Physicochemical evaluation method and device for successive aging of asphalt pavement, terminal and medium

Abstract

The application discloses a physical and chemical evaluation method and device for successive aging of asphalt pavement, a terminal and a medium, and relates to the technical field of data processing. The method comprises the following steps: determining first physical and chemical correlation data based on physical and chemical historical data of asphalt materials, aging test historical data and historical aging grade data; determining second physical and chemical correlation data based on environmental state historical data, pavement structure position data and historical aging grade data; and performing physical and chemical evaluation on the asphalt pavement based on the first physical and chemical correlation data and the second physical and chemical correlation data to obtain a target evaluation result. The application aims to realize accurate physical and chemical evaluation on successive aging of asphalt pavement through multi-dimensional data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a physicochemical evaluation method and device for successive aging of asphalt pavement, a terminal and a medium. BACKGROUND

[0002] Asphalt pavement is the mainstream pavement form of global highways and urban roads due to its high flatness, comfortable driving, and convenient construction, etc. Its service life directly affects the traffic efficiency and safety. However, in the long-term use, the asphalt pavement will be successively aged due to environmental effects (ultraviolet radiation, temperature fluctuation, and precipitation erosion) and traffic load (vehicle rolling and impact) - from the oxidation of light oil in the surface asphalt and the decrease of viscoelasticity, to the gradual development of internal structure loosening and crack expansion, and finally to the appearance of pavement skid resistance attenuation and pothole diseases, which may need to be milled and repaved in severe cases, causing huge economic losses.

[0003] However, the traditional physicochemical evaluation technology of asphalt pavement aging has the following shortcomings: the existing technology is mostly based on "physicochemical data of asphalt materials" or "single aging test data" (such as RTFOT / PAV test results) as the basis for evaluation, without integrating environmental state and pavement structure position data; the existing technology mostly outputs evaluation results (such as only determining the aging stage) through "manual statistics" or "simple regression model", which cannot adapt to the gradualness of successive aging and is difficult to support fine maintenance decisions; at the same time, the existing technology mostly relies on the experience of technical personnel to judge and decide, which cannot avoid the deviation of subjective judgment or decision. SUMMARY

[0004] The main purpose of the present application is to provide a physicochemical evaluation method, device, terminal and medium for successive aging of asphalt pavement, which aims to realize accurate physicochemical evaluation of successive aging of asphalt pavement through multi-dimensional data.

[0005] To achieve the above purpose, the present application provides a physicochemical evaluation method for successive aging of asphalt pavement, which comprises:

[0006] obtaining physicochemical historical data of asphalt materials corresponding to the asphalt pavement, aging test historical data corresponding to the asphalt pavement, historical aging grade data corresponding to the asphalt pavement, environmental state historical data corresponding to the asphalt pavement, and pavement structure position data corresponding to the asphalt pavement;

[0007] determining first physicochemical correlation data based on the physicochemical historical data of asphalt materials, the aging test historical data, and the historical aging grade data, wherein the first physicochemical correlation data is used to represent the correlation between the intrinsic physicochemical properties of asphalt materials, the aging test data, and the successive aging process of the asphalt pavement;

[0008] determine second physicochemical correlation data based on the environmental state historical data, the pavement structure position data, and the historical aging grade data, wherein the second physicochemical correlation data is used to represent the correlation between external environmental factors, pavement structure positions, and the aging process of the asphalt pavement;

[0009] perform physicochemical evaluation on the asphalt pavement based on the first physicochemical correlation data and the second physicochemical correlation data to obtain a target evaluation result.

[0010] Specifically, the determination of the first physicochemical correlation data based on the asphalt material physicochemical historical data, the aging test historical data, and the historical aging grade data comprises:

[0011] determine a material physicochemical weight coefficient based on the asphalt material physicochemical historical data, wherein the material physicochemical weight coefficient is used to represent the influence degree of physicochemical indexes of the asphalt pavement on the aging of the asphalt pavement;

[0012] determine an aging test weight coefficient based on the aging test historical data, wherein the aging test weight coefficient is used to represent the performance ability of the historical test data of the asphalt pavement on the aging stage;

[0013] determine the first physicochemical correlation data based on the asphalt material physicochemical historical data, the aging test historical data, the historical aging grade data, the material physicochemical weight coefficient, and the aging test weight coefficient.

[0014] Specifically, the determination of the first physicochemical correlation data based on the asphalt material physicochemical historical data, the aging test historical data, the historical aging grade data, the material physicochemical weight coefficient, and the aging test weight coefficient comprises:

[0015] calculate a first similarity between the asphalt material physicochemical historical data and the historical aging grade data;

[0016] calculate a second similarity between the aging test historical data and the historical aging grade data;

[0017] perform weighted summation processing on the first similarity and the second similarity based on the material physicochemical weight coefficient and the aging test weight coefficient to obtain the first physicochemical correlation data.

[0018] Specifically, the determination of the second physicochemical correlation data based on the environmental state historical data, the pavement structure position data, and the historical aging grade data comprises:

[0019] determine an environmental factor weight coefficient based on the environmental state historical data, wherein the environmental factor weight coefficient is used to represent a driving degree of an environmental condition of the asphalt pavement on aging;

[0020] determine a structure position weight coefficient based on the pavement structure position data, wherein the structure position weight coefficient is used to represent a sensitive degree of different depths of the asphalt pavement on aging;

[0021] determine the second physicochemical correlation data based on the environmental state historical data, the pavement structure position data, the historical aging grade data, the environmental factor weight coefficient, and the structure position weight coefficient.

