A road material stability analysis method and system

Abstract

The present invention discloses a road material stability analysis method and system, which relates to the technical field of material analysis, including: calculating the initial stability performance value of the road material according to the characteristics of the road material; calculating the influence weight of the climate condition on the road material stability performance value; calculating the influence weight of the traffic load on the road material stability performance value; creating a road material stability prediction model based on the influence weight of the climate condition and the traffic load on the road material stability performance value; and predicting the current stability of the road material using the road material stability prediction model according to the application environment of the road material and the initial stability performance value of the road material. The stability of the road material can be evaluated more comprehensively and accurately, providing a scientific basis for road design, construction and maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of material analysis, and in particular to a road material stability analysis method and system. Background Art

[0002] Current research emphasizes the important impact of road material stability on people's lives and production. With the increase in transportation vehicles, the overloaded operation of roads has led to the emergence of pavement problems. Therefore, higher requirements are put forward for the research on road material stability, including analyzing the factors affecting roadbed stability and proposing corresponding solutions. Summary of the invention

[0003] The present invention provides a road material stability analysis method, comprising:

[0004] Step 1. Calculate the initial stability performance value of the road material according to the characteristics of the road material;

[0005] Step 2, calculate the influence weight of climate conditions on the performance value of road material stability;

[0006] Step 3, calculate the influence weight of traffic load on the performance value of road material stability;

[0007] Step 4: Create a road material stability prediction model based on the influence weights of climate conditions and traffic loads on the road material stability performance value;

[0008] Step 5: According to the application environment of the road material and the initial stability performance value of the road material, use the road material stability prediction model to predict the current stability of the road material.

[0009] A road material stability analysis method as described above, wherein the initial stability performance value of the road material is calculated according to the characteristics of the road material, specifically comprises the following sub-steps:

[0010] Extracting stability parameters of road materials from experimental test data of road materials;

[0011] Calculate the influence coefficients of different characteristics of road materials on stability parameters, and organize the calculation results into an influence coefficient table;

[0012] The initial stability performance value of the road material to be analyzed is calculated based on the influence coefficient table.

[0013] A road material stability analysis method as described above, wherein the weight of the influence of climate conditions on the road material stability performance value is calculated, specifically comprises the following sub-steps:

[0014] On-site collection of road material samples under different climatic conditions;

[0015] Performing performance tests on road material samples to obtain the current stability performance values ​​of the samples;

[0016] Calculate the initial stability performance value of the sample according to the characteristics of the road material sample;

[0017] The climate characteristics are used as input, and the difference between the initial stability performance value and the current stability performance value of the sample is used as output for model training;

[0018] After the model training is completed, the weights of each climate feature in the model are extracted.

[0019] A road material stability analysis method as described above, wherein the weight of the influence of traffic load on the road material stability performance value is calculated, specifically comprises the following sub-steps:

[0020] Collect samples of road materials and traffic load data on different sections of roads in the same area;

[0021] Extracting traffic load characteristics from traffic load data;

[0022] Calculate the stability performance values ​​of road material samples at different road sections;

[0023] The difference in stability performance values ​​of samples from different road sections is used as output, and the traffic load characteristics are used as input for model training;

[0024] After the model training is completed, the weights of each traffic load feature in the model are extracted.

[0025] A road material stability analysis method as described above, wherein the current stability of the road material is predicted using a road material stability prediction model, specifically comprises the following sub-steps:

[0026] Collect climatic conditions data and traffic load data of the road material application environment;

[0027] Extract climate characteristics from climate condition data and traffic load characteristics from traffic load data;

[0028] The climate characteristics, traffic load characteristics, and initial stability performance values ​​of road materials are input into the road material stability prediction model, and the prediction results are output.

[0029] The present invention also provides a road material stability analysis system, comprising: a road material initial stability determination module, a road material stability influencing factor analysis module, and a road material stability prediction module;

[0030] A road material initial stability determination module, used to calculate the initial stability performance value of the road material according to the characteristics of the road material;

[0031] The road material stability influencing factor analysis module is used to analyze the relationship between the factors affecting the road material stability and the stability performance value, so as to determine the weight of the influencing factors;

[0032] The road material stability prediction module is used to predict the current stability of the road material using the road material stability prediction model according to the application environment of the road material and the initial stability performance value of the road material.

