Roadbed quality moisture content prediction method and system based on dielectric properties and compaction degree

Abstract

The present invention proposes a method and system for predicting the mass moisture content of a roadbed based on dielectric properties and compaction degree. By comprehensively considering the dielectric properties and compaction state of the soil, a prediction model for the relationship between the dielectric constant, the compaction degree of the roadbed and the mass moisture content of the roadbed is constructed, thereby accurately predicting the mass moisture content of different types of soil under different compaction conditions. The method has the advantages of high precision and strong adaptability. Key parameters are obtained through regression analysis fitting, which can be widely applied to different types of soil and various compaction conditions. In actual engineering applications, the model parameters can be adjusted according to the actual situation on site to ensure the accuracy and applicability of the measurement. This feature enables the method to show excellent prediction results in a variety of construction environments.

Description

Technical Field

[0001] The present invention belongs to the technical field related to roadbed mass moisture content detection, and in particular relates to a method and system for predicting roadbed mass moisture content based on dielectric properties and compaction degree. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In roadbed construction, soil moisture content is a significant factor affecting compaction, stability, and bearing capacity. Traditional moisture content measurement methods, such as drying and alcohol combustion, offer high measurement accuracy but are complex and time-consuming, often requiring destructive sampling and making it difficult to meet the demands of real-time monitoring during construction. With the development of non-contact measurement technology, moisture content measurement methods based on dielectric properties have gradually gained application. These methods utilize the relationship between soil moisture content and dielectric constant, indirectly determining moisture content by measuring the dielectric constant. However, these methods often establish a relationship between dielectric constant and volumetric water content (VWC), while mass water content (MWC) is often used in actual engineering applications.

[0004] Therefore, how to quickly and accurately measure the moisture content of the roadbed mass to solve the problems of long test time, large test error and low test accuracy in the existing technology for the moisture content of the roadbed mass is a technical problem that needs to be solved at present. Summary of the Invention

[0005] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method and system for predicting the mass moisture content of roadbed based on dielectric properties and compaction degree, which can accurately predict the mass moisture content of different types of soil under different compaction conditions, and has the advantages of high precision and strong adaptability.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree, comprising:

[0008] Constructing a first relationship based on the volume of the roadbed and the corresponding dielectric constant, and the volume of each phase of the roadbed and the corresponding dielectric constant of each phase;

[0009] According to the relationship between the porosity and mass moisture content of the roadbed and the specific gravity of soil particles, combined with the first relationship, the relationship between the dielectric constants of the roadbed and the mass moisture content of the roadbed is obtained;

[0010] Substituting the roadbed compaction degree into the relationship between the corresponding dielectric constant of the roadbed and the water content of the roadbed mass, the relationship between the corresponding dielectric constant, roadbed compaction degree and the water content of the roadbed mass is obtained;

[0011] Based on the test data of different roadbed types, the regression parameters in the relationship between the corresponding dielectric constant, roadbed compaction degree and roadbed mass moisture content were fitted to obtain the corresponding prediction model;

[0012] According to the prediction model of the roadbed type corresponding to the roadbed to be predicted, the roadbed mass moisture content of the roadbed to be predicted is predicted to obtain a prediction result.

[0013] In a second aspect, the present invention provides a system for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree, comprising:

[0014] A first construction module is configured to: construct a first relationship based on the roadbed volume and the corresponding dielectric constant, and the volume of each phase of the roadbed and the corresponding dielectric constant of each phase;

[0015] The second building block is configured to: obtain a relationship between the dielectric constant of each corresponding roadbed and the roadbed mass moisture content based on a relationship between the porosity and mass moisture content of the roadbed and the specific gravity of soil particles, in combination with the first relationship;

[0016] The third module is configured to: substitute the roadbed compaction degree into the relationship formula between the dielectric constant and the water content of the roadbed mass corresponding to each roadbed, to obtain the relationship formula between the dielectric constant, the roadbed compaction degree and the water content of the roadbed mass corresponding to each roadbed;

[0017] A prediction model building module is configured to: based on the test data of different roadbed types, fit the regression parameters in the relationship between the dielectric constant, roadbed compaction degree and roadbed mass moisture content of each corresponding roadbed type to obtain a corresponding prediction model;

[0018] The prediction module is configured to: predict the water content of the roadbed to be predicted according to the prediction model of the roadbed type to be predicted, and obtain a prediction result.