[0022] Specifically, the determining the second physicochemical correlation data based on the environmental state historical data, the pavement structure position data, the historical aging grade data, the environmental factor weight coefficient, and the structure position weight coefficient comprises:

[0023] calculate a first difference degree between the environmental state historical data and the historical aging grade data;

[0024] calculate a second difference degree between the pavement structure position data and the historical aging grade data;

[0025] perform inverse processing and normalization processing on the first difference degree and the second difference degree respectively in sequence to obtain a first calculation result and a second calculation result;

[0026] perform weighted summation processing on the first calculation result and the second calculation result based on the environmental factor weight coefficient and the structure position weight coefficient to obtain the second physicochemical correlation data.

[0027] Specifically, the performing physicochemical evaluation on the asphalt pavement based on the first physicochemical correlation data and the second physicochemical correlation data to obtain a target evaluation result comprises:

[0028] input the first physicochemical correlation data and the second physicochemical correlation data into a preset physicochemical evaluation model to output the target evaluation result, wherein the target evaluation result comprises an aging stage classification result corresponding to the asphalt pavement and a physicochemical index decay rate corresponding to the asphalt pavement.

[0029] Specifically, the preset physicochemical evaluation model comprises an input layer, a feature processing layer, a fusion layer, a deep extraction layer, and an output layer.

[0030] the inputting the first physicochemical correlation data and the second physicochemical correlation data into a preset physicochemical evaluation model to output the target evaluation result comprises:

[0031] The first physicochemical correlation data input vector and the second physicochemical correlation data input vector are obtained according to the first physicochemical correlation data and the second physicochemical correlation data through the input layer.

[0032] The first high-dimensional feature vector and the second high-dimensional feature vector are obtained according to the first physicochemical correlation data input vector and the second physicochemical correlation data input vector through the feature processing layer.

[0033] The fusion feature vector is obtained according to the first high-dimensional feature vector and the second high-dimensional feature vector through the fusion layer.

[0034] The deep feature vector is obtained according to the fusion feature vector through the deep extraction layer.

[0035] The target evaluation result is obtained according to the deep feature vector through the output layer.

[0036] To achieve the above object, the present application further provides a physicochemical evaluation device for asphalt pavement successive aging, which comprises:

[0037] A first unit is configured to acquire physicochemical historical data of asphalt material corresponding to the asphalt pavement, historical data of aging test corresponding to the asphalt pavement, historical aging grade data corresponding to the asphalt pavement, historical environmental state data corresponding to the asphalt pavement, and pavement structure position data corresponding to the asphalt pavement.

[0038] A second unit is configured to determine first physicochemical correlation data based on the physicochemical historical data of asphalt material, the historical data of aging test, and the historical aging grade data, wherein the first physicochemical correlation data is used to represent the correlation between the intrinsic physicochemical properties of asphalt material of the asphalt pavement, the aging test data, and the successive aging process.

[0039] A third unit is configured to determine second physicochemical correlation data based on the historical environmental state data, the pavement structure position data, and the historical aging grade data, wherein the second physicochemical correlation data is used to represent the correlation between the external environmental factors, the pavement structure position, and the successive aging process of the asphalt pavement.

[0040] A fourth unit is configured to perform physicochemical evaluation on the asphalt pavement based on the first physicochemical correlation data and the second physicochemical correlation data to obtain a target evaluation result.

[0041] To achieve the above object, the present application further provides a terminal comprising a memory storing a plurality of instructions; and a processor loading the instructions from the memory to execute the steps in any of the methods provided by the present application.

[0042] To achieve the above object, the application further provides a medium storing a plurality of instructions adapted to be loaded by a processor to execute the steps in any method provided by the application.

[0043] The asphalt pavement successive aging physicochemical evaluation method, device, terminal and medium provided by the application can first determine first physicochemical correlation data based on physicochemical historical data of asphalt materials, aging test historical data and historical aging grade data; determine second physicochemical correlation data based on environmental state historical data, pavement structure position data and historical aging grade data; and perform physicochemical evaluation on the asphalt pavement based on the first physicochemical correlation data and the second physicochemical correlation data to obtain a target evaluation result, so as to realize accurate physicochemical evaluation on the asphalt pavement successive aging. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 Flowchart of the method provided by the embodiment of the application;

[0045] Figure 2 Application scenario block diagram of the preset physicochemical evaluation model provided by the embodiment of the application;

[0046] Figure 3 Structural schematic diagram of the asphalt pavement successive aging physicochemical evaluation device provided by the embodiment of the application;

[0047] Figure 4 Structural schematic diagram of the terminal provided by the embodiment of the application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0049] The traditional physicochemical evaluation technology for asphalt pavement aging has the following deficiencies: the existing technology is mostly based on “physicochemical data of asphalt materials” or “single aging test data” (such as RTFOT / PAV test results) as the evaluation basis, and does not integrate environmental state and pavement structure position data; the existing technology mostly outputs evaluation results (such as only determining the aging stage) through “manual statistics” or “simple regression model”, and cannot adapt to the gradualness of successive aging, and is difficult to support fine maintenance decision-making; at the same time, the existing technology mostly relies on the experience of technical personnel to make judgments and decisions, and cannot avoid the deviation in subjective judgment or decision-making.

[0050] Therefore, the embodiment of the present application provides a physical and chemical evaluation method, device, terminal and medium for the successive aging of asphalt pavement to solve the actual technical problem.

[0051] In some embodiments, the device can be integrated in an electronic terminal, which can be a terminal, a server, etc.