[0033] A road material stability analysis system as described above, wherein the road material stability influencing factor analysis module includes a climate condition analysis submodule and a traffic load analysis submodule;

[0034] Climate condition analysis submodule: used to calculate the influence weight of climate conditions on the performance value of road material stability;

[0035] The traffic load analysis submodule is used to calculate the impact weight of traffic load on the stability performance value of road materials.

[0036] A road material stability analysis system as described above, wherein the road material stability prediction module includes a road material stability prediction model creation submodule and a road material stability output submodule;

[0037] The road material stability prediction model creation submodule is used to create a road material stability prediction model based on the weights of the influence of climate conditions and traffic loads on the road material stability performance value;

[0038] The road material stability output submodule is used to output the prediction results of road material stability.

[0039] The beneficial effects achieved by the present invention are as follows: the stability of road materials is evaluated more comprehensively and accurately, providing a scientific basis for road design, construction and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0041] Figure 1 This is a flow chart of a road material stability analysis method provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0042] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0043] Embodiment 1

[0044] like Figure 1 As shown, Embodiment 1 of the present invention provides a road material stability analysis method, comprising:

[0045] Step S10: calculating the initial stability performance value of the road material according to the characteristics of the road material;

[0046] The characteristics of road materials refer to the composition and mixing ratio of the main material, aggregate, and filler, which together determine the stability of the road material. The following sub-steps are combined to determine and quantify the stability of the road material:

[0047] Step S11: extracting stability parameters of road materials from experimental test data of road materials;

[0048] Under standard temperature and humidity conditions, the characteristics of road materials are adjusted to repeatedly perform pressure, bending, abrasion, tensile and other performance tests on material samples to obtain experimental test data of road materials. It can also be directly obtained through existing databases or research reports and other media.

[0049] The stability parameters of road materials include: load, fatigue life, deformation resistance, crack resistance, wear resistance and other data items that reflect the performance of road materials.

[0050] Step S12: Calculate the influence coefficients of different characteristics of road materials on stability parameters, and organize the calculation results into an influence coefficient table;

[0051] The proportions of different mixtures in road materials are expressed as decimals, and the components are expressed by their own initial compressive strength values. For example, if 10mm crushed stone is used as aggregate, it is expressed by the maximum pressure that the 10mm crushed stone itself can withstand.

[0052] First, the experimental test data are grouped according to the characteristics of the road material. The grouping condition is that the values ​​of other characteristics are consistent except for the characteristic item to be calculated. Then the data in the group are sorted in positive order, and then the formula is used:

[0053] To calculate the influence coefficient of this characteristic on the stability parameter, where a k is the value of the characteristic item calculated in the kth experimental data in the group, a k-1is the value of the characteristic item calculated in the k-1th experimental data in the group, b tk is the tth stability parameter value in the kth experimental data in the group, b tk-1 is the tth stability parameter value in the k-1th experimental data in the group, k ranges from 2 to z, z is the total number of experimental data in the group, t ranges from 1 to w, w is the total number of stability parameters.

[0054] The calculated influence coefficient is filled back into the influence coefficient table. The fields of the influence coefficient table include characteristic number, characteristic value, stability parameter and influence coefficient. The influence coefficient table is associated with the road material characteristic table according to the characteristic number.

[0055] Step S13: calculating the initial stability performance value of the road material to be analyzed based on the influence coefficient table;

[0056] Select the characteristics of the road material from the human-computer interaction page, that is, the main material, aggregate, and filler, the components and proportions of these three mixtures, obtain the characteristic number from the road material characteristic table according to the selected content, and use the characteristic number to query the corresponding characteristic value and influence coefficient from the influence coefficient table, using the formula: To calculate the initial stability performance value of the road material to be analyzed, where μ t The weight set for the tth stability parameter, c td is the influence coefficient of the dth road material property on the tth stability parameter, a d is the value of the dth road material characteristic, d takes values ​​from 1 to q, q is the total number of road material characteristics, and t takes values ​​from 1 to w, w is the total number of stability parameters.

[0057] It should be noted that μ t It is a preset quantity, which is preset manually based on certain professional experience. It is used to integrate multiple road material stability parameters into a stability performance value, which serves as the basic data for subsequent analysis and processing steps.

