Roadbed quality moisture content prediction method and system based on dielectric property and compactness

Abstract

According to the method and the system for predicting the mass moisture content of the roadbed based on the dielectric property and the compaction degree, the dielectric property and the compaction state of a soil body are comprehensively considered, and a prediction model of the relationship among the dielectric constant, the compaction degree of the roadbed and the mass moisture content of the roadbed is constructed; the mass moisture content of different types of soil bodies under different compaction conditions is accurately predicted, and the method has the advantages of being high in precision and high in adaptability. Key parameters are obtained through regression analysis fitting, and the method can be widely applied to different types of soil bodies and various compaction degree conditions. In practical engineering application, model parameters can be adjusted according to actual conditions on site, accuracy and applicability of measurement are ensured, and the method can show an excellent prediction effect in various construction environments due to the characteristic.

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 an important factor affecting compaction, stability and bearing capacity. Traditional moisture content determination methods, such as drying method and alcohol combustion method, although with high measurement accuracy, are complex and time-consuming to operate, usually require destructive sampling, and are difficult to meet the needs of real-time detection during construction. With the development of non-contact measurement technology, moisture content measurement methods based on dielectric properties have gradually been applied. These methods use the relationship between soil moisture content and dielectric constant to indirectly obtain moisture content by measuring dielectric constant. However, most of these methods establish the relationship between dielectric constant and volume moisture content (VWC), while mass moisture content (MWC) is mostly used in actual engineering applications.

[0004] Therefore, how to quickly and accurately determine the moisture content of the roadbed mass to solve the problems of long testing time, large testing error and low testing accuracy for the moisture content of the roadbed mass in the prior art is a technical problem that needs to be solved at present. Summary of the invention

[0005] In order to overcome the shortcomings 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 realize accurate prediction of 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 solution:

[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] A first relationship is constructed 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 ratio of the roadbed and the water content of the roadbed mass and the specific gravity of soil particles, combined with the first relationship, the relationship between the dielectric constants of the roadbed and the water content of the roadbed mass is obtained;

[0010] Substituting the roadbed compaction degree into the relationship between each corresponding dielectric constant of the roadbed and the water content of the roadbed mass, the relationship between each corresponding dielectric constant, roadbed compaction degree and 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 are 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 water content of the roadbed mass is predicted for the roadbed to be predicted, and the prediction result is obtained.

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

[0014] A first construction module is configured to: construct 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;

[0015] The second building block is configured to: obtain the relationship between the dielectric constants of the roadbed and the water content of the roadbed mass according to the relationship between the porosity of the roadbed and the water content of the roadbed mass and the specific gravity of the soil particles, combined with the first relationship;

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

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

[0018] The prediction module is configured to: predict the water content of the roadbed mass of the roadbed to be predicted according to the prediction model of the roadbed type corresponding to the roadbed 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 when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[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, and a prediction model of the relationship between the dielectric constant, the compaction degree of the roadbed and the mass moisture content of the roadbed is constructed, so as to 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. The key parameters are obtained by 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 effects 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 become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings in the specification, 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 It is a 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 the first embodiment of the present invention;

[0028] Figure 4(a)-Figure 4(b) They are respectively the relationship diagrams between the dielectric constant and the water content, and the dielectric constant and the compaction degree of the CL soil in the indoor test provided in the first embodiment of the present invention;

[0029] Figure 5(a)-Figure 5(b) They are respectively the relationship diagrams between the dielectric constant and the water content, and the dielectric constant and the compaction degree of the GW soil in the field test provided in the first embodiment 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 meanings as those 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 specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

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

[0034] Embodiment 1

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

[0036] A first relationship is constructed 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 ratio of the roadbed and the water content of the roadbed mass and the specific gravity of soil particles, combined with the first relationship, the relationship between the dielectric constants of the roadbed and the water content of the roadbed mass is obtained;

[0038] Substituting the roadbed compaction degree into the relationship between each corresponding dielectric constant of the roadbed and the water content of the roadbed mass, the relationship between each corresponding dielectric constant, roadbed compaction degree and 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 are 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 water content of the roadbed mass is predicted for the roadbed to be predicted, and the prediction result is obtained.