[0052] In some embodiments, the server can also be implemented in the form of a terminal.

[0053] The server can be a stand-alone physical server, a server cluster or a distributed system composed of multiple physical servers, a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms.

[0054] The terminal can be a smartphone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected through wired or wireless communication, which is not limited in the present application.

[0055] The following will be described in detail. It should be noted that the serial numbers of the following embodiments do not limit the preferred order of the embodiments.

[0056] The embodiment of the present application provides a physical and chemical evaluation method, device, terminal and medium for the successive aging of asphalt pavement, aiming to realize accurate physical and chemical evaluation of the successive aging of asphalt pavement through multi-dimensional data.

[0057] In some embodiments, a provincial highway G1 section (four-lane in both directions, opened to traffic in 2019, using SBS modified asphalt pavement) has been in service for 5 years until 2024, and the maintenance department needs to perform physical and chemical evaluation of the successive aging of the asphalt pavement in the K100-K105 interval of the section to determine the current aging stage (initial / middle / late) and the core physical and chemical index decline rate, and provide a basis for the development of the maintenance plan in 2025.

[0058] As Figure 1 , the specific process of the method can include:

[0059] S110, acquiring asphalt material physical and chemical history data corresponding to the asphalt pavement, aging test history data corresponding to the asphalt pavement, historical aging grade data corresponding to the asphalt pavement, environmental state history data corresponding to the asphalt pavement, and pavement structure position data corresponding to the asphalt pavement.

[0060] In some embodiments, the following data can be collected through the road section maintenance archives, laboratory test reports, and Internet of Things monitoring systems (assuming a time span of 2019-2024, with detection once a year):

[0061] Physicochemical historical data of asphalt materials: can include four components (asphaltene / gum / aromatic fraction / saturated fraction, unit: %), conventional indicators (penetration: dmm, softening point: ℃, ductility: cm);

[0062] Historical data of aging tests: RTFOT short-term aging (163℃, 85min), post-penetration retention rate (%), PAV long-term aging (100℃, 20h), post-carbonyl index;

[0063] Historical aging grade data: calibration threshold: initial aging (penetration drop rate ≤ 30%, carbonyl index ≤ 0.3); intermediate (30% < drop rate ≤ 60%, 0.3 < index ≤ 0.6); late (drop rate > 60%, index > 0.6);

[0064] Historical data of environmental conditions: average annual ultraviolet radiation intensity (W / m²), average annual temperature fluctuation amplitude (℃), average annual precipitation (mm);

[0065] Pavement structure location data: pavement structure: surface layer (4cm, AC-13), middle layer (6cm, AC-20), bottom layer (8cm, AC-25); annual aging rate of each layer (penetration annual drop rate: % / year).

[0066] S120, determining first physicochemical correlation data based on the physicochemical historical data of the asphalt material, the historical data of the aging tests, and the historical aging grade data, wherein the first physicochemical correlation data is used to represent the correlation between the intrinsic physicochemical properties of the asphalt material, the aging test data, and the successive aging process of the asphalt pavement.

[0067] In some embodiments, determining the first physicochemical correlation data based on the physicochemical historical data of the asphalt material, the historical data of the aging tests, and the historical aging grade data includes the steps of S121 to S123 as shown below:

[0068] S121, determining a material physicochemical weight coefficient based on the physicochemical historical data of the asphalt material, wherein the material physicochemical weight coefficient is used to represent the influence degree of physicochemical indicators of the asphalt pavement on the aging of the asphalt pavement.

[0069] In some embodiments, the analytic hierarchy process (AHP) can be used to score the "aging influence degree" of the four indicators "four components, penetration, softening point, and ductility", construct a judgment matrix, and calculate the weight:

[0070] Four components (asphaltene): 0.35;

[0071] Penetration: 0.25;

[0072] Softening point: 0.20;

[0073] Ductility: 0.20.

[0074] Weighted sum: material physicochemical weight coefficient = 0.6, used to represent the influence degree of physicochemical indexes of asphalt pavement on aging of asphalt pavement (after normalization, the actual calculation can be distributed according to the weight of each index). The larger the value is, the greater the influence degree of physicochemical indexes of asphalt material on aging of asphalt pavement is. For example, the weight of asphaltene (0.35) is greater than that of other indexes, indicating that it has the most significant influence on the aging process.

[0075] S122, determining an aging test weight coefficient based on the aging test historical data, wherein the aging test weight coefficient is used to represent the performance ability of historical test data of asphalt pavement on aging stage.

[0076] In some embodiments, the entropy method can be used to calculate the information entropy of RTFOT and PAV test data (the smaller the entropy value is, the higher the index differentiation degree is, and the greater the weight is):

[0077] RTFOT penetration retention rate: information entropy 0.82, weight 0.4;

[0078] PAV carbonyl index: information entropy 0.65, weight 0.6.

[0079] Weighted sum: aging test weight coefficient = 0.4 (after normalization), used to represent the performance ability of historical test data of asphalt pavement on aging stage. The larger the value is, the stronger the performance ability of historical test data on aging stage is. For example, the weight of PAV carbonyl index (0.6) is greater than that of RTFOT penetration retention rate (0.4), indicating that the PAV test data has stronger differentiation ability on aging stage.

[0080] S123, determining the first physicochemical correlation data based on the asphalt material physicochemical historical data, the aging test historical data, the historical aging grade data, the material physicochemical weight coefficient, and the aging test weight coefficient.