[0019] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0021] One or more of the above technical solutions have the following beneficial effects:

[0022] In the present invention, the dielectric properties and compaction state of the soil are comprehensively considered to construct a prediction model for the relationship between the dielectric constant, roadbed compaction degree, and roadbed mass moisture content. This allows for accurate prediction of the mass moisture content of different types of soil under different compaction conditions, with the advantages of high precision and strong adaptability. Key parameters are obtained through regression analysis and fitting, which can be widely applied to different types of soil and various compaction conditions. In actual engineering applications, the model parameters can be adjusted according to the actual situation on site to ensure the accuracy and applicability of the measurement. This feature enables this method to demonstrate excellent prediction results in a variety of construction environments.

[0023] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0025] Figure 1 Schematic diagram of the three-phase state of the roadbed mentioned in the first embodiment of the present invention;

[0026] Figure 2(a)-Figure 2(b) They are respectively the dielectric constant acquisition device used in the indoor test and the dielectric constant acquisition device used in the field test in the first embodiment of the present invention;

[0027] Figure 3 A schematic diagram of a site for on-site testing of GW soil provided in Example 1 of the present invention;

[0028] Figure 4(a)-Figure 4(b) These are the relationship diagrams between the dielectric constant and moisture content, and the dielectric constant and compaction degree of the CL soil in the indoor test provided in Example 1 of the present invention;

[0029] Figure 5(a)-Figure 5(b) These are the relationship diagrams between the dielectric constant and moisture content, and the dielectric constant and compaction degree of the GW soil in the field test provided in Example 1 of the present invention;

[0030] Figure 6 This is a graph showing the predicted moisture content and the actual moisture content of the semi-empirical model proposed in Example 1 of the present invention. DETAILED DESCRIPTION

[0031] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0032] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0033] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0034] Example 1

[0035] This embodiment discloses a method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree, including:

[0036] Constructing a first relationship based on the volume of the roadbed and the corresponding dielectric constant, and the volume of each phase of the roadbed and the corresponding dielectric constant of each phase;

[0037] According to the relationship between the porosity and mass moisture content of the roadbed and the specific gravity of soil particles, combined with the first relationship, the relationship between the dielectric constants of the roadbed and the mass moisture content of the roadbed is obtained;

[0038] Substituting the roadbed compaction degree into the relationship between the corresponding dielectric constant of the roadbed and the water content of the roadbed mass, the relationship between the corresponding dielectric constant, roadbed compaction degree and the water content of the roadbed mass is obtained;

[0039] Based on the test data of different roadbed types, the regression parameters in the relationship between the corresponding dielectric constant, roadbed compaction degree and roadbed mass moisture content were fitted to obtain the corresponding prediction model;

[0040] According to the prediction model of the roadbed type corresponding to the roadbed to be predicted, the roadbed mass moisture content of the roadbed to be predicted is predicted to obtain a prediction result.

[0041] This method comprehensively considers the dielectric properties and compaction state of soil, accurately predicting the mass moisture content of different soil types under different compaction conditions. This method, with its high accuracy and adaptability, provides a new technical solution for moisture control and quality monitoring during roadbed construction.

[0042] The method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree proposed in this embodiment is described in detail below.

[0043] Topp et al. found that the speed of electromagnetic waves passing through a medium is:

[0044]

[0045] Where v is the speed of electromagnetic waves through the medium, c is the speed of light, K' is the real part of the dielectric constant, and tan 2 δ is the electrical loss. Since the electrical loss is very small, it does not significantly change the propagation speed of electromagnetic waves in the soil, so: tan 2 δ≈0.

[0046] Therefore, from formula (1) we can get:

[0047]

[0048] The measured dielectric constant is called the apparent dielectric constant ε. For simplicity, the apparent dielectric constant is referred to as the dielectric constant. If the frequency of the electromagnetic wave is high enough, then:

[0049] ε=K′ (3)

[0050] The roadbed is a three-phase material consisting of soil, water and air. The three-phase state of the roadbed is as follows: Figure 1 As shown, the relationship between the volumes of each phase of soil is:

[0051]

[0052] Where V and l represent the volume and thickness of the roadbed respectively, V i represents the volume of different phases that make up the roadbed, l i Indicates the thickness of the different phases that make up the roadbed.

