Prediction method and electronic equipment for damage condition of highway asphalt pavement

Abstract

The present disclosure provides a method and electronic device for predicting the damage condition of a highway asphalt pavement, which comprises the steps of obtaining the pavement type of the predicted highway asphalt pavement, searching a database according to the pavement type, and determining a maximum pavement damage value and a minimum pavement damage value; obtaining structural layer information of the predicted highway asphalt pavement, and determining a damage rate of an upper layer structure and a damage rate of a middle layer structure according to the structural layer information; determining a damage transfer coefficient corresponding to the predicted highway asphalt pavement according to the damage rate of the upper layer structure and the damage rate of the middle layer structure; determining a predicted age, and inputting the predicted age, the maximum pavement damage value, the minimum pavement damage value, the damage rate of the upper layer structure, the damage rate of the middle layer structure, and the damage transfer coefficient into a pre-obtained decay model, and obtaining a predicted value of the pavement damage degree through processing and outputting the decay model; and outputting a prompt message in response to the predicted value of the pavement damage degree being greater than a preset damage threshold, wherein the prompt message is used to indicate that the pavement damage is serious.

Description

Technical Field

[0001] The present disclosure relates to the field of renewable energy prediction, and in particular to a method and electronic equipment for predicting the damage condition of a highway asphalt pavement. Background Art

[0002] The causes of highway asphalt pavement diseases are relatively complex, and are often caused by the combined effects of natural climate, traffic load, design quality, construction quality, maintenance measures and other factors.

[0003] In the process of highway network maintenance decision-making, it is impossible to carry out detailed special inspection work on a large scale, such as core sampling, material aging and mechanical properties testing, structural strength testing, etc., resulting in the lack of targeted analysis of the causes of pavement diseases and the low accuracy of maintenance decisions. Summary of the invention

[0004] In view of this, the purpose of the present disclosure is to provide a method and electronic device for predicting the damage condition of a highway asphalt pavement, so as to solve or partially solve the above-mentioned problems.

[0005] Based on the above purpose, the first aspect of the present disclosure provides a method for predicting the damage condition of a highway asphalt pavement, the method comprising:

[0006] Obtaining the road surface type of the predicted highway asphalt road surface, searching the database according to the road surface type, and determining the maximum and minimum road surface damage values;

[0007] Acquire the structural layer information of the predicted highway asphalt pavement, and determine the upper surface layer structural damage rate and the middle surface layer structural damage rate according to the structural layer information;

[0008] Determine the damage transfer coefficient corresponding to the predicted highway asphalt pavement according to the damage rate of the upper surface layer structure and the damage rate of the middle surface layer structure;

[0009] Determine the predicted years, input the predicted years, the maximum pavement damage value, the minimum pavement damage value, the upper surface layer structure damage rate, the middle surface layer structure damage rate and the damage transfer coefficient into a pre-obtained decay model, and obtain the predicted value of the pavement damage degree through processing output by the decay model;

[0010] In response to the predicted value of the road surface damage degree being greater than a preset damage threshold, a prompt message is output, wherein the prompt message is used to indicate that the road surface damage is serious.

[0011] Based on the same inventive concept, the second aspect of the present disclosure proposes a device for predicting the damage condition of a highway asphalt pavement, comprising:

[0012] A data acquisition module is configured to acquire the road surface type of the predicted highway asphalt road surface, search the database according to the road surface type, and determine the maximum road surface damage value and the minimum road surface damage value;

[0013] A damage rate determination module is configured to obtain structural layer information of the predicted highway asphalt pavement, and determine the damage rate of the upper layer structure and the damage rate of the middle layer structure according to the structural layer information;

[0014] A damage transfer coefficient determination module is configured to determine a damage transfer coefficient corresponding to the predicted highway asphalt pavement according to the damage rate of the upper surface layer structure and the damage rate of the middle surface layer structure;

[0015] The damage degree prediction module is configured to determine a predicted period, input the predicted period, the maximum pavement damage value, the minimum pavement damage value, the upper surface layer structure damage rate, the middle surface layer structure damage rate and the damage transfer coefficient into a pre-obtained decay model, and obtain a predicted value of the pavement damage degree through processing output by the decay model;

[0016] The prompt module is configured to output a prompt message in response to the predicted value of the road surface damage degree being greater than a preset damage threshold, wherein the prompt message is used to indicate that the road surface damage is serious.

[0017] Based on the same inventive concept, the third aspect of the present disclosure proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the above-mentioned method for predicting the damage condition of highway asphalt pavement when executing the computer program.

[0018] Based on the same inventive concept, the fourth aspect of the present disclosure proposes a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the method for predicting the damage condition of a highway asphalt pavement as described above.

[0019] As can be seen from the above, the present disclosure proposes a method and electronic device for predicting the damage condition of a highway asphalt pavement, obtains the pavement type of the predicted highway asphalt pavement, searches a database according to the pavement type, determines the maximum pavement damage value and the minimum pavement damage value, and determines the maximum pavement damage degree and the minimum pavement damage degree more accurately and more closely matches the pavement type. The structural layer information of the predicted highway asphalt pavement is obtained, and the damage rate of the upper layer structure and the damage rate of the middle layer structure are determined according to the structural layer information. The damage transfer coefficient corresponding to the predicted highway asphalt pavement is determined according to the damage rate of the upper layer structure and the damage rate of the middle layer structure. The predicted years are determined, and the predicted years, the maximum pavement damage value, the minimum pavement damage value, the damage rate of the upper layer structure, the damage rate of the middle layer structure and the damage transfer coefficient are input into a pre-obtained decay model, and the predicted value of the pavement damage degree is obtained through processing and outputting by the decay model. On the one hand, the predicted value of the pavement damage degree corresponding to the predicted years can be determined according to actual needs, so that the predicted value of the pavement damage degree is more in line with user needs. On the other hand, the corresponding road surface damage degree prediction value is determined by the trained decay model, and the obtained road surface damage degree prediction value is more accurate. When the road surface damage degree prediction value is greater than the preset damage threshold, a prompt message is output, wherein the prompt message is used to indicate that the road surface is seriously damaged, so as to remind the user that the predicted asphalt road surface of the highway is seriously damaged in the predicted period and needs more attention to avoid the occurrence of safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flowchart of a method for predicting damage conditions of a highway asphalt pavement according to an embodiment of the present disclosure;

[0022] Figure 2 It is a structural block diagram of a device for predicting damage conditions of a highway asphalt pavement according to an embodiment of the present disclosure;

[0023] Figure 3 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0025] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0026] The causes of highway asphalt pavement diseases are relatively complex, and are often caused by the combined effects of natural climate, traffic load, design quality, construction quality, maintenance measures and other factors.