[0081] In some embodiments, the determination of the first physicochemical correlation data based on the asphalt material physicochemical historical data, the aging test historical data, the historical aging grade data, the material physicochemical weight coefficient, and the aging test weight coefficient includes the steps of S1231 to S1233 as shown below:

[0082] S1231, calculate a first similarity between the asphalt material physical and chemical history data and the historical aging grade data.

[0083] In some embodiments, a vector can be constructed according to the asphalt material physical and chemical history data and the historical aging grade data:

[0084] Asphalt material physical and chemical vector X (2024 data): [asphaltene 12%, penetration 45 dmm, softening point 62℃, ductility 18 cm];

[0085] Historical aging grade vector Z (intermediate aging threshold): [asphaltene 10%, penetration 50 dmm, softening point 60℃, ductility 20 cm];

[0086] Cosine similarity formula: S_1 = X * Z / ||X|| * ||Z||;

[0087] Vector dot product: 12x10 + 45x50 + 62x60 + 18x20 = 120 + 2250 + 3720 + 360 = 6450

[0088] Vector length: ||X|| = √(12² + 45² + 62² + 18²) = √(144 + 2025 + 3844 + 324) = √6337 ≈ 79.6; ||Z|| = √(10² + 50² + 60² + 20²) = √(100 + 2500 + 3600 + 400) = √6600 ≈ 81.2; where √ denotes the arithmetic square root of the object.

[0089] Then the first similarity S_1 = 6450 / 79.6x81.2 ≈ 0.998.

[0090] S1232, calculate a second similarity between the aging test history data and the historical aging grade data.

[0091] In some embodiments, a vector can be constructed according to the aging test history data and the historical aging grade data:

[0092] Aging test vector Y (2024 data): [RTFOT penetration retention rate 75%, PAV carbonyl index 0.45];

[0093] Historical aging grade vector Z' (intermediate aging threshold): [RTFOT retention rate ≥ 70%, PAV index 0.4].

[0094] Cosine similarity calculation, i.e. calculation of the second similarity: S_2 = (75x70 + 0.45x0.4) / √(75² + 0.45²) x √(70² + 0.4²) ≈ 0.999; where √ denotes the arithmetic square root of the object.

[0095] S1233、based on the material physicochemical weight coefficient and the aging test weight coefficient, performing weighted sum processing on the first similarity and the second similarity to obtain the first physicochemical correlation data.

[0096] In some embodiments, the first physicochemical correlation data is obtained as R_1=0.6×0.998+0.4×0.999≈0.599+0.399≈0.998. That is, R_1 is close to 1, indicating that the correlation between “material physicochemical properties + aging test data” and “intermediate aging” is extremely strong.

[0097] S130, based on the environmental state historical data, the pavement structure position data, and the historical aging grade data, determining second physicochemical correlation data, wherein the second physicochemical correlation data is used to represent the correlation between external environmental factors, pavement structure position, and the aging process of asphalt pavement.

[0098] In some embodiments, the determination of the second physicochemical correlation data based on the environmental state historical data, the pavement structure position data, and the historical aging grade data includes the steps of S131 to S133 as shown below:

[0099] S131, based on the environmental state historical data, determining an environmental factor weight coefficient, wherein the environmental factor weight coefficient is used to represent the driving degree of the environmental conditions of asphalt pavement on aging.

[0100] In some embodiments, the Pearson correlation coefficient method is used to calculate the correlation between the environmental data from 2019 to 2024 and the “penetration drop rate”, the higher the correlation, the greater the weight, and the environmental factor weight coefficient=0.5 is obtained, which is used to represent the driving degree of the environmental conditions of asphalt pavement on aging. The larger this value, the stronger the driving effect of environmental conditions (such as ultraviolet rays, temperature, and precipitation) on the aging of asphalt pavement. The weight 0.5 indicates that the environmental factors have a significant impact on the aging process.

[0101] S132, based on the pavement structure position data, determining a structure position weight coefficient, wherein the structure position weight coefficient is used to represent the sensitivity of different depths of asphalt pavement to aging.

[0102] In some embodiments, the distance decay model is used to allocate weights according to “the surface layer is the most serious, and the bottom layer is the least”:

[0103] Surface layer: 0.5, directly exposed to the environment, and the fastest aging;

[0104] Middle layer: 0.3, less affected by the environment, and the aging rate is moderate;

[0105] The bottom layer (0.2) is protected by both environmental and load conditions, resulting in the slowest aging.

[0106] The weighted summation yields a structural location weight coefficient of 0.5, which characterizes the sensitivity of different depths of asphalt pavement to aging. A larger value indicates a higher sensitivity of that structural location to aging. For example, the surface layer has the largest weight (0.5), indicating that the surface layer is most sensitive to aging, while the bottom layer (0.2) is the least sensitive.

[0107] S133. Based on the historical environmental status data, the road surface structure location data, the historical aging level data, the environmental factor weighting coefficient, and the structure location weighting coefficient, determine the second physicochemical correlation data.

[0108] In some embodiments, determining the second physicochemical correlation data based on the historical environmental state data, the pavement structure location data, the historical aging level data, the environmental factor weighting coefficient, and the structure location weighting coefficient includes the following steps S1331 to S1334:

[0109] S1331. Calculate the first degree of difference between the historical environmental state data and the historical aging level data.

[0110] In some embodiments, a time series can be constructed based on the historical environmental state data and the historical aging level data:

[0111] Environmental state sequence: E (UV intensity 2019-2024, unit: W / m²): [180,185,190,195,200,205];

[0112] Historical aging level sequence: Z_env (UV threshold sequence corresponding to mid-term aging, W / m²): [190,192,195,198,200,202].

