vehicle-borne modulus of the subgrade, and compare it with the true value measured by the bearing plate method. The average relative error is about 8.6%, and the average absolute error is about 8.5 MPa.

[0121] Example 3

[0122] The present invention also provides a subgrade vehicle-borne modulus detection system based on continuous compaction technology for implementing the method, including:

[0123] The first stress characteristic acquisition module is used to acquire the first stress characteristics of the subgrade at different preset depths under the vibration real load based on continuous compaction technology;

[0124] The second stress characteristic acquisition module is used to acquire the second stress characteristics of the subgrade at different preset depths under vehicle load based on finite element numerical simulation;

[0125] The constitutive equation construction module is used to perform cyclic dynamic triaxial experiments based on the first stress characteristics and the second stress characteristics to obtain the subgrade modulus constitutive equation;

[0126] The detection module is used to complete the continuous detection of the subgrade vehicle-borne modulus based on the subgrade modulus constitutive equation.

[0127] A further implementation manner is that the constitutive equation construction module includes:

[0128] A triaxial test unit, which is used to perform cyclic dynamic triaxial tests based on a first stress characteristic and a second stress characteristic, and obtain the subgrade bulk modulus under different load levels;

[0129] An initial equation fitting unit, which is used to fit an initial subgrade modulus constitutive equation based on the subgrade bulk modulus under different load levels and using stress as a characterization parameter;

[0130] A constitutive equation obtaining unit, which is used to establish the relationship between soil influence variables and the fitting parameters of the initial path modulus constitutive equation, and obtain the path modulus constitutive equation under different soil influence variable conditions; wherein, the soil influence variables include water content, gradation, confining pressure, density, and loading frequency.

[0131] A further implementation manner is that the detection module includes:

[0132] A subgrade vibration modulus obtaining unit, which is used to establish a subgrade-vibrating wheel simulation model based on the path modulus constitutive equation under different soil influence variable conditions, and calculate the subgrade vibration modulus based on the subgrade-vibrating wheel simulation model;

[0133] A subgrade vehicle load modulus obtaining unit, which is used to establish a subgrade simulation model under vehicle load based on the path modulus constitutive equation under different soil influence variable conditions, and obtain the subgrade vehicle load modulus based on the subgrade simulation model;

[0134] A vehicle load modulus prediction model constructing unit, which is used to establish a vehicle load modulus prediction model based on the subgrade vibration modulus and the subgrade vehicle load modulus, and continuously detect the subgrade vehicle load modulus based on the vehicle load modulus prediction model.

[0135] The above-described embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for detecting vehicle-mounted modulus of roadbed based on continuous compaction technology, characterized in that: The method comprises: Based on the continuous compaction technology, the first stress characteristics of the roadbed at different preset depths under the vibration real load are obtained; Based on finite element numerical simulation, the second stress characteristics of different preset depths of the roadbed under vehicle load are obtained; Based on the first stress characteristic and the second stress characteristic, a cyclic dynamic triaxial test is performed to obtain a constitutive equation of the roadbed modulus; Based on the roadbed modulus constitutive equation, continuous detection of the roadbed vehicle-mounted modulus is completed.

2. The method according to claim 1, characterized in that The first stress characteristic and the second stress characteristic both include a stress amplitude along a roadbed depth direction, a stress amplitude along a driving direction, and a stress amplitude along a direction perpendicular to driving.

3. The method according to claim 1, characterized in that The methods for obtaining the constitutive equation of the roadbed modulus include: Based on the first stress characteristic and the second stress characteristic, a cyclic dynamic triaxial test is performed to obtain the basic bulk modulus of the road under different load levels; Based on the bulk modulus of the roadbed under different load levels, and taking stress as a characterization parameter, the constitutive equation of the initial roadbed modulus is fitted. The relationship between the soil influencing variables and the fitting parameters of the initial path modulus constitutive equation is established to obtain the path modulus constitutive equation under different soil influencing variable conditions; among them, the soil influencing variables include moisture content, gradation, confining pressure, density and loading frequency.