[0027] In the process of highway network maintenance decision-making, it is impossible to carry out detailed special inspection work on a large scale, such as core sampling, material aging and mechanical properties testing, structural strength testing, etc., resulting in the lack of targeted analysis of the causes of pavement diseases and the low accuracy of maintenance decisions.

[0028] The current diagnosis and prediction methods for pavement diseases mainly have the following problems:

[0029] The standard specification method mainly uses qualitative description, lacks quantitative analysis and calculation, and is not very operational. The regression analysis method mainly uses statistical analysis, which is not very applicable to the changing rules under different use conditions or scenarios. In the mechanical empirical analysis, a clear quantitative calculation model is proposed, but the required model parameters are difficult to obtain and are not suitable for highway network maintenance decision analysis.

[0030] Based on the above description, this embodiment proposes a method for predicting the damage condition of a highway asphalt pavement. Figure 1 As shown, the method includes:

[0031] Step 101, obtaining the predicted road surface type of the asphalt road surface, searching a database according to the road surface type, and determining the maximum and minimum road surface damage values.

[0032] In a specific implementation, a road surface type of a predicted highway asphalt road surface is obtained, and the road surface type includes a highway and a non-highway. A database is searched according to the road surface type, and the database pre-stores a corresponding relationship between the road surface type and the maximum value of the road surface damage degree and the minimum value of the road surface damage degree, wherein the expression form of the corresponding relationship includes at least one of the following: a relationship table, a function relationship, a curve relationship, a key-value pair relationship, and a bar graph relationship.

[0033] Exemplarily, it is determined that the pavement type of the predicted highway asphalt pavement is a highway, and the database is searched to obtain that the maximum pavement damage degree corresponding to the highway is 100, and the maximum pavement damage degree corresponding to the highway is 60.

[0034] Step 102, obtaining the structural layer information of the predicted highway asphalt pavement, and determining the upper surface layer structural damage rate and the middle surface layer structural damage rate according to the structural layer information.

[0035] During the specific implementation, the structural layer information of the predicted highway asphalt pavement is obtained. In this embodiment, the structural layer information of the predicted highway asphalt pavement is obtained by ground penetrating radar equipment. The structural layer information includes the internal damage condition of the pavement structure, the location and impact range of the structural layer thickness, poor adhesion between structural layers, loose structural layers, voids, water-rich, cracks and other defects.

[0036] Based on the collected structural layer information, the upper surface layer structure damage rate and the middle surface layer structure damage rate are calculated.

[0037] Step 103: Determine the damage transfer coefficient corresponding to the predicted highway asphalt pavement according to the damage rate of the upper surface layer structure and the damage rate of the middle surface layer structure.

[0038] In practice, fatigue damage of pavement structure is generally in the weak position of structure. Under the continuous action of vehicle load, the internal damage of structure will be reflected upward until the road surface, forming road surface disease. Centered on the source of structural damage, the disease develops outward. The design principle of my country's highway pavement structure is strong base and thin surface. The strength of base and roadbed structure is high and the stability is good. Generally, the direction of internal damage reflection of structure is mainly upward, and the impact of the disease development downward on the road surface disease can be ignored.

[0039] Therefore, the damage transfer coefficient corresponding to the predicted highway asphalt pavement can be calculated based on the damage rate of the upper surface layer structure and the damage rate of the middle surface layer structure.

[0040] Step 104, determine the predicted years, input the predicted years, the maximum pavement damage value, the minimum pavement damage value, the upper layer structure damage rate, the middle layer structure damage rate and the damage transfer coefficient into a pre-obtained decay model, and obtain the predicted value of the pavement damage degree through processing output by the decay model.

[0041] In specific implementation, a pre-trained decay model is obtained to obtain a predicted age, which is a age set by a user. The predicted age, the maximum pavement damage value, the minimum pavement damage value, the upper surface structure damage rate, the middle surface structure damage rate and the damage transfer coefficient are input into the pre-obtained decay model, and the pavement damage degree prediction value is obtained through processing the output of the decay model.

[0042] Step 105: In response to the predicted value of the road surface damage degree being greater than a preset damage threshold, outputting a prompt message, wherein the prompt message is used to indicate that the road surface damage is serious.

[0043] In specific implementation, a preset damage threshold is obtained. When the predicted value of the road surface damage degree is greater than the preset damage threshold, it means that the predicted road asphalt pavement will be seriously damaged by the predicted period, and there may be safety problems. Therefore, a prompt message is output to prompt the user that the road surface is seriously damaged. The prompt message includes at least one of the following: voice, text, image, rich text, etc.

[0044] Exemplarily, the preset damage threshold is 60, and the predicted value of the road damage degree is 55, which means that by the predicted year, the damage of the predicted highway asphalt pavement will be relatively serious, and a prompt message "Please pay attention to this road surface" is output.