[0041] This embodiment comprehensively considers the dielectric properties and compaction state of the soil, and realizes accurate prediction of the mass moisture content of different types of soil under different compaction conditions. This method has the characteristics of high accuracy and strong adaptability, and provides a new technical solution for moisture control and quality detection 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 passing 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 below. 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. Figure 1 As shown, the relationship between the volumes of various phases 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 They 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 soil particles are rearranged and interparticle pores are reduced during compaction. The reduction in pore volume is mainly due to the air volume V a Because the volume of water is difficult to compress, 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 water content w of the roadbed 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] And then:

[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 compactness of the dielectric constant of each component. w and ε a The 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] In this embodiment, indoor tests and field tests were conducted to obtain the moisture content of different roadbeds at different compaction degrees and dielectric constants. The indoor test selected a typical clay soil (CL) in Shandong as the test object, and the field test selected a typical red sandstone roadbed (GW) in Chongqing as the test object.

[0085] Table 1 summarizes the two soils 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 moulds and compaction equipment, soil samples were compacted one by one according to different target compaction degrees (ranging from 0.90 to 0.98) as per specification requirements.

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

[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] The results of laboratory tests show that under the same moisture content, as the compaction degree of the soil sample increases, its dielectric constant also increases significantly. This indicates that the more compacted the soil is, the smaller the pores between the soil particles are, and the better the water retention capacity is, thus increasing 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 water content and low water content. 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 roadbed soil is compacted by a vibrating tamper to ensure the required compaction range is achieved. The compacted soil is then sampled using a cutting ring for further testing of its actual moisture content in the laboratory.

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

[0098] The data obtained through the field test confirmed the observations in the laboratory test: under the same moisture content conditions, the higher the compaction degree of the soil, the greater its dielectric constant. Compared with laboratory tests, field tests can simulate the 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 tests and field tests. Figure 4(a)-Figure 4(b) and Figure 5(a)-Figure 5(b)As shown in the figure, at different compaction degrees, except for a few abnormal data (marked in the figure), the water content of CL and GW increases with the growth of dielectric constant. It can be explained that water has a higher dielectric constant and its higher content will lead to an increase in the dielectric constant of the roadbed. The higher the compaction degree of the roadbed, the higher the dielectric constant at the same water content. A higher compaction degree means that the soil particles are in closer contact with each other and there are fewer gaps between the particles. In this case, the roadbed is better able to show better water retention.

[0100] This example fits the regression parameters of the semi-empirical model to verify the accuracy of the model. The model regression analysis is performed by combining the soil compaction, dielectric constant and mass moisture content data obtained indoors and on-site with the existing literature data, including:

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

[0102] Table 2: Soil properties from the literature

[0103]

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

[0105]

[0106] In the formula, 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 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 2 As an evaluation indicator to ensure the predictive 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 Figure 6 shown.

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

[0111]

[0112] This embodiment compares the prediction model after fitting regression with several existing moisture content prediction models, including Topp et al., Drnevich et al., and Zhao et al. The same data set is used for prediction and compared 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] Among them, the values ​​of a1 and b1 for cohesionless soil are 1.0 and 8.5 respectively, and the values ​​for cohesive soil are 0.95 and 8.8. 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 compared with 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 the prediction model of the roadbed mass moisture content of the soil dielectric properties and compaction state, and combines the dielectric properties of the soil with the compaction degree, significantly improving the moisture content prediction accuracy under various soil types and compaction conditions. Most of the existing technologies are based on volume moisture content prediction methods, such as Topp et al., Drnevich et al., Zhao et al., etc. These methods fail to fully consider the impact of compaction degree on dielectric constant when predicting soil moisture content, resulting in low prediction accuracy under different compaction conditions.

[0124] The prediction model of the present invention is based on a large amount of data from laboratory and field tests, and key parameters are obtained through regression analysis and fitting. It 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.