[0053] Assuming that the total propagation time of electromagnetic waves through the roadbed is equal to the sum of the time it takes to pass through each phase of the roadbed, we have:

[0054] T=∑T i (5)

[0055] Where T represents the total time for electromagnetic waves to propagate in the roadbed, T i It represents the propagation time of electromagnetic waves in different phases of the roadbed. Taking the soil of unit width, the speed of electromagnetic waves in each phase is:

[0056]

[0057] Combining all the above formulas, we can get:

[0058]

[0059] Among them, ε, ε s , ε w , ε a are the dielectric constants of roadbed, soil, water and air respectively. V, V s , V w and Va Represent the volumes of roadbed, soil particles, water and air respectively.

[0060] Define e as the void ratio of the roadbed, then:

[0061]

[0062] e decreases with increasing compaction because the soil particles are rearranged during compaction and the interparticle pores decrease. The decrease in pore volume is mainly due to the air volume V a Because the volume of water is difficult to compress, there is V w / V s is a constant. Therefore, e can be approximated by the following equation:

[0063]

[0064] In this case, e is slightly smaller than the actual void ratio, and its value varies between 0.4 and 1.5, which is consistent with the range of the actual void ratio. In addition, the three-phase relationship of the roadbed can be obtained:

[0065]

[0066] Among them, ρ s is the density of soil particles, ρ d is the dry density of the roadbed.

[0067] The subgrade moisture content w can be calculated by the following formula:

[0068]

[0069] Among them, G s is the specific gravity of soil particles, ρ w is the density of distilled water at 4°C.

[0070] Combining the above equations, we can get:

[0071]

[0072] Then we have:

[0073]

[0074] Then we can get:

[0075]

[0076] Define k as the compaction degree of the roadbed, then:

[0077]

[0078] Among them, ρ dmaxis the maximum dry density of the roadbed.

[0079] Then we can get:

[0080]

[0081] As shown in Equation (16), the relationship between w and ε is affected by the size and compaction of the dielectric constant of each component. w and ε a Typical values ​​of are 81 and 1 respectively. Therefore, the equation can be expressed as:

[0082]

[0083] Among them, G dmax =ρ dmax / ρ w is the maximum dry weight ratio of soil, a, b and c are regression parameters considering different roadbed types.

[0084] This example conducted both indoor and field tests to determine the moisture content of different roadbeds at varying degrees of compaction and dielectric constant. For the indoor test, a typical clay soil (CL) from Shandong Province was selected as the test object, while for the field test, a typical red sandstone roadbed (GW) from Chongqing was selected as the test object.

[0085] Table 1 summarizes the two soil types including liquid limit (LL), plastic limit (PL), plasticity index (PI), maximum dry density (MDD), optimum moisture content (OMC), uniformity coefficient (Cu), curvature coefficient (Cc), and soil classification type determined according to USCS.

[0086] Table 1: Parameter characteristics of the two test soils

[0087] parameter Shandong clay soil Chongqing Hongyan Roadbed Liquid limit, LL (%) 28.4 23.9 Plastic limit, PL (%) 11.5 13.7 Plasticity Index, PI 16.9 10.2 <![CDATA[Maximum dry density, MDD (g / cm 3 )]]> 2.02 1.73 Optimum moisture content, OMC (%) 10.7 15.8 <![CDATA[Uniformity coefficient, C u > 32.9 11.2 <![CDATA[Curvature coefficient, C c > 1.2 1.6 Unified soil classification types GW CL

[0088] The specific steps of the laboratory test are as follows:

[0089] Sample preparation: First, the soil sample is dried and crushed to ensure uniformity. Then the moisture content of the soil sample is adjusted to be within 3% of the optimum moisture content (OMC).

[0090] Compaction test: Using standard test molds and compaction equipment, soil samples were compacted one by one according to different target compaction degrees (ranging from 0.90 to 0.98) in accordance with the specification requirements.

[0091] Dielectric constant measurement: The dielectric constant of soil samples under varying compaction and moisture content was measured using a SOILTOP-200 dielectric constant meter in conjunction with a CYZ-20 three-pin insertion probe. The acquisition equipment is shown in Figure 2(a). The SOILTOP-200 operates in a frequency range of 1 MHz to 4 GHz, ensuring accurate acquisition of the soil's dielectric properties.

[0092] Moisture content acquisition: After each measurement, the sample is taken out and dried, and the actual mass moisture content of the soil sample is determined using the standard drying method.

[0093] Laboratory test results show that under the same moisture content conditions, as the compaction degree of the soil sample increases, its dielectric constant also increases significantly. This indicates that the more compacted the soil, the smaller the pores between the soil particles, which has better water retention capacity and thus increases the dielectric constant.