[0045] Through the above scheme, by obtaining the pavement type of the predicted highway asphalt pavement, searching the database according to the pavement type, determining the maximum pavement damage value and the minimum pavement damage value, the determination of the maximum pavement damage degree and the minimum pavement damage degree is more accurate and more matched with the pavement type. The structural layer information of the predicted highway asphalt pavement is obtained, and the upper layer structure damage rate and the middle layer structure damage rate are determined according to the structural layer information. The damage transfer coefficient corresponding to the predicted highway asphalt pavement is determined according to the upper layer structure damage rate and the middle layer structure damage rate. The predicted years are determined, and the predicted years, the maximum pavement damage value, the minimum pavement damage value, the upper layer structure damage rate, the middle layer structure damage rate and the damage transfer coefficient are input into the pre-obtained decay model, and the pavement damage degree prediction value is obtained through the decay model processing output. On the one hand, the pavement damage degree prediction value corresponding to the corresponding predicted years can be determined according to actual needs, so that the pavement damage degree prediction value is more in line with user needs. On the other hand, the corresponding pavement damage degree prediction value is determined by the trained decay model, and the pavement damage degree prediction value obtained is more accurate. When the predicted value of the road surface damage degree is greater than the preset damage threshold, a prompt message is output, wherein the prompt message is used to indicate that the road surface is seriously damaged, so as to remind the user that the predicted asphalt road surface of the highway is seriously damaged in the predicted period and needs more attention to avoid the occurrence of safety accidents.

[0046] In some embodiments, the training process of the decay model specifically includes:

[0047] Step 10A, obtaining a training data set and an initial decay model, wherein the training data set includes a training prediction period, a training maximum pavement damage value, a training minimum pavement damage value, a training upper surface structure damage rate, a training middle surface structure damage rate, a training damage transfer coefficient, and an actual pavement damage degree.

[0048] Step 10B: training the initial decay model using the data in the training data set to obtain a predicted degree of damage to the highway asphalt pavement.

[0049] Step 10C: determining a loss function according to the predicted degree of damage to the asphalt pavement of the highway and the actual degree of damage to the pavement.

[0050] Step 10D, in response to the loss function converging to a preset convergence threshold and satisfying a training end condition, a decay model is obtained.

[0051] During the specific implementation, a training data set and an initial decay model are obtained, wherein the training data set includes a training prediction period, a training maximum pavement damage value, a training minimum pavement damage value, a training upper surface structure damage rate, a training middle surface structure damage rate, a training damage transfer coefficient, and an actual pavement damage degree.

[0052] The data in the training data set is input into the initial decay model for training to obtain the predicted degree of damage of the asphalt pavement of the highway. A loss function is determined according to the predicted degree of damage of the asphalt pavement of the highway and the actual degree of damage of the pavement, and the type of the loss function includes at least one of the following: mean square error loss function, cross entropy loss function, logarithmic loss function, exponential loss function, square loss function or absolute value loss function, etc. When the loss function converges to a preset convergence threshold, it is determined that the training end condition is met and a decay model is obtained.

[0053] In some embodiments, the structural layer information includes damage type and damage information, and step 102 specifically includes:

[0054] Step 1021, obtaining a first damage type corresponding to the upper layer and a second damage type corresponding to the middle layer of the predicted highway asphalt pavement.

[0055] Step 1022: determine a first target weight and first damage information corresponding to the first damage type, and determine a second target weight and second damage information corresponding to the second damage type.

[0056] Step 1023: Calculate the damage rate of the upper surface layer structure according to the first target weight and the first damage information, and calculate the damage rate of the middle surface layer structure according to the second target weight and the second damage information.

[0057] In specific implementation, the first damage type corresponding to the upper layer of the predicted highway asphalt pavement and the second damage type corresponding to the final surface layer are obtained, and the first damage type and the second damage type are any of the following: the structural layer is loose, hollow, rich in water or cracked.

[0058] A first target weight and first damage information corresponding to the first damage type are determined, and a second target weight and second damage information corresponding to the second damage type are determined.

[0059] For example, if the first damage type is loose structure, the first target weight is 0.9. If the first damage type is hollow, the first target weight is 1. If the first damage type is rich in water, the first target weight is 0.7. If the first damage type is crack, the first target weight is 0.6.

[0060] The upper surface structure damage rate is calculated according to the first target weight and the first damage information, and the middle surface structure damage rate is calculated according to the second target weight and the second damage information. The upper surface structure damage rate and the middle surface structure damage rate are expressed by the formula:

[0061]

[0062] in, is the damage rate of the upper surface structure or the damage rate of the middle surface structure, i It is the upper layer structure or the middle layer structure. is the first target weight or the second target weight, is the first damage information or the second damage information, j For the damage type, A To predict the total area of ​​highway asphalt pavement or the total length of highway asphalt pavement.

[0063] In some embodiments, step 1022 specifically includes:

[0064] Step 10221, determine a first target weight corresponding to the first damage type and a second target weight corresponding to the second damage type.

[0065] Step 10222: In response to the first damage type and / or the second damage type being that the structural layer is loose, hollow, or rich in water, the first damage information and the second damage information are determined to be damage areas.

[0066] or,

[0067] Step 10223: In response to the first damage type and / or the second damage type being a crack, determine that the first damage information and the second damage information are damage lengths.

[0068] In a specific implementation, a first target weight corresponding to the first damage type and a second target weight corresponding to the second damage type are determined, wherein the first target weight and the second target weight may be the same or different.

[0069] If the first damage type and / or the second damage type is that the structural layer is loose, hollow, or rich in water, the first damage information and the second damage information are determined to be the damage area.

[0070] If the first damage type and / or the second damage type is a crack, the first damage information and the second damage information are determined to be damage lengths.

[0071] In some embodiments, the preset road surface includes an upper layer, a middle layer, a lower layer, an upper base layer, a lower base layer and a subbase layer, and step 103 specifically includes:

[0072] Step 1031, respectively determine the lower layer structure damage rate corresponding to the lower layer, the upper base structure damage rate corresponding to the upper base, the lower base structure damage rate corresponding to the lower base, and the lower base structure damage rate corresponding to the lower base.

[0073] Step 1032, selecting the maximum value among the upper layer structure damage rate, the middle layer structure damage rate, the lower layer structure damage rate, the upper base structure damage rate, the lower base structure damage rate and the bottom base structure damage rate as the target structure damage rate.