[0113] The DTW algorithm can be used to calculate the distance: by matching the two sequences through dynamic programming, the first difference D_e = 8.2 is finally obtained (the smaller the value, the higher the matching degree between the environment and the mid-term aging).

[0114] S1332. Calculate the second degree of difference between the road surface structure location data and the historical aging level data.

[0115] In some embodiments, a time series can be constructed based on the pavement structure location data and the historical aging level data:

[0116] Structural location sequence: S (annual decrease rate of surface penetration from 2019 to 2024, % / year): [5,6,7,8,9,10];

[0117] Historical aging grade sequence: Z_space (threshold sequence of surface subsidence rate corresponding to medium-term aging, % / year): [6, 7, 8, 9, 10, 11].

[0118] The DTW distance calculation can be adopted: the second difference degree D_s=6.5 is obtained.

[0119] S1333, respectively, the first difference degree, the second difference degree is taken inverse processing, normalization processing, to obtain the first calculation result and the second calculation result.

[0120] In some embodiments, the first calculation result (environmental correlation degree): Norm (1 / D_e)=Norm (1 / 8.2)=Norm (0.122)=0.45; the second calculation result (structure correlation degree): Norm (1 / D_s)=Norm (1 / 6.5)=Norm (0.154)=0.58.

[0121] S1334, based on the environmental factor weight coefficient and the structure position weight coefficient, the first calculation result and the second calculation result are weighted and summed to obtain the second physicochemical correlation data.

[0122] In some embodiments, the second physicochemical correlation data R_2=0.5×0.45+0.5×0.58=0.225+0.29=0.515 is obtained by substituting the data, R_2>0.5, indicating that "environment + structure position" and "medium-term aging" are significantly related.

[0123] S140, based on the first physicochemical correlation data and the second physicochemical correlation data, the asphalt pavement is physicochemically evaluated to obtain a target evaluation result.

[0124] In some embodiments, the asphalt pavement is physicochemically evaluated based on the first physicochemical correlation data and the second physicochemical correlation data to obtain a target evaluation result, including the specific implementation process as follows:

[0125] The first physicochemical correlation data and the second physicochemical correlation data are input into a preset physicochemical evaluation model, and the target evaluation result is output, wherein the target evaluation result includes an aging stage classification result corresponding to the asphalt pavement and a physicochemical index degradation rate corresponding to the asphalt pavement.

[0126] In some embodiments, as Figure 2 , the preset physicochemical evaluation model includes an input layer, a feature processing layer, a fusion layer, a deep extraction layer, and an output layer.

[0127] Specifically, the first physicochemical correlation data and the second physicochemical correlation data are input into a preset physicochemical evaluation model to output the target evaluation result, including the following steps S141 to S145:

[0128] S141, obtaining a first physicochemical correlation data input vector and a second physicochemical correlation data input vector according to the first physicochemical correlation data and the second physicochemical correlation data through the input layer.

[0129] In some embodiments, R_1 (0.9986) and R_2 (0.515) are converted into vectors:

[0130] The first physicochemical correlation data input vector is [0.9986, 0.999 (S2), 0.35 (asphaltene weight),...] (dimension: 8 dimensions, including core intermediate parameters);

[0131] The second physicochemical correlation data input vector is [0.515, 0.45 (environmental correlation degree), 0.58 (structural correlation degree),...] (dimension: 8 dimensions).

[0132] S142, obtaining a first high-dimensional feature vector and a second high-dimensional feature vector according to the first physicochemical correlation data input vector and the second physicochemical correlation data input vector through the feature processing layer.

[0133] In some embodiments, the 8-dimensional input vector is mapped to a 128-dimensional high-dimensional feature vector through a fully connected layer (ReLU activation function) in the feature processing layer:

[0134] The first high-dimensional feature vector is [0.82, 0.75, 0.91,..., 0.88] (128 dimensions);

[0135] The second high-dimensional feature vector is [0.55, 0.62, 0.48,..., 0.53] (128 dimensions).

[0136] S143, obtaining a fusion feature vector according to the first high-dimensional feature vector and the second high-dimensional feature vector through the fusion layer.

[0137] In some embodiments, the attention mechanism in the fusion layer is adopted to assign weights (0.65 and 0.35) according to the correlation intensity of R_1 (0.9986) and R_2 (0.515), and the weighted fusion feature vector is obtained: [0.82×0.65+0.55×0.35, 0.75×0.65+0.62×0.35,...] (128 dimensions).

[0138] S144, obtaining a deep feature vector according to the fusion feature vector through the deep extraction layer.

[0139] In some embodiments, the "aging trend from 2019 to 2024" in the fusion feature vector is dynamically extracted through an LSTM network layer (2 layers, 64 hidden units) in the deep extraction layer, and the output is: deep feature vector: [0.78, 0.85, 0.69,..., 0.81] (64 dimensions).

[0140] S145, obtaining the target evaluation result according to the deep feature vector through the output layer.

[0141] In some embodiments, the output layer adopts softmax activation (classification) + linear activation (regression), and the double-task output is obtained:

[0142] Aging stage classification result: mid-term aging (probability: 92.3%);

[0143] Physicochemical index decline rate: penetration decrease rate 45.2%, carbonyl index growth rate 120.5%, ductility retention rate 40.3%.

[0144] Therefore, the K100-K105 section of the expressway G1 is currently in the mid-term aging stage, and the core physicochemical index decline is consistent with the mid-term characteristics; it is recommended to use the preventive maintenance scheme of "micro-surfacing + asphalt restorative agent spraying" in 2025 to delay the aging process to the later stage (expected to extend the service period by 3-4 years).