[0058] Step S20: Calculate the influence weight of climate conditions on the performance value of road material stability;

[0059] Temperature changes will affect the viscoelasticity of asphalt mixtures, thereby affecting their crack resistance and rutting resistance; freeze-thaw cycles will significantly reduce the crack resistance and durability of road materials. It can be seen that climate conditions have a significant impact on the stability performance value of road materials. Analyzing the impact of different climate conditions on the stability performance value of road materials specifically includes the following sub-steps:

[0060] Step S21: collecting road material samples under different climatic conditions on site;

[0061] Step S22: Performing a performance test on the road material sample to obtain a current stability performance value of the sample;

[0062] Step S23: calculating the initial stability performance value of the sample according to the characteristics of the road material sample;

[0063] Step S24: taking the climate characteristics as input and the difference between the initial stability performance value and the current stability performance value of the sample as output for model training;

[0064] Climate characteristics include maximum temperature, minimum temperature, maximum precipitation, minimum precipitation, freeze-thaw period, rainy season and other data reflecting the local climate.

[0065] The model here is represented as: Among them B initial is the initial stability performance value of the road material sample, B current is the current stability performance value of the road material sample, E1~E n They have different climate characteristics. are the impact weights of different climate characteristics, θ is the error term, and n is the number of climate characteristics.

[0066] Step S25: After the model training is completed, the weights of various climate characteristics in the model are extracted;

[0067] After verifying that the accuracy of the model meets expectations, the weights of the extracted climate characteristics are used to quantify the impact of different climate conditions on the stability performance value of road materials.

[0068] Step S30: Calculate the influence weight of traffic load on the performance value of road material stability;

[0069] In addition to climate factors, traffic load is also a key factor affecting the stability of road materials. The calculation of the impact weight of traffic load on the performance value of road material stability includes the following sub-steps:

[0070] Step S31: Collect samples of road materials and traffic load data on different sections in the same area;

[0071] The purpose of collecting data in the same area is to ensure the consistency of climatic conditions. Traffic load data can be collected by installing collection points on the road and using various collection equipment to obtain data such as the weight, axle load, speed, and vehicle type of passing vehicles.

[0072] Step S32: extracting traffic load characteristics from traffic load data;

[0073] Traffic load characteristics include but are not limited to: the proportion of passing vehicle types, maximum axle load, maximum driving speed, average traffic volume, average load frequency, and average number of load actions.

[0074] Step S33: calculating the stability performance values ​​of road material samples at different road sections;

[0075] Step S34: using the difference of the stability performance values ​​of samples of different road sections as output and the traffic load characteristics as input to perform model training;

[0076] The model here is expressed as: R0-R1=F1×ω1+F2×ω2+…+F m ×ω m +θ, where R0-R1 represents the difference between the sample stability performance values ​​obtained from sections R0 and R1, and F1~F m They represent the characteristic values ​​of different traffic load data collected from road section R0, ω1~ω m They represent the weights of different traffic load characteristics, θ is the error term, and m is the total number of traffic load characteristics.

[0077] Step S35: After the model training is completed, the weights of each traffic load feature in the model are extracted.

[0078] Step S40: creating a road material stability prediction model based on the influence weights of climate conditions and traffic loads on the road material stability performance value;

[0079] The road material stability prediction model is expressed as: Where B is the initial stability performance value of the road material, E i is the value of the i-th climate feature input, is the weight of the i-th climate feature, F j is the value of the jth traffic load characteristic input, ω j is the weight of the jth traffic load feature, θ is the error term, i takes values ​​from 1 to n, n is the total number of climate features, and j takes values ​​from 1 to m, m is the total number of traffic load features.

[0080] Step S50: Based on the application environment of the road material and the initial stability performance value of the road material, the road material stability prediction model is used to predict the current stability of the road material. Specifically:

[0081] Step S51: collecting climate condition data and traffic load data of the road material application environment;

[0082] Step S52: extracting climate features from the climate condition data and traffic load features from the traffic load data;

[0083] Step S53: inputting climate characteristics, traffic load characteristics, and initial stability performance values ​​of road materials into a road material stability prediction model, and outputting prediction results.