[0074] Step 1033, determine the mean value of the thickness uniformity coefficient and the mean value of the interlayer bonding coefficient, and obtain road age information.

[0075] Step 1034, based on the target structure damage rate, the thickness uniformity coefficient mean, the interlayer bonding coefficient mean and the road age information, the damage transfer coefficient corresponding to the predicted highway asphalt pavement is determined, wherein the damage transfer coefficient is expressed by the formula:

[0076]

[0077] in, is the damage transmission coefficient, is the damage rate of the upper layer structure, the damage rate of the middle layer structure, the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure or the damage rate of the lower base structure, i is the upper layer, the middle layer, the lower layer, the upper base, the lower base or the lower base, is the target structure damage rate, is the mean value of thickness uniformity coefficient, is the mean value of interlayer bonding coefficient, and T is the road age information.

[0078] During specific implementation, the damage rate of the lower layer structure corresponding to the lower layer, the damage rate of the upper base structure corresponding to the upper base, the damage rate of the lower base structure corresponding to the lower base and the damage rate of the lower base structure corresponding to the bottom base are determined respectively.

[0079] In this embodiment, the method for determining the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure and the damage rate of the bottom base structure is the same as the method for determining the damage rate of the upper layer structure and the damage rate of the middle layer structure, and is expressed by the formula:

[0080]

[0081] in, is the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure or the damage rate of the bottom base structure, i The structure is the lower layer structure, the upper layer structure, the lower layer structure or the bottom layer structure. is the target weight corresponding to the layer structure, is the damage information corresponding to the layer structure, j For the damaged type, A To predict the total area of ​​highway asphalt pavement or the total length of highway asphalt pavement.

[0082] The damage rate of the upper layer structure, the damage rate of the middle layer structure, the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure and the damage rate of the bottom base structure are compared, and the maximum value among them is selected as the target structure damage rate.

[0083] The mean value of the thickness uniformity coefficient, the mean value of the interlayer bonding coefficient and the road age information are obtained, and the damage transfer coefficient corresponding to the predicted highway asphalt pavement is determined according to the target structure damage rate, the mean value of the thickness uniformity coefficient, the mean value of the interlayer bonding coefficient and the road age information, wherein the damage transfer coefficient is expressed by the formula:

[0084]

[0085] in, is the damage transmission coefficient, The damage rate of the upper layer structure, the damage rate of the middle layer structure, the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure or the damage rate of the bottom base structure, i It is the upper layer, middle layer, lower layer, upper base layer, lower base layer or bottom base layer. is the target structure damage rate, is the mean value of thickness uniformity coefficient, is the mean value of interlayer bonding coefficient, T Road age information.

[0086] In some embodiments, the process of determining the mean value of the thickness uniformity coefficient specifically includes:

[0087] Step A, for each layer of the preset road surface:

[0088] Obtain the preset structural layer thickness and the actual structural layer thickness of the layer;

[0089] The thickness uniformity coefficient corresponding to the layer is determined according to the preset structural layer thickness and the actual structural layer thickness. The thickness uniformity coefficient is expressed by the formula:

[0090]

[0091] in, is the thickness uniformity coefficient, is the actual structural layer thickness, i It is the upper layer, middle layer, lower layer, upper base layer, lower base layer or bottom base layer. m is the preset structure layer thickness, n is the number of measurement points;

[0092] Step B, obtaining the thickness uniformity coefficients corresponding to all layers, and averaging all the thickness uniformity coefficients to obtain the mean value of the thickness uniformity coefficients.

[0093] In a specific implementation, for each layer in the preset road surface, the preset structural layer thickness and the actual structural layer thickness of the layer are obtained, and the thickness uniformity coefficient corresponding to the layer is determined according to the preset structural layer thickness and the actual structural layer thickness. The thickness uniformity coefficient is expressed by the formula:

[0094]

[0095] in, is the thickness uniformity coefficient, is the actual structural layer thickness, i It is the upper layer, middle layer, lower layer, upper base layer, lower base layer or bottom base layer. m is the preset structure layer thickness, n is the number of measurement points.

[0096] In some embodiments, the process of determining the mean value of the interlayer bonding coefficient specifically includes:

[0097] Step a, for any two layers in the preset road surface, obtain the total length of the preset road surface and the first length of the poor interlayer bonding section between the any two layers, perform ratio processing on the total length of the road surface and the first length, and obtain the interlayer bonding coefficient between the any two layers.

[0098] Step b, obtaining the interlayer bonding coefficients between all arbitrary two layers, and averaging all the interlayer bonding coefficients to obtain the mean interlayer bonding coefficient.

[0099] In a specific implementation, for any two layers in the preset road surface, the total length of the road surface of the preset road surface and the first length of the poor interlayer bonding section between the two layers are obtained. The total length of the road surface and the first length are processed by ratio to obtain the interlayer bonding coefficient between the two layers, wherein the interlayer bonding coefficient is expressed by the formula:

[0100]

[0101] in, is the interlayer bonding coefficient, is the first length, is the total length of the road surface.

[0102] In some embodiments, step 104 specifically includes:

[0103] Step 1041, obtaining road age information and current road damage degree, and obtaining predicted years;

[0104] Step 1042, the current road surface damage degree, the road age information, the predicted years, the maximum road surface damage value, the minimum road surface damage value, the upper surface layer structure damage rate, the middle surface layer structure damage rate and the damage transfer coefficient are input into a pre-obtained decay model, and the road surface damage degree prediction value is obtained by processing the output of the decay model. The road surface damage degree prediction value is expressed by the formula:

[0105] in, is the predicted value of road damage degree, The current road damage degree, is the maximum value of road damage, is the minimum value of road damage, is the damage transmission coefficient, is the damage rate of the upper structure, is the damage rate of the middle surface structure, T is the road age information, B are the model parameters of the decay model, t For the forecast period.