[0094] The main steps of the field test are as follows:

[0095] Test area division: The test site was divided into three main areas (A, B and C), corresponding to low, medium and high compaction areas (compaction degree k<0.90, 0.90 <k<0.93,k> 0.93). Each area is further divided into two situations: high moisture content and low moisture content, such as Figure 3 As shown, the final test area can cover soil properties at a variety of compaction degrees and moisture contents.

[0096] On-site Compaction and Sampling: In each area, the subgrade soil is compacted using a vibratory rammer to ensure the desired compaction range is achieved. A cutting ring is then used to sample the compacted soil for further laboratory testing of its actual moisture content.

[0097] Field dielectric constant measurement: The SOILTOP-300 measuring device, paired with a CYZ-20 three-pin insertion probe, was used to measure the dielectric constant of soil in each area, acquiring dielectric constant data under varying compaction and moisture content conditions. This device is smaller, lighter, and has a faster response time than the SOILTOP-200 used in the laboratory, as shown in Figure 2(b).

[0098] Data obtained from field testing confirmed observations from laboratory tests: at the same moisture content, the more compacted the soil, the greater its dielectric constant. Compared to laboratory testing, field testing can simulate actual operating conditions during construction, making the test results more meaningful for practical engineering applications.

[0099] A total of 75 data points were collected from laboratory and field tests. Figure 4(a)-Figure 4(b) and Figure 5(a)-Figure 5(b)As shown in the figure, at various compaction levels, with the exception of a few outliers (marked in the figure), the water content of CL and GW increases with increasing dielectric constant. This can be explained by the fact that water has a higher dielectric constant, and its higher content leads to an increase in the dielectric constant of the roadbed. The higher the compaction level of the roadbed, the higher the dielectric constant at the same water content. Higher compaction means closer contact between soil particles, with fewer gaps between particles. In this case, the roadbed exhibits better water retention.

[0100] This example fits the regression parameters of the semi-empirical model to verify the accuracy of the model. The model's regression analysis was performed using soil compaction, dielectric constant, and mass moisture content data obtained indoors and on-site, combined with existing literature data. The results include:

[0101] A total of 75 valid data points were obtained from laboratory and field tests, covering clay soil (CL) in Shandong and red sandstone roadbed (GW) in Chongqing. In addition, to improve the applicability and stability of the model, this example also combined 48 soil data collected from the literature. The data covered six typical soil types: low-plasticity clay (CL), high-plasticity clay (CH), silt soil (ML), well-graded sand (SW), and well-graded gravel soil (GW). In total, 123 sets of data points were obtained, providing sufficient samples for regression analysis. Table 2 shows the soil properties obtained from the literature.

[0102] Table 2: Soil properties from the literature

[0103]

[0104] The subgrade mass moisture content prediction model proposed in this embodiment based on the soil dielectric properties and compaction state is:

[0105]

[0106] Where w is the water content of the roadbed mass, ε is the dielectric constant, k is the compaction degree, G dmax =ρ dmax / ρ w is the maximum dry weight ratio of soil, a, b and c are regression parameters considering different roadbed types.

[0107] IBM SPSS Statistics 25 software was used to perform a multivariate nonlinear regression analysis based on the Levenberg-Marquardt algorithm. The values ​​of parameters a, b, and c were optimized by minimizing the error between the model prediction value and the measured value. In the fitting process, the correlation coefficient R was introduced. 2 As an evaluation indicator to ensure the prediction performance of the model. The fitting results are:

[0108]

[0109] The regression parameters and their related statistical information are shown in Table 3. The predicted moisture content and actual moisture content of the semi-empirical model of this embodiment are shown in the figure Figure 6 shown.

[0110] Table 3: Regression parameters and their related statistics

[0111]

[0112] This example compares the prediction model after fitting regression with several existing moisture content prediction models, including the Topp method, the Drnevich method, and the Zhao method. The same data set is used to make predictions and compare them with the actual measured moisture content, including:

[0113] Topp et al. proposed a classic empirical model:

[0114] θ=4.3×10 -6 ε 3 -5.5×10 -4 ε 2 +2.92×10 -2 ε-5.3×10 -2

[0115] Here, θ represents the volumetric water content (VWC).