[0145] In summary, the present application provides a physicochemical evaluation method for successive aging of asphalt pavement, which realizes accurate physicochemical evaluation of successive aging of asphalt pavement through multi-dimensional data.

[0146] In order to better implement the above method, the present application also provides a physicochemical evaluation device for successive aging of asphalt pavement, which can be integrated in an electronic terminal. The electronic terminal can be a terminal, a server, etc. The terminal can be a mobile phone, a tablet computer, a smart Bluetooth terminal, a notebook computer, a personal computer, etc. The server can be a single server or a server cluster composed of multiple servers.

[0147] For example, in this embodiment, the physicochemical evaluation device for successive aging of asphalt pavement is integrated in a terminal.

[0148] For example, as shown in Figure 3 The physicochemical evaluation device for successive aging of asphalt pavement 300 can include a first unit 301, a second unit 302, a third unit 303, and a fourth unit 304, and the device includes:

[0149] The first unit 301 is configured to acquire asphalt material physical and chemical history data corresponding to the asphalt pavement, aging test history data corresponding to the asphalt pavement, historical aging grade data corresponding to the asphalt pavement, environmental state history data corresponding to the asphalt pavement, and pavement structure position data corresponding to the asphalt pavement.

[0150] The second unit 302 is configured to determine first physical and chemical correlation data based on the asphalt material physical and chemical history data, the aging test history data, and the historical aging grade data, wherein the first physical and chemical correlation data is used to represent the correlation between the intrinsic physical and chemical properties of the asphalt material of the asphalt pavement, the aging test data, and the successive aging process.

[0151] The third unit 303 is configured to determine second physical and chemical correlation data based on the environmental state history data, the pavement structure position data, and the historical aging grade data, wherein the second physical and chemical correlation data is used to represent the correlation between the external environmental factors, the pavement structure position, and the successive aging process of the asphalt pavement.

[0152] The fourth unit 304 is configured to perform physical and chemical evaluation on the asphalt pavement based on the first physical and chemical correlation data and the second physical and chemical correlation data to obtain a target evaluation result.

[0153] In specific implementation, each of the above units can be implemented as an independent entity, or can be combined as the same or several entities. The specific implementation of each of the above units can be referred to the method embodiments above, and will not be described here.

[0154] As can be seen from the above, the embodiments of the present application can realize accurate physical and chemical evaluation of the successive aging of the asphalt pavement through multi-dimensional data.

[0155] The embodiments of the present application also provide an electronic terminal, which can be a terminal, a server, etc. The terminal can be a mobile phone, a tablet computer, a smart Bluetooth terminal, a notebook computer, a personal computer, etc. The server can be a single server or a server cluster composed of multiple servers, etc.

[0156] In some embodiments, the product processing device can also be integrated in multiple electronic terminals, for example, the product processing device can be integrated in multiple servers to realize the method for physical and chemical evaluation of the successive aging of the asphalt pavement according to the present application by the multiple servers.

[0157] In the present embodiment, the electronic terminal of the present embodiment will be taken as a terminal as an example for detailed description, for example, as shown in FIG. 4, which shows a structural schematic diagram of the terminal 400 according to the present embodiment, in particular: Figure 4

[0158] ​The terminal 400 can include a processor 401 having one or more processing cores, a memory 402 having one or more mediums, a power supply 403, an input module 404, a communication module 405, and the like. Those skilled in the art can understand that Figure 4 The terminal 400 structure shown in the figure is not a limitation on the terminal 400, and can include more or fewer components than shown, or combine certain components, or different component arrangements. Among them:

[0159] The processor 401 is the control center of the terminal 400, which connects all parts of the terminal 400 through various interfaces and lines, executes various functions of the terminal 400 and processes data by running or executing software programs and / or modules stored in the memory 402, and calling data stored in the memory 402, thereby overall monitoring the terminal 400. In some embodiments, the processor 401 can include one or more processing cores; in some embodiments, the processor 401 can integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application program, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 401.

[0160] The memory 402 can be used to store software programs and modules, and the processor 401 executes various functions and data processing by running the software programs and modules stored in the memory 402. The memory 402 can mainly include a program storage area and a data storage area, wherein the program storage area can store the operating system, at least one application program required by the function (such as sound playing function, image playing function, etc.), etc.; the data storage area can store data created according to the use of the terminal 400, etc. In addition, the memory 402 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory 402 can also include a memory controller to provide access for the processor 401 to the memory 402.

[0161] The terminal 400 also includes a power supply 403 for powering various components. In some embodiments, the power supply 403 can be logically connected to the processor 401 through a power management system, so as to realize the functions of managing charging, discharging, and power consumption management, etc. through the power management system. The power supply 403 can also include one or more direct or alternating current power supplies, recharging systems, power supply failure detection circuits, power supply converters or inverters, power supply status indicators, and the like.

[0162] The terminal 400 can also include an input module 404, which can be used to receive input digital or character information, and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0163] The terminal 400 can also include a communication module 405, which in some embodiments can include a wireless module through which the terminal 400 can perform short-range wireless transmission, thereby providing the user with wireless broadband Internet access. For example, the communication module 405 can be used to help the user send and receive emails, browse web pages, and access streaming media, etc.