[0106] In the specific implementation, the pavement technical condition rapid detection equipment (CiCS) is used to detect the pavement disease detailed data and calculate the current pavement damage degree. The current pavement damage degree is expressed by the formula:

[0107]

[0108]

[0109] in, is the current road damage degree. Since the road surface in this embodiment is asphalt road surface, Take 15.00, Take 0.412, is the total number of damage types, and Take 21. For the i The cumulative area of ​​road damage of this type, i The road surface damage type, including the degree of damage (light, moderate, or severe). A For road surface inspection or survey area, is the weight or conversion factor of the i-th type of pavement damage.

[0110] The current road surface damage degree, the road age information, the predicted years, the maximum road surface damage value, the minimum road surface damage value, the upper surface layer structure damage rate, the middle surface layer structure damage rate and the damage transfer coefficient are input into the pre-obtained decay model, and the road surface damage degree prediction value is obtained by processing the output of the decay model. The road surface damage degree prediction value is expressed by the formula:

[0111] in, is the predicted value of road damage degree, The current road damage degree, is the maximum value of road damage, is the minimum value of road damage, is the damage transmission coefficient, is the damage rate of the upper structure, is the damage rate of the middle surface structure, T is the road age information, B are the model parameters of the decay model, t For the forecast period.

[0112] Based on the same inventive concept, another embodiment of the present disclosure provides a method for predicting the damage condition of a highway asphalt pavement, which specifically includes:

[0113] Use the Pavement Technical Condition Rapid Detection Equipment (CiCS) to detect pavement disease details and calculate the pavement damage index The specific calculation method is expressed by the formula:

[0114]

[0115]

[0116] in, is the current road damage degree. Since the road surface in this embodiment is asphalt road surface, Take 15.00, Take 0.412, is the total number of damage types, and Take 21. is the cumulative area of ​​the i-th type of road damage, i The road surface damage type, including the degree of damage (light, moderate, or severe). A For road surface inspection or survey area, For the i Weight or conversion factor for type of pavement damage.

[0117] Ground penetrating radar equipment is used to detect the internal damage of the pavement structure, collect the location and impact range of structural layer thickness, poor bonding between structural layers, loose structural layers, voids, water-rich, cracks and other defects, and calculate indicators such as interlayer bonding coefficient, damage rate of each structural layer, and thickness uniformity coefficient of each structural layer.

[0118] Among them, the interlayer bonding coefficient is expressed by the formula:

[0119]

[0120] in, is the interlayer bonding coefficient, is the first length, is the total length of the road surface.

[0121] The damage rate of the structural layer is expressed by the formula:

[0122]

[0123] in, The damage rate of the upper layer structure, the damage rate of the middle layer structure, the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure or the damage rate of the bottom base structure, i It is the upper layer, middle layer, upper base structure, lower base structure or bottom base structure. is the target weight corresponding to the layer structure, is the damage information corresponding to the layer structure, j For the damage type, A To predict the total area of ​​highway asphalt pavement or the total length of highway asphalt pavement.

[0124] The thickness uniformity coefficient is expressed by the formula:

[0125]

[0126] in, is the thickness uniformity coefficient, is the actual structural layer thickness, i It is the upper layer, middle layer, lower layer, upper base layer, lower base layer or bottom base layer. m is the preset structure layer thickness, n is the number of measurement points.

[0127] Pavement structure fatigue damage is generally located at the weak position of the structure. Under the continuous action of vehicle load, the internal damage of the structure will be reflected upward until the road surface, forming road surface diseases. Centered on the source of structural damage, the disease develops outward. The design principle of my country's highway pavement structure is strong base and thin surface. The base and roadbed structure are strong and stable. Generally, the direction of internal structural damage reflection is mainly upward, and the impact of the downward development of the disease on the road surface disease can be ignored.

[0128] The damage transfer coefficient is determined to characterize the transfer relationship of the damage conditions of each structural layer of the pavement. The damage transfer coefficient is expressed by the formula:

[0129]

[0130] in, is the damage transmission coefficient, The damage rate of the upper layer structure, the damage rate of the middle layer structure, the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure or the damage rate of the bottom base structure, i It is the upper layer, middle layer, lower layer, upper base layer, lower base layer or bottom base layer. is the target structure damage rate, is the mean value of thickness uniformity coefficient, is the mean value of interlayer bonding coefficient, T Road age information.

[0131] The decay of pavement performance shows the following characteristics: the first stage: the initial performance decays slowly and can basically be maintained at a relatively high level. The corresponding maintenance nature under this road condition level is preventive maintenance;

[0132] The second stage: after a certain period of time (usually 3 to 5 years), the road condition begins to show a trend of accelerated decay, and the decay rate of the road condition in this stage is 2 to 3 times that of the initial stage; in this stage, when the road condition level is still at a high level, the corresponding maintenance nature is medium repair; when the road condition level is at a lower level, the corresponding maintenance nature is functional overhaul;

[0133] The third stage: After accelerated attenuation to a certain stage, the road condition indicators are at a lower level, and the attenuation rate will slow down again until the end of its life; the corresponding maintenance nature under this road condition level is structural overhaul.

[0134] According to the decay law of pavement performance, a decay model is pre-trained, and the current pavement damage degree, the road age information, the predicted years, the maximum pavement damage value, the minimum pavement damage value, the upper surface structure damage rate, the middle surface structure damage rate and the damage transfer coefficient are input into the pre-obtained decay model, and the pavement damage degree prediction value is obtained through the decay model processing output. The pavement damage degree prediction value is expressed by the formula:

[0135] in, is the predicted value of road damage degree, The current road damage degree, is the maximum value of road damage, is the minimum value of road damage, is the damage transmission coefficient, is the damage rate of the upper structure, is the damage rate of the middle surface structure, T is the road age information, B are the model parameters of the decay model, t For the forecast period.

[0136] In this embodiment, the model architecture adopts a modified S-type model, which can better fit the general law of road disease development. At the same time, the model can set the index value range, which generally varies between 70 and 100 for expressways, between 60 and 100 for first and second-class roads, and between 40 and 100 for third and fourth-class roads.