[0084] Embodiment 2

[0085] Embodiment 2 of the present invention provides a road material stability analysis system, including: a road material initial stability determination module, a road material stability influencing factor analysis module, and a road material stability prediction module;

[0086] (1) a road material initial stability determination module, used to calculate the initial stability performance value of the road material according to the characteristics of the road material;

[0087] The characteristics of road materials refer to the composition and mixing ratio of the main material, aggregate, and filler, which together determine the stability of road materials. Determining and quantifying the stability of road materials specifically includes:

[0088] I. Extracting stability parameters of road materials from experimental test data of road materials;

[0089] Under standard temperature and humidity conditions, the characteristics of road materials are adjusted to repeatedly perform pressure, bending, abrasion, tensile and other performance tests on material samples to obtain experimental test data of road materials. It can also be directly obtained through existing databases or research reports and other media.

[0090] The stability parameters of road materials include: load, fatigue life, deformation resistance, crack resistance, wear resistance and other data items that reflect the performance of road materials.

[0091] II. Calculate the influence coefficients of different characteristics of road materials on stability parameters, and organize the calculation results into an influence coefficient table;

[0092] The proportions of different mixtures in road materials are expressed as decimals, and the components are expressed by their own initial compressive strength values. For example, if 10mm crushed stone is used as aggregate, it is expressed by the maximum pressure that the 10mm crushed stone itself can withstand.

[0093] First, the experimental test data are grouped according to the characteristics of the road material. The grouping condition is that the values ​​of other characteristics are consistent except for the characteristic item to be calculated. Then the data in the group are sorted in positive order, and then the formula is used:

[0094] To calculate the influence coefficient of this characteristic on the stability parameter, where a k is the value of the characteristic item calculated in the kth experimental data in the group, a k-1 is the value of the characteristic item calculated in the k-1th experimental data in the group, b tk is the tth stability parameter value in the kth experimental data in the group, b tk-1 is the tth stability parameter value in the k-1th experimental data in the group, k ranges from 2 to z, z is the total number of experimental data in the group, t ranges from 1 to w, w is the total number of stability parameters.

[0095] The calculated influence coefficient is filled back into the influence coefficient table. The fields of the influence coefficient table include characteristic number, characteristic value, stability parameter and influence coefficient. The influence coefficient table is associated with the road material characteristic table according to the characteristic number.

[0096] III. Calculate the initial stability performance value of the road material to be analyzed based on the influence coefficient table;

[0097] Select the characteristics of the road material from the human-computer interaction page, that is, the main material, aggregate, and filler, the components and proportions of these three mixtures, obtain the characteristic number from the road material characteristic table according to the selected content, and use the characteristic number to query the corresponding characteristic value and influence coefficient from the influence coefficient table, using the formula: To calculate the initial stability performance value of the road material to be analyzed, where μ t The weight set for the tth stability parameter, c td is the influence coefficient of the dth road material property on the tth stability parameter, a d is the value of the dth road material characteristic, d takes values ​​from 1 to q, q is the total number of road material characteristics, and t takes values ​​from 1 to w, w is the total number of stability parameters.

[0098] It should be noted that μ t It is a preset quantity, which is preset manually based on certain professional experience. It is used to integrate multiple road material stability parameters into a stability performance value, which serves as the basic data for subsequent analysis and processing steps.

[0099] (2) Road material stability influencing factor analysis module, which is used to analyze the relationship between factors affecting road material stability and stability performance values, so as to determine the weight of the influencing factors, including climate condition analysis submodule and traffic load analysis submodule;

[0100] 1. Climate condition analysis submodule: used to calculate the influence weight of climate conditions on the performance value of road material stability;

[0101] Temperature changes will affect the viscoelasticity of asphalt mixtures, thereby affecting their crack resistance and rutting resistance; freeze-thaw cycles will significantly reduce the crack resistance and durability of road materials. It can be seen that climate conditions have a significant impact on the stability performance value of road materials. The impact of different climate conditions on the stability performance value of road materials is analyzed, including:

[0102] I. Collect road material samples under different climatic conditions on site;

[0103] II. Performing performance tests on road material samples to obtain the current stability performance values ​​of the samples;

[0104] III. Calculate the initial stability performance value of the sample according to the characteristics of the road material sample;

[0105] IV. Take climate characteristics as input and the difference between the initial stability performance value and the current stability performance value of the sample as output for model training;

[0106] Climate characteristics include maximum temperature, minimum temperature, maximum precipitation, minimum precipitation, freeze-thaw period, rainy season and other data reflecting the local climate.