[0164] Although not shown, the terminal 400 can also include a display unit, etc., which will not be described here. In particular, in the present embodiment, the processor 401 in the terminal 400 will load the executable file corresponding to the process of one or more application programs into the memory 402 according to the following instructions, and run the application program stored in the memory 402 by the processor 401, thereby realizing various functions, such as:

[0165] Obtaining physical and chemical historical data of asphalt material corresponding to the asphalt pavement, historical data of aging test corresponding to the asphalt pavement, historical aging grade data of the asphalt pavement, historical environmental state data of the asphalt pavement, and pavement structure position data of the asphalt pavement;

[0166] Determining first physical and chemical correlation data based on the physical and chemical historical data, the historical aging test data, and the historical aging grade data, wherein the first physical and chemical correlation data is used to represent the correlation between the intrinsic physical and chemical properties of the asphalt material of the asphalt pavement, the aging test data, and the successive aging process;

[0167] Determining second physical and chemical correlation data based on the historical environmental state data, the pavement structure position data, and the historical aging grade data, wherein the second physical and chemical correlation data is used to represent the correlation between the external environmental factors, the pavement structure position, and the successive aging process of the asphalt pavement;

[0168] Performing physical and chemical evaluation on the asphalt pavement based on the first physical and chemical correlation data and the second physical and chemical correlation data, to obtain a target evaluation result.

[0169] As can be seen from the above, the embodiments of the present application can realize precise physical and chemical evaluation of the successive aging of the asphalt pavement through multi-dimensional data.

[0170] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions or by controlling relevant hardware by instructions, which can be stored in a medium and loaded and executed by a processor.

[0171] To this end, an embodiment of the present application provides a medium, in which a plurality of instructions are stored, which can be loaded by a processor to execute the steps in any of the physico-chemical evaluation methods of the successive aging of asphalt pavement provided by the embodiments of the present application. For example, the instructions can execute the following steps:

[0172] obtaining physico-chemical history data of asphalt material corresponding to the asphalt pavement, aging test history data corresponding to the asphalt pavement, historical aging grade data corresponding to the asphalt pavement, environmental state history data corresponding to the asphalt pavement, and pavement structure position data corresponding to the asphalt pavement;

[0173] determining first physico-chemical correlation data based on the physico-chemical history data, the aging test history data, and the historical aging grade data, wherein the first physico-chemical correlation data is used to represent the correlation between the intrinsic physico-chemical properties of the asphalt material of the asphalt pavement, the aging test data, and the successive aging process;

[0174] determining second physico-chemical correlation data based on the environmental state history data, the pavement structure position data, and the historical aging grade data, wherein the second physico-chemical correlation data is used to represent the correlation between the external environmental factors, the pavement structure position, and the successive aging process of the asphalt pavement;

[0175] performing physico-chemical evaluation on the asphalt pavement based on the first physico-chemical correlation data and the second physico-chemical correlation data to obtain a target evaluation result.

[0176] The medium can include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, or the like.

[0177] According to an aspect of the present application, a computer program product or computer program is provided, which includes computer instructions stored in a medium. A processor of a computer terminal reads the computer instructions from the medium, and the processor executes the computer instructions to enable the computer terminal to perform the method provided in the various optional implementations provided in the above embodiments.

[0178] Due to the instructions stored in the medium, the steps in any one of the physicochemical evaluation methods for the successive aging of the asphalt pavement provided in the embodiments of the present application can be executed, thus the beneficial effects that can be achieved by any one of the physicochemical evaluation methods for the successive aging of the asphalt pavement provided in the embodiments of the present application can be achieved, which will be described in detail in the foregoing embodiments, and will not be described here again.

[0179] The foregoing has described in detail the physicochemical evaluation method, device, terminal and medium provided in the embodiments of the present application, the principles and implementation manners of the present application have been described by applying specific examples, the foregoing embodiment description is only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will have changes, and the foregoing description should not be understood as limiting the present application.