[0137] In this embodiment, the model is predicted based on the damage status of the existing structure. The impact of the damage status of the existing structure on the prediction indicators in the future years is mainly reflected in the difference in decay speed. and The two indicators characterize the speed of structural damage transmission. The larger the indicator, the faster the decay rate.

[0138] In the model, the current PCI detection value is used as the benchmark for predicting road condition indicators. The decay model is used to calculate the attenuation value of the PCI indicator within the future prediction period. Finally, the two are combined to calculate the final prediction value. At the same time, in order to reflect the differences in the changes in road performance indicators under different regions and traffic volumes, the model provides parameters B , the model parameters can be fitted according to the actual data to improve the prediction accuracy.

[0139] In this embodiment, a three-dimensional evaluation index system for characterizing the internal damage of the pavement structure is created using ground penetrating radar detection data, including three items: structural layer damage rate, structural layer thickness uniformity coefficient, and inter-structure layer bonding coefficient. After big data statistical analysis, it is verified that the three indicators have a good correlation with the pavement damage condition and the change of the overall strength of the structure, which can be used for maintenance decision analysis. At the same time, the correlation between the internal structural damage of the pavement and the road surface disease index based on the ground penetrating radar detection data is established, and the pavement structure damage gradient parameter is constructed. Through this indicator, key information such as the source of structural damage, the direction of disease development, the speed of structural damage development, the uniformity of structural layer thickness, and the bonding effect between structural layers can be expressed at the same time. Finally, based on big data and statistical analysis, an accurate model of the decay law of the PCI index of pavement damage is established. The model adopts an S-shaped curve and incorporates key information such as the damage condition of the existing pavement structure layer, the classification of road surface disease causes, and the age of the road.

[0140] Compared with the current highway pavement performance prediction technology, the present invention integrates the advantages of regression analysis and mechanical empirical analysis, and uses big data regression analysis to determine the model framework; using mechanical empirical analysis, special investigation indicators such as pavement structure layer damage rate, interlayer adhesion, and structural layer thickness are related to the development form of pavement diseases. Based on the current structural damage condition, the future structural damage trend can be predicted more accurately.

[0141] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0142] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0143] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a device for predicting the damage condition of a highway asphalt pavement.

[0144] refer to Figure 2 , Figure 2 The device for predicting the damage condition of a highway asphalt pavement according to an embodiment includes:

[0145] The data acquisition module 201 is configured to acquire the road surface type of the predicted highway asphalt road surface, search the database according to the road surface type, and determine the maximum road surface damage value and the minimum road surface damage value;

[0146] The damage rate determination module 202 is configured to obtain the structural layer information of the predicted highway asphalt pavement, and determine the damage rate of the upper layer structure and the damage rate of the middle layer structure according to the structural layer information;

[0147] The damage transfer coefficient determination module 203 is configured to determine the damage transfer coefficient corresponding to the predicted highway asphalt pavement according to the damage rate of the upper surface layer structure and the damage rate of the middle surface layer structure;

[0148] The damage degree prediction module 204 is configured to determine a predicted period, input the predicted period, the maximum pavement damage value, the minimum pavement damage value, the upper surface layer structure damage rate, the middle surface layer structure damage rate and the damage transfer coefficient into a pre-obtained decay model, and obtain a predicted value of the pavement damage degree through processing output by the decay model;

[0149] The prompt module 205 is configured to output a prompt message in response to the predicted value of the road surface damage degree being greater than a preset damage threshold, wherein the prompt message is used to indicate that the road surface damage is serious.

[0150] In some embodiments, the device further includes a model training module, and the model training module is specifically configured to:

[0151] Obtaining a training data set and an initial decay model, wherein the training data set includes a training prediction period, a training maximum pavement damage value, a training minimum pavement damage value, a training upper surface structure damage rate, a training middle surface structure damage rate, a training damage transfer coefficient, and an actual pavement damage degree;

[0152] Using the data in the training data set to train the initial decay model, the predicted degree of damage to the asphalt pavement of the highway is obtained;

[0153] Determining a loss function according to the predicted degree of damage to the asphalt pavement of the highway and the actual degree of damage to the pavement;

[0154] In response to the loss function converging to a preset convergence threshold and satisfying a training end condition, a decay model is obtained.

[0155] In some embodiments, the structural layer information includes damage type and damage information, and the damage rate determination module 202 specifically includes:

[0156] A damage type determination unit is configured to obtain a first damage type corresponding to the upper layer and a second damage type corresponding to the middle layer of the predicted highway asphalt pavement;

[0157] a damage information determination unit, configured to determine a first target weight and first damage information corresponding to the first damage type, and determine a second target weight and second damage information corresponding to the second damage type;

[0158] The damage rate determination unit is configured to calculate the damage rate of the upper surface structure according to the first target weight and the first damage information, and calculate the damage rate of the middle surface structure according to the second target weight and the second damage information.

[0159] In some embodiments, the damage information determination unit is specifically configured to:

[0160] Determining a first target weight corresponding to the first damage type and a second target weight corresponding to the second damage type;

[0161] In response to the first damage type and / or the second damage type being that the structural layer is loose, hollow or rich in water, the first damage information and the second damage information are determined to be damage areas; or,

[0162] In response to the first damage type and / or the second damage type being a crack, the first damage information and the second damage information are determined to be a damage length.

[0163] In some embodiments, the damage rate determination unit is specifically configured to:

[0164] The upper surface structure damage rate is calculated according to the first target weight and the first damage information, and the middle surface structure damage rate is calculated according to the second target weight and the second damage information. The upper surface structure damage rate and the middle surface structure damage rate are expressed by the formula:

[0165]

[0166] in, is the damage rate of the upper surface structure or the damage rate of the middle surface structure, i It is the upper layer structure or the middle layer structure. is the first target weight or the second target weight, is the first damage information or the second damage information, j For the damage type, A To predict the total area of ​​highway asphalt pavement or the total length of highway asphalt pavement.