4. The method according to claim 3, characterized in that Methods for continuous testing of the vehicle-mounted modulus of the roadbed include: Based on the path modulus constitutive equation under different soil influencing variable working conditions, a roadbed-vibration wheel simulation model is established, and the roadbed vibration modulus is calculated based on the roadbed-vibration wheel simulation model; Based on the path modulus constitutive equation under different soil influencing variable working conditions, a roadbed simulation model under vehicle load is established, and the roadbed vehicle load modulus is obtained based on the roadbed simulation model; A vehicle-mounted modulus estimation model is established based on the roadbed vibration modulus and the roadbed vehicle-mounted modulus, and continuous detection of the roadbed vehicle-mounted modulus is performed based on the vehicle-mounted modulus estimation model.

5. The method according to claim 4, characterized in that Methods for establishing a vehicle load modulus estimation model include: The roadbed vibration modulus and other characteristics are used as input characteristics, and the roadbed vehicle-mounted modulus is used as an output indicator to establish a vehicle-mounted modulus estimation database; wherein the other characteristics include vibration compaction frequency, vibration compaction amplitude, soil moisture content, roller travel speed, vibration wheel vertical vibration acceleration and derived indicators; Based on the vehicle-mounted modulus estimation database, a deep learning algorithm is used to establish a conversion relationship between the roadbed vibration modulus and the roadbed vehicle-mounted modulus; Based on the conversion relationship between the roadbed vibration modulus and the roadbed vehicle-mounted modulus, the vehicle-mounted modulus estimation model is obtained.

6. A roadbed vehicle-mounted modulus detection system based on continuous compaction technology, used to implement the method described in any one of claims 1 to 5, characterized in that: include: A first stress characteristic acquisition module, used for acquiring first stress characteristics of different preset depths of roadbed under vibration actual load based on continuous compaction technology; A second stress characteristic acquisition module, used for acquiring second stress characteristics of different preset depths of the roadbed under vehicle load based on finite element numerical simulation; A constitutive equation building module is used to perform a cyclic dynamic triaxial test based on the first stress characteristic and the second stress characteristic to obtain a constitutive equation of the roadbed modulus; The detection module is used to complete the continuous detection of the vehicle-mounted modulus of the roadbed based on the roadbed modulus constitutive equation.

7. The system according to claim 6, characterized in that The constitutive equation building module includes: A triaxial test unit is used to perform a cyclic dynamic triaxial test based on the first stress characteristic and the second stress characteristic to obtain a basic bulk modulus of the road under different load levels; The initial equation fitting unit is used to fit the initial roadbed modulus constitutive equation based on the roadbed bulk modulus under different load levels and taking stress as a characterization parameter; The constitutive equation acquisition unit is used to establish the relationship between the soil influencing variables and the initial path modulus constitutive equation fitting parameters, and obtain the path modulus constitutive equation under different soil influencing variable conditions; wherein the soil influencing variables include moisture content, gradation, confining pressure, density and loading frequency.

8. The system according to claim 6, characterized in that The detection module comprises: A roadbed vibration modulus acquisition unit is used to establish a roadbed-vibration wheel simulation model based on the path modulus constitutive equation under different soil influencing variable working conditions, and calculate the roadbed vibration modulus based on the roadbed-vibration wheel simulation model; A roadbed vehicle load modulus acquisition unit is used to establish a roadbed simulation model under vehicle load based on the path modulus constitutive equation under different soil influencing variable working conditions, and to acquire the roadbed vehicle load modulus based on the roadbed simulation model; The vehicle-mounted modulus prediction model building unit is used to establish a vehicle-mounted modulus prediction model based on the roadbed vibration modulus and the roadbed vehicle-mounted modulus, and to continuously detect the roadbed vehicle-mounted modulus based on the vehicle-mounted modulus prediction model.

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