[0107] The model here is represented as: Among them B initial is the initial stability performance value of the road material sample, B current is the current stability performance value of the road material sample, E1~E n They have different climate characteristics. are the impact weights of different climate characteristics, θ is the error term, and n is the number of climate characteristics.

[0108] V. After the model training is completed, the weights of each climate feature in the model are extracted;

[0109] After verifying that the accuracy of the model meets expectations, the weights of the extracted climate characteristics are used to quantify the impact of different climate conditions on the stability performance value of road materials.

[0110] 2. Traffic load analysis submodule, used to calculate the impact weight of traffic load on the stability performance value of road materials;

[0111] In addition to climate factors, traffic load is also a key factor affecting the stability of road materials. The weight of traffic load on the performance value of road material stability is calculated, including:

[0112] I. Collect samples of road materials and traffic load data on different sections of roads in the same area;

[0113] The purpose of collecting data in the same area is to ensure the consistency of climatic conditions. Traffic load data can be collected by installing collection points on the road and using various collection equipment to obtain data such as the weight, axle load, speed, and vehicle type of passing vehicles.

[0114] II. Extracting traffic load characteristics from traffic load data;

[0115] Traffic load characteristics include but are not limited to: the proportion of passing vehicle types, maximum axle load, maximum driving speed, average traffic volume, average load frequency, and average number of load actions.

[0116] III. Calculate the stability performance values ​​of road material samples in different sections;

[0117] IV. The difference of stability performance values ​​of samples in different road sections is used as output, and the traffic load characteristics are used as input for model training;

[0118] The model here is expressed as: R0-R1=F1×ω1+F2×ω2+…+F m ×ω m +θ, where R0-R1 represents the difference between the sample stability performance values ​​obtained from sections R0 and R1, and F1~F m They represent the characteristic values ​​of different traffic load data collected from road section R0, ω1~ω m They represent the weights of different traffic load characteristics, θ is the error term, and m is the total number of traffic load characteristics.

[0119] V. After model training is completed, the weights of each traffic load feature in the model are extracted.

[0120] (3) a road material stability prediction module, which is used to predict the current stability of the road material using a road material stability prediction model according to the application environment of the road material and the initial stability performance value of the road material, and includes a road material stability prediction model creation submodule and a road material stability output submodule;

[0121] 1. The road material stability prediction model creation submodule is used to create a road material stability prediction model based on the influence weights of climate conditions and traffic loads on the road material stability performance value;

[0122] The road material stability prediction model is expressed as: Where B is the initial stability performance value of the road material, E i is the value of the i-th climate feature input, is the weight of the i-th climate feature, F j is the value of the jth traffic load characteristic input, ω j is the weight of the jth traffic load feature, θ is the error term, i takes values ​​from 1 to n, n is the total number of climate features, and j takes values ​​from 1 to m, m is the total number of traffic load features.

[0123] 2. The road material stability output submodule is used to output the prediction results of road material stability, including:

[0124] I. Collect climate condition data and traffic load data of the road material application environment;

[0125] II. Extract climate characteristics from climate condition data and traffic load characteristics from traffic load data;

[0126] III. Input the climate characteristics, traffic load characteristics, and initial stability performance values ​​of road materials into the road material stability prediction model and output the prediction results.

[0127] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A road material stability analysis method, characterized in that: The method comprises: Step 1. Calculate the initial stability performance value of the road material according to the characteristics of the road material; Step 2, calculate the influence weight of climate conditions on the performance value of road material stability; Step 3, calculate the influence weight of traffic load on the performance value of road material stability; Step 4. Create a road material stability prediction model based on the influence weights of climate conditions and traffic loads on the performance value of road material stability; the road material stability prediction model is expressed as: Where B is the initial stability performance value of the road material, E i is the value of the i-th climate feature input, is the weight of the i-th climate feature, F j is the value of the jth traffic load characteristic input, ω j is the weight of the jth traffic load feature, θ is the error term, i takes values ​​from 1 to n, n is the total number of climate features, j takes values ​​from 1 to m, m is the total number of traffic load features; Step 5. According to the application environment of the road material and the initial stability performance value of the road material, use the road material stability prediction model to predict the current stability of the road material; Calculating the weight of the impact of climate conditions on the performance value of road material stability includes the following sub-steps: On-site collection of road material samples under different climatic conditions; Performing performance tests on road material samples to obtain the current stability performance values ​​of the samples; Calculate the initial stability performance value of the sample according to the characteristics of the road material sample; The climate characteristics are used as input, and the difference between the initial stability performance value and the current stability performance value of the sample is used as output for model training; the model is expressed as: Among them B initial is the initial stability performance value of the road material sample, B current is the current stability performance value of the road material sample, E1~E n They have different climate characteristics. are the impact weights of different climate characteristics; After the model training is completed, the weights of each climate feature in the model are extracted.