[0167] In some embodiments, the preset road surface includes an upper layer, a middle layer, a lower layer, an upper base layer, a lower base layer and a subbase layer, and the damage transfer coefficient determination module 203 is specifically configured as follows:

[0168] Determine the damage rate of the lower layer structure corresponding to the lower layer, the damage rate of the upper base structure corresponding to the upper base, the damage rate of the lower base structure corresponding to the lower base, and the damage rate of the lower base structure corresponding to the lower base;

[0169] The maximum value among the damage rate of the upper layer structure, the damage rate of the middle layer structure, the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure and the damage rate of the bottom base structure is selected as the target structure damage rate;

[0170] Determine the mean value of thickness uniformity coefficient and interlayer bonding coefficient, and obtain road age information;

[0171] According to the target structure damage rate, the thickness uniformity coefficient mean, the interlayer bonding coefficient mean and the road age information, the damage transfer coefficient corresponding to the predicted highway asphalt pavement is determined, wherein the damage transfer coefficient is expressed by the formula:

[0172]

[0173] in, is the damage transmission coefficient, The damage rate of the upper layer structure, the damage rate of the middle layer structure, the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure or the damage rate of the bottom base structure, i It is the upper layer, middle layer, lower layer, upper base layer, lower base layer or bottom base layer. is the target structure damage rate, is the mean value of thickness uniformity coefficient, is the mean value of interlayer bonding coefficient, T Road age information.

[0174] In some embodiments, the damage transfer coefficient determination module 203 further includes a thickness uniformity coefficient mean value determination unit, and the thickness uniformity coefficient mean value determination unit is specifically configured as follows:

[0175] For each layer in the preset road surface:

[0176] Obtain the preset structural layer thickness and the actual structural layer thickness of the layer;

[0177] The thickness uniformity coefficient corresponding to the layer is determined according to the preset structural layer thickness and the actual structural layer thickness. The thickness uniformity coefficient is expressed by the formula:

[0178]

[0179] in, is the thickness uniformity coefficient, is the actual structural layer thickness, i It is the upper layer, middle layer, lower layer, upper base layer, lower base layer or bottom base layer. m is the preset structure layer thickness, n is the number of measurement points;

[0180] The thickness uniformity coefficients corresponding to all layers are obtained, and all thickness uniformity coefficients are averaged to obtain the mean value of the thickness uniformity coefficients.

[0181] In some embodiments, the damage transfer coefficient determination module 203 further includes an interlayer bonding coefficient mean value determination unit, and the interlayer bonding coefficient mean value determination unit is specifically configured as follows:

[0182] For any two layers of the preset road surface:

[0183] Obtaining the total length of the preset road surface and the first length of the poor interlayer bonding section between any two layers;

[0184] Performing ratio processing on the total length of the pavement and the first length to obtain an interlayer bonding coefficient between any two layers;

[0185] The interlayer bonding coefficients between any two layers are obtained, and all the interlayer bonding coefficients are averaged to obtain the average interlayer bonding coefficient.

[0186] In some embodiments, the damage degree prediction module 204 is specifically configured to:

[0187] Obtain road age information and current road damage level, and obtain predicted service life;

[0188] The current road surface damage degree, the road age information, the predicted years, the maximum road surface damage value, the minimum road surface damage value, the upper surface layer structure damage rate, the middle surface layer structure damage rate and the damage transfer coefficient are input into the pre-obtained decay model, and the road surface damage degree prediction value is obtained by processing the output of the decay model. The road surface damage degree prediction value is expressed by the formula:

[0189] in, is the predicted value of road damage degree, The current road damage degree, is the maximum value of road damage, is the minimum value of road damage, is the damage transmission coefficient, is the damage rate of the upper structure, is the damage rate of the middle surface structure, T is the road age information, B are the model parameters of the decay model, t For the forecast period.

[0190] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0191] The device of the above embodiment is used to implement the corresponding prediction method of the highway asphalt pavement damage condition in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0192] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for predicting the damage condition of the highway asphalt pavement described in any of the above embodiments is implemented.

[0193] Figure 3 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0194] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0195] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0196] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0197] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0198] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0199] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0200] The electronic device of the above embodiment is used to implement the corresponding method for predicting the damage condition of the highway asphalt pavement in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0201] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for predicting the damage condition of highway asphalt pavement as described in any of the above embodiments.

[0202] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0203] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method for predicting the damage condition of the highway asphalt pavement as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0204] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0205] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0206] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0207] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0208] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0209] In addition, to simplify the description and discussion, and in order not to obscure the embodiments of the present disclosure, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, devices may be shown in the form of block diagrams to avoid obscuring the embodiments of the present disclosure, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0210] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the discussed embodiments.

[0211] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for predicting the damage condition of a highway asphalt pavement, characterized in that: include: Obtaining the road surface type of the predicted highway asphalt road surface, searching the database according to the road surface type, and determining the maximum and minimum road surface damage values; Acquire the structural layer information of the predicted highway asphalt pavement, and determine the upper surface layer structural damage rate and the middle surface layer structural damage rate according to the structural layer information; Determine the damage transfer coefficient corresponding to the predicted highway asphalt pavement according to the damage rate of the upper surface layer structure and the damage rate of the middle surface layer structure; Determine the predicted years, input the predicted years, the maximum pavement damage value, the minimum pavement damage value, the upper surface layer structure damage rate, the middle surface layer structure damage rate and the damage transfer coefficient into a pre-obtained decay model, and obtain the predicted value of the pavement damage degree through processing output by the decay model; In response to the predicted value of the road surface damage degree being greater than a preset damage threshold, outputting a prompt message, wherein the prompt message is used to indicate that the road surface damage is serious; The preset road surface includes the upper layer, middle layer, lower layer, upper base layer, lower base layer and subbase layer. The method of determining and predicting the damage transfer coefficient corresponding to the asphalt pavement of the highway according to the damage rate of the upper surface layer structure and the damage rate of the middle surface layer structure includes: Determine the damage rate of the lower layer structure corresponding to the lower layer, the damage rate of the upper base structure corresponding to the upper base, the damage rate of the lower base structure corresponding to the lower base, and the damage rate of the lower base structure corresponding to the lower base; The maximum value among the damage rate of the upper layer structure, the damage rate of the middle layer structure, the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure and the damage rate of the bottom base structure is selected as the target structure damage rate; Determine the mean value of thickness uniformity coefficient and interlayer bonding coefficient, and obtain road age information; According to the target structure damage rate, the thickness uniformity coefficient mean, the interlayer bonding coefficient mean and the road age information, the damage transfer coefficient corresponding to the predicted highway asphalt pavement is determined, wherein the damage transfer coefficient is expressed by the formula: in, is the damage transmission coefficient, is the damage rate of the upper layer structure, the damage rate of the middle layer structure, the damage rate of the lower layer structure, the damage rate of the upper base structure, the damage rate of the lower base structure or the damage rate of the lower base structure, i is the upper layer, the middle layer, the lower layer, the upper base, the lower base or the lower base, is the target structure damage rate, is the mean value of thickness uniformity coefficient, is the mean value of interlayer bonding coefficient, and T is the road age information.