Claims

1. A method for physico-chemical evaluation of successive aging of asphalt pavement, characterized by, The method comprises: acquiring asphalt pavement corresponding asphalt material physical and chemical history data, asphalt pavement corresponding aging test history data, asphalt pavement corresponding historical aging grade data, asphalt pavement corresponding environmental state history data, and asphalt pavement corresponding pavement structure position data; determining first physical and chemical correlation data based on the asphalt material physical and chemical history data, the aging test history data, and the historical aging grade data, wherein the first physical and chemical correlation data is used to represent the correlation between the intrinsic physical and chemical properties of the asphalt material of the asphalt pavement, the aging test data, and the successive aging process; determining second physical and chemical correlation data based on the environmental state history data, the pavement structure position data, and the historical aging grade data, wherein the second physical and chemical correlation data is used to represent the correlation between the external environmental factors, the pavement structure position, and the successive aging process of the asphalt pavement; performing physical and chemical evaluation on the asphalt pavement based on the first physical and chemical correlation data and the second physical and chemical correlation data to obtain a target evaluation result; determining the first physical and chemical correlation data based on the asphalt material physical and chemical history data, the aging test history data, and the historical aging grade data comprises: determining a material physical and chemical weight coefficient based on the asphalt material physical and chemical history data, wherein the material physical and chemical weight coefficient is used to represent the influence degree of the physical and chemical indicators of the asphalt pavement on the aging of the asphalt pavement; determining an aging test weight coefficient based on the aging test history data, wherein the aging test weight coefficient is used to represent the performance ability of the historical test data of the asphalt pavement on the aging stage; determining the first physical and chemical correlation data based on the asphalt material physical and chemical history data, the aging test history data, the historical aging grade data, the material physical and chemical weight coefficient, and the aging test weight coefficient; determining the first physical and chemical correlation data based on the asphalt material physical and chemical history data, the aging test history data, the historical aging grade data, the material physical and chemical weight coefficient, and the aging test weight coefficient comprises: calculating a first similarity between the asphalt material physical and chemical history data and the historical aging grade data; calculating a second similarity between the aging test history data and the historical aging grade data; performing weighted sum processing on the first similarity and the second similarity based on the material physical and chemical weight coefficient and the aging test weight coefficient to obtain the first physical and chemical correlation data; determining the second physical and chemical correlation data based on the environmental state history data, the pavement structure position data, and the historical aging grade data comprises: determining an environmental factor weight coefficient based on the environmental state history data, wherein the environmental factor weight coefficient is used to represent the driving degree of the environmental conditions of the asphalt pavement on the aging; determining a structure position weight coefficient based on the pavement structure position data, wherein the structure position weight coefficient is used to represent the sensitivity degree of different depths of the asphalt pavement on the aging; determine the second physicochemical correlation data based on the environmental state historical data, the pavement structure position data, the historical aging grade data, the environmental factor weight coefficient, and the structure position weight coefficient; The second physicochemical correlation data is determined based on the environmental state historical data, the pavement structure position data, the historical aging grade data, the environmental factor weight coefficient, and the structure position weight coefficient, comprising: a first difference degree between the environmental state historical data and the historical aging grade data is calculated; a second difference degree between the pavement structure position data and the historical aging grade data is calculated; The first difference degree and the second difference degree are respectively and sequentially subjected to reciprocal processing and normalization processing to obtain first calculation results and second calculation results; The first calculation results and the second calculation results are weighted and summed based on the environmental factor weight coefficient and the structure position weight coefficient to obtain the second physicochemical correlation data; The first physicochemical correlation data and the second physicochemical correlation data are input into a preset physicochemical evaluation model to output the target evaluation result, wherein the target evaluation result includes an aging stage classification result corresponding to the asphalt pavement and a physicochemical index degradation rate corresponding to the asphalt pavement. The preset physicochemical evaluation model includes an input layer, a feature processing layer, a fusion layer, a deep extraction layer, and an output layer; 2. The method of claim 1, wherein, The first physicochemical correlation data and the second physicochemical correlation data are input into a preset physicochemical evaluation model to output the target evaluation result, comprising: Through the input layer, the first physicochemical correlation data and the second physicochemical correlation data are obtained to obtain a first physicochemical correlation data input vector and a second physicochemical correlation data input vector; Through the feature processing layer, the first physicochemical correlation data input vector and the second physicochemical correlation data input vector are obtained to obtain a first high-dimensional feature vector and a second high-dimensional feature vector; Through the fusion layer, the first high-dimensional feature vector and the second high-dimensional feature vector are obtained to obtain a fusion feature vector; Through the deep extraction layer, the fusion feature vector is obtained to obtain a deep feature vector; Through the output layer, the deep feature vector is obtained to obtain the target evaluation result. The device comprises:

3. A device for physico-chemical evaluation of successive aging of asphalt pavement, characterized by, A first unit for obtaining asphalt material physicochemical historical data corresponding to the asphalt pavement, aging test historical data corresponding to the asphalt pavement, historical aging grade data corresponding to the asphalt pavement, environmental state historical data corresponding to the asphalt pavement, and pavement structure position data corresponding to the asphalt pavement; A second unit for determining first physicochemical correlation data based on the asphalt material physicochemical historical data, the aging test historical data, and the historical aging grade data, wherein the first physicochemical correlation data is used to represent the correlation between the intrinsic physicochemical properties of the asphalt material of the asphalt pavement, the aging test data, and the aging process. ​ The third unit is configured to determine second physicochemical correlation data based on the environmental state historical data, the pavement structure position data, and the historical aging grade data, where the second physicochemical correlation data is used to represent the correlation between external environmental factors, pavement structure positions, and the aging process of the asphalt pavement. The fourth unit is configured to perform physicochemical evaluation on the asphalt pavement based on the first physicochemical correlation data and the second physicochemical correlation data to obtain a target evaluation result. The determination of the first physicochemical correlation data based on the asphalt material physicochemical historical data, the aging test historical data, and the historical aging grade data includes: determining a material physicochemical weight coefficient based on the asphalt material physicochemical historical data, where the material physicochemical weight coefficient is used to represent the influence degree of physicochemical indexes of the asphalt pavement on the aging of the asphalt pavement; determining an aging test weight coefficient based on the aging test historical data, where the aging test weight coefficient is used to represent the performance ability of the historical test data of the asphalt pavement on the aging stage; determining the first physicochemical correlation data based on the asphalt material physicochemical historical data, the aging test historical data, the historical aging grade data, the material physicochemical weight coefficient, and the aging test weight coefficient; The determination of the first physicochemical correlation data based on the asphalt material physicochemical historical data, the aging test historical data, the historical aging grade data, the material physicochemical weight coefficient, and the aging test weight coefficient includes: calculating a first similarity between the asphalt material physicochemical historical data and the historical aging grade data; calculating a second similarity between the aging test historical data and the historical aging grade data; performing weighted sum processing on the first similarity and the second similarity based on the material physicochemical weight coefficient and the aging test weight coefficient to obtain the first physicochemical correlation data; The determination of the second physicochemical correlation data based on the environmental state historical data, the pavement structure position data, and the historical aging grade data includes: determining an environmental factor weight coefficient based on the environmental state historical data, where the environmental factor weight coefficient is used to represent the driving degree of the environmental conditions of the asphalt pavement on the aging; determining a structure position weight coefficient based on the pavement structure position data, where the structure position weight coefficient