[0125] Traditional moisture content measurement methods, such as drying methods, require destructive sampling and are time-consuming, and are not suitable for real-time detection at construction sites. The present invention measures the dielectric constant of the soil and combines it with the proposed prediction model to quickly and accurately measure the mass moisture content of the soil. The test process does not require complicated operations and is suitable for real-time application on site, greatly improving the detection efficiency during the construction process.

[0126] Embodiment 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 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;

[0129] The second building block is configured to: obtain the relationship between the dielectric constants of the roadbed and the water content of the roadbed mass according to the relationship between the porosity of the roadbed and the water content of the roadbed mass and the specific gravity of the soil particles, combined with the first relationship;

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

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

[0132] The prediction module is configured to: predict the water content of the roadbed mass of the roadbed to be predicted according to the prediction model of the roadbed type corresponding to the roadbed 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 on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, no further description is given here.

[0135] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf 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 the processor may also be any conventional processor, etc.

[0136] The memory may include a read-only memory and a random access memory, and provide 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. When the computer instructions are executed by a processor, the method described in embodiment 1 is completed.

[0138] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0139] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in the first embodiment 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 included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, 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 functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for 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 the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0142] In the context of the present invention, computer program codes or related data may 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, etc. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0143] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0144] Although the above describes the specific implementation mode 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 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: A first relationship is constructed 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 ratio of the roadbed and the water content of the roadbed mass and the specific gravity of soil particles, combined with the first relationship, the relationship between the dielectric constants of the roadbed and the water content of the roadbed mass is obtained; Substituting the roadbed compaction degree into the relationship between each corresponding dielectric constant of the roadbed and the water content of the roadbed mass, the relationship between each corresponding dielectric constant, roadbed compaction degree and water content of the roadbed mass is obtained; 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 are fitted to obtain the corresponding prediction model; According to the prediction model of the roadbed type corresponding to the roadbed to be predicted, the water content of the roadbed mass is predicted for the roadbed to be predicted, and the prediction result is obtained.

2. The method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree according to claim 1, characterized in that: Based on the volume of the roadbed 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, which is: 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; According to 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, the first sub-relationship and the second sub-relationship are combined to obtain the first relationship.

3. The method for predicting the moisture content of roadbed mass based on dielectric properties and compaction degree according to claim 1, characterized in that: 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 mass moisture content of the roadbed and the specific gravity of the soil particles 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 the 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, characterized in that: 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, characterized in that: The porosity of the roadbed is expressed by the ratio of the volume of the roadbed air to the volume of the 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 claim 1, characterized in that: The relationship between the corresponding dielectric constant, roadbed compaction and roadbed mass moisture content is as follows: 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 are regression parameters considering different roadbed types, k is the compaction degree of the roadbed, and ε is the dielectric constant of the roadbed.

7. 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 6, characterized in that: According to the test 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 by regression analysis method. The Levenberg-Marquardt algorithm was used to determine the optimal regression parameter value with the goal of minimizing the error between the predicted value and the measured value of the prediction model.

8. 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 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; The second building block is configured to: obtain the relationship between the dielectric constants of the roadbed and the water content of the roadbed mass according to the relationship between the porosity of the roadbed and the water content of the roadbed mass and the specific gravity of the soil particles, combined with the first relationship; The third module is configured to: substitute the roadbed compaction degree into the relationship formula between each corresponding dielectric constant of the roadbed and the water content of the roadbed mass, and obtain the relationship formula between each corresponding dielectric constant, roadbed compaction degree and the water content of the roadbed mass; A prediction model building module is configured to: based on the test data of different roadbed types, respectively fit the regression parameters in the relationship between the corresponding dielectric constant, roadbed compaction degree and roadbed mass moisture content to obtain the corresponding prediction model; The prediction module is configured to: predict the water content of the roadbed mass of the roadbed to be predicted according to the prediction model of the roadbed type corresponding to the roadbed to be predicted, and obtain a prediction result.

9. 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 7 is completed.

10. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 7.

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