2. The method according to claim 1, characterized in that The training process of the decay model specifically includes: Obtaining a training data set and an initial decay model, wherein the training data set includes a training prediction period, a training maximum pavement damage value, a training minimum pavement damage value, a training upper surface structure damage rate, a training middle surface structure damage rate, a training damage transfer coefficient, and an actual pavement damage degree; Using the data in the training data set to train the initial decay model, the predicted degree of damage to the asphalt pavement of the highway is obtained; Determining a loss function according to the predicted degree of damage to the asphalt pavement of the highway and the actual degree of damage to the pavement; In response to the loss function converging to a preset convergence threshold and satisfying a training end condition, a decay model is obtained.

3. The method according to claim 1, characterized in that The structural layer information includes damage type and damage information; The step of obtaining the predicted structural layer information of the asphalt pavement of the highway and determining the upper surface layer structural damage rate and the middle surface layer structural damage rate according to the structural layer information includes: Obtaining a first damage type corresponding to the upper layer and a second damage type corresponding to the middle layer of the predicted highway asphalt pavement; Determine a first target weight and first damage information corresponding to the first damage type, and determine a second target weight and second damage information corresponding to the second damage type; The upper surface structure damage rate is calculated according to the first target weight and the first damage information, and the middle surface structure damage rate is calculated according to the second target weight and the second damage information.

4. The method according to claim 3, characterized in that The determining of a first target weight and first damage information corresponding to the first damage type, and determining a second target weight and second damage information corresponding to the second damage type, includes: Determining a first target weight corresponding to the first damage type and a second target weight corresponding to the second damage type; In response to the first damage type and / or the second damage type being that the structural layer is loose, hollow or rich in water, the first damage information and the second damage information are determined to be damage areas; or, In response to the first damage type and / or the second damage type being a crack, the first damage information and the second damage information are determined to be a damage length.

5. The method according to claim 3, characterized in that: The calculating of the damage rate of the upper surface structure according to the first target weight and the first damage information, and the calculating of the damage rate of the middle surface structure according to the second target weight and the second damage information include: The upper surface structure damage rate is calculated according to the first target weight and the first damage information, and the middle surface structure damage rate is calculated according to the second target weight and the second damage information. The upper surface structure damage rate and the middle surface structure damage rate are expressed by the formula: in, is the damage rate of the upper layer structure or the damage rate of the middle layer structure, i is the upper layer structure or the middle layer structure, is the first target weight or the second target weight, is the first damage information or the second damage information, j is the damage type, and A is the predicted total area of ​​the asphalt pavement of the highway or the predicted total length of the asphalt pavement of the highway.

6. The method according to claim 1, characterized in that Determining the thickness uniformity coefficient mean value includes: For each layer in the preset road surface: Obtain the preset structural layer thickness and the actual structural layer thickness of the layer; The thickness uniformity coefficient corresponding to the layer is determined according to the preset structural layer thickness and the actual structural layer thickness. The thickness uniformity coefficient is expressed by the formula: in, is the thickness uniformity coefficient, is the actual structural layer thickness, i is the upper layer, middle layer, lower layer, upper base layer, lower base layer or bottom base layer, m is the preset structural layer thickness, and n is the number of measurement points; The thickness uniformity coefficients corresponding to all layers are obtained, and all thickness uniformity coefficients are averaged to obtain the mean value of the thickness uniformity coefficients.

7. The method according to claim 1, characterized in that The determining of the mean value of the interlayer bonding coefficient comprises: For any two layers of the preset road surface: Obtaining the total length of the preset road surface and the first length of a poor interlayer bonding section between any two layers; Performing ratio processing on the total length of the pavement and the first length to obtain an interlayer bonding coefficient between any two layers; The interlayer bonding coefficients between any two layers are obtained, and all the interlayer bonding coefficients are averaged to obtain the average interlayer bonding coefficient.

8. The method according to claim 1, characterized in that The method of determining the predicted life span, inputting the predicted life span, the maximum pavement damage value, the minimum pavement damage value, the upper surface layer structure damage rate, the middle surface layer structure damage rate and the damage transfer coefficient into a pre-obtained decay model, and obtaining a predicted value of the pavement damage degree through processing the output of the decay model, includes: Obtain road age information and current road damage level, and obtain predicted service life; The current road surface damage degree, the road age information, the predicted years, the maximum road surface damage value, the minimum road surface damage value, the upper surface layer structure damage rate, the middle surface layer structure damage rate and the damage transfer coefficient are input into the pre-obtained decay model, and the road surface damage degree prediction value is obtained by processing the output of the decay model. The road surface damage degree prediction value is expressed by the formula: in, is the predicted value of road damage degree, The current road damage degree, is the maximum value of road damage, is the minimum value of road damage, is the damage transmission coefficient, is the damage rate of the upper structure, is the damage rate of the middle surface structure, T is the road age information, B is the model parameter of the decay model, and t is the predicted years.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the program.

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