[0116] Drnevich et al. used a two-step method to determine the moisture content and dry density of the soil and gave an empirical calibration model:

[0117]

[0118] The values ​​of a1 and b1 for cohesionless soil are 1.0 and 8.5 respectively, and those for cohesive soil are 0.95 and 8.8 respectively. The mass moisture content prediction model proposed by Zhao et al. is:

[0119]

[0120] The statistical parameters related to the prediction performance of the four models are summarized in Table 4, where the statistical parameters related to the best performing model are highlighted in bold. It is obvious that the semi-empirical model proposed in this example shows better accuracy than other prediction models in the literature.

[0121] Table 4: Prediction performance statistics of the four models

[0122]

[0123] This embodiment directly measures the mass moisture content of the soil based on a roadbed mass moisture content prediction model based on the soil's dielectric properties and compaction state. This model combines the soil's dielectric properties with its compaction degree, significantly improving the moisture content prediction accuracy for a variety of soil types and compaction conditions. Existing methods, such as Topp et al., Drnevich et al., and Zhao et al., mostly rely on volumetric moisture content prediction methods. These methods fail to fully consider the impact of compaction on the dielectric constant when predicting soil moisture content, resulting in low prediction accuracy under different compaction conditions.

[0124] The prediction model presented in this paper is based on extensive data from laboratory and field tests. Key parameters are derived through regression analysis and fitting, making it widely applicable to different soil types and various compaction conditions. In practical engineering applications, model parameters can be adjusted based on actual site conditions to ensure measurement accuracy and applicability. This characteristic enables the method to demonstrate excellent prediction results across a wide range of construction environments.

[0125] Traditional moisture content measurement methods, such as drying, require destructive sampling and are time-consuming, making them unsuitable for real-time monitoring at construction sites. This method, by measuring the soil's dielectric constant and combining it with a proposed prediction model, can quickly and accurately determine the mass moisture content of the soil. The test process requires no complex procedures, making it suitable for real-time on-site application and significantly improving detection efficiency during construction.

[0126] Example 2

[0127] The purpose of this embodiment is to provide a roadbed quality moisture content prediction system based on dielectric properties and compaction degree, including:

[0128] A first construction module is configured to: construct a first relationship based on the roadbed volume and the corresponding dielectric constant, and the volume of each phase of the roadbed and the corresponding dielectric constant of each phase;

[0129] The second building block is configured to: obtain a relationship between the dielectric constant of each corresponding roadbed and the roadbed mass moisture content based on a relationship between the porosity and mass moisture content of the roadbed and the specific gravity of soil particles, in combination with the first relationship;

[0130] The third module is configured to: substitute the roadbed compaction degree into the relationship formula between the dielectric constant and the water content of the roadbed mass corresponding to each roadbed, to obtain the relationship formula between the dielectric constant, the roadbed compaction degree and the water content of the roadbed mass corresponding to each roadbed;

[0131] A prediction model building module is configured to: based on the test data of different roadbed types, fit the regression parameters in the relationship between the dielectric constant, roadbed compaction degree and roadbed mass moisture content of each corresponding roadbed type to obtain a corresponding prediction model;

[0132] The prediction module is configured to: predict the water content of the roadbed to be predicted according to the prediction model of the roadbed type to be predicted, and obtain a prediction result.

[0133] In further embodiments, there is also provided:

[0134] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed by the processor. When the computer instructions are executed by the processor, the method described in Example 1 is performed. For the sake of brevity, no further details are given here.

[0135] It should be understood that in this embodiment, the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0136] The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0137] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in embodiment 1 is performed.

[0138] The method in Example 1 can be directly implemented as being executed by a hardware processor, or by a combination of hardware and software modules within the processor. The software module can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not given here.

[0139] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in embodiment 1 is implemented.

[0140] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions contained in program modules, which are executed in a device on a real or virtual processor of a target to perform the process / method described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided between program modules as needed. The machine-executable instructions for the program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0141] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the computer or other programmable data processing device, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0142] In the context of the present invention, computer program code or related data can be carried by any appropriate carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, and the like.