2. A road material stability analysis method according to claim 1, characterized in that: The initial stability performance value of the road material is calculated according to the characteristics of the road material, which specifically includes the following sub-steps: Extracting stability parameters of road materials from experimental test data of road materials; Calculate the influence coefficients of different characteristics of road materials on stability parameters, and organize the calculation results into an influence coefficient table; The initial stability performance value of the road material to be analyzed is calculated based on the influence coefficient table.

3. A road material stability analysis method according to claim 1, characterized in that: Calculating the weight of the impact of traffic load on the performance value of road material stability includes the following sub-steps: Collect samples of road materials and traffic load data on different sections of roads in the same area; Extracting traffic load characteristics from traffic load data; Calculate the stability performance values ​​of road material samples at different road sections; The difference in the stability performance values ​​of samples from different road sections is used as the output, and the traffic load characteristics are used as the input for model training; the model is expressed as: R0-R1=F1×ω1+F2×ω2+…+F m ×ω m +θ, where R0-R1 represents the difference between the sample stability performance values ​​obtained from sections R0 and R1, and F1~F m They represent the characteristic values ​​of different traffic load data collected from road section R0, ω1~ω m They represent the weights of different traffic load characteristics respectively; After the model training is completed, the weights of each traffic load feature in the model are extracted.

4. A road material stability analysis method according to claim 1, characterized in that: The road material stability prediction model is used to predict the current stability of the road material, which specifically includes the following sub-steps: Collect climatic conditions data and traffic load data of the road material application environment; Extract climate characteristics from climate condition data and traffic load characteristics from traffic load data; The climate characteristics, traffic load characteristics, and initial stability performance values ​​of road materials are input into the road material stability prediction model, and the prediction results are output.

5. A road material stability analysis system, characterized in that: The system comprises: a road material initial stability determination module, a road material stability influencing factor analysis module, and a road material stability prediction module; A road material initial stability determination module, used to calculate the initial stability performance value of the road material according to the characteristics of the road material; The road material stability influencing factor analysis module is used to analyze the relationship between the factors affecting the road material stability and the stability performance value, so as to determine the weight of the influencing factors; The road material stability prediction module is used to predict the current stability of the road material using the road material stability prediction model according to the application environment of the road material and the initial stability performance value of the road material; the road material stability prediction model is expressed as: Where B is the initial stability performance value of the road material, E i is the value of the i-th climate feature input, is the weight of the i-th climate feature, F j is the value of the jth traffic load characteristic input, ω j is the weight of the jth traffic load feature, θ is the error term, i takes values ​​from 1 to n, n is the total number of climate features, j takes values ​​from 1 to m, m is the total number of traffic load features; The road material stability influencing factor analysis module includes a climate condition analysis submodule, which is specifically used for: Collect road material samples under different climatic conditions on site; perform performance tests on road material samples to obtain the current stability performance value of the samples; calculate the initial stability performance value of the samples according to the characteristics of the road material samples; use climate characteristics as input and the difference between the initial stability performance value and the current stability performance value of the samples as output for model training; the model is expressed as: Among them B initial is the initial stability performance value of the road material sample, B current is the current stability performance value of the road material sample, E1~E n They have different climate characteristics. They are the influence weights of different climate characteristics respectively; after the model training is completed, the weights of each climate characteristic in the model are extracted.

6. A road material stability analysis system according to claim 5, characterized in that: A road material stability prediction module, including a road material stability prediction model creation submodule and a road material stability output submodule; The road material stability prediction model creation submodule is used to create a road material stability prediction model based on the influence weights of climate conditions and traffic loads on the road material stability performance value; the road material stability output submodule is used to output the prediction results of road material stability.

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