[0143] Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0144] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree, characterized in that: include: Constructing a first relationship based on the volume of the roadbed and the corresponding dielectric constant, and the volume of each phase of the roadbed and the corresponding dielectric constant of each phase; According to the relationship between the porosity and mass moisture content of the roadbed and the specific gravity of soil particles, combined with the first relationship, the relationship between the dielectric constants of the roadbed and the mass moisture content of the roadbed is obtained; Substituting the roadbed compaction degree into the relationship between the corresponding dielectric constant and the roadbed mass moisture content, the relationship between the corresponding dielectric constant, roadbed compaction degree and roadbed mass moisture content is obtained, which is specifically: ， Among them, G dmax = ρ dmax / ρ w is the maximum dry weight ratio of soil, ρ dmax is the maximum dry density of the roadbed, a 、 b and c In order to consider the regression parameters of different roadbed types, k is the roadbed compaction degree, ɛ is the dielectric constant of the roadbed; Based on the test data of different roadbed types, the regression parameters in the relationship between the corresponding dielectric constant, roadbed compaction degree and roadbed mass moisture content were fitted to obtain the corresponding prediction model; According to the prediction model of the roadbed type corresponding to the roadbed to be predicted, the roadbed mass moisture content of the roadbed to be predicted is predicted to obtain a prediction result.

2. The method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree according to claim 1, wherein: Based on the roadbed volume and the corresponding dielectric constant, as well as the volume of each phase of the roadbed and the corresponding dielectric constant of each phase, a first relationship is constructed, specifically: Construct the first sub-relationship between the propagation speed of electromagnetic waves in the roadbed and the speed of light and the apparent dielectric constant of the roadbed; According to the relationship between the propagation speed of the electromagnetic wave in each phase of the roadbed and the thickness of the corresponding phase and the corresponding propagation time, and the relationship between the volume of the corresponding phase and the corresponding propagation time, the second sub-relationship is constructed respectively; The first relational expression is obtained by combining the first sub-relational expression and the second sub-relational expression based on the equation relationship between the total propagation time of the electromagnetic wave in the roadbed and the sum of the propagation time of the electromagnetic wave in each phase of the roadbed.

3. The method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree according to claim 1, wherein: The porosity of the roadbed is determined based on the ratio of the volume of air in the roadbed to the volume of soil particles in the roadbed. The relationship between the specific gravity of soil particles in the roadbed and the mass moisture content of the roadbed is constructed based on the volume of water in the roadbed, the volume of soil particles in the roadbed, and the relationship between the specific gravity of soil particles in the roadbed and the mass moisture content of the roadbed.

4. The method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree according to claim 1, wherein: The compaction degree of the roadbed is determined based on the ratio of the dry density of the roadbed to the maximum dry density of the roadbed.

5. The method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree according to claim 1, wherein: The porosity of the roadbed is expressed by the ratio of the volume of roadbed air to the volume of roadbed soil particles. Based on the relationship between the various phases of the roadbed, the expression of the roadbed porosity is converted into the ratio of the density of the roadbed soil particles to the dry density of the roadbed.

6. The method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree according to any one of claims 1 to 5, characterized in that: Based on the experimental data of mass moisture content under different roadbed compaction degrees and dielectric constants corresponding to different roadbed types, the regression parameters of the prediction models corresponding to different roadbed types were fitted using the regression analysis method. The Levenberg-Marquardt algorithm was used to determine the optimal regression parameter values ​​with the goal of minimizing the error between the predicted values ​​of the prediction model and the measured values.

7. The roadbed quality moisture content prediction system based on dielectric properties and compaction degree is characterized by: include: A first construction module is configured to: construct a first relationship based on the roadbed volume and the corresponding dielectric constant, and the volume of each phase of the roadbed and the corresponding dielectric constant of each phase; The second building block is configured to: obtain a relationship between the dielectric constant of each corresponding roadbed and the roadbed mass moisture content based on a relationship between the porosity and mass moisture content of the roadbed and the specific gravity of soil particles, in combination with the first relationship; The third module is configured to substitute the roadbed compaction degree into the relationship between the corresponding dielectric constant and the roadbed mass moisture content of the roadbed to obtain the relationship between the corresponding dielectric constant, roadbed compaction degree and roadbed mass moisture content, specifically: ， Among them, G dmax = ρ dmax / ρ w is the maximum dry weight ratio of soil, ρ dmax is the maximum dry density of the roadbed, a 、 b and c In order to consider the regression parameters of different roadbed types, k is the roadbed compaction degree, ɛ is the dielectric constant of the roadbed; A prediction model building module is configured to: based on the test data of different roadbed types, fit the regression parameters in the relationship between the dielectric constant, roadbed compaction degree and roadbed mass moisture content of each corresponding roadbed type to obtain a corresponding prediction model; The prediction module is configured to: predict the water content of the roadbed to be predicted according to the prediction model of the roadbed type to be predicted, and obtain a prediction result.

8. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that Used to store computer instructions, which, when executed by a processor, complete the method according to any one of claims 1 to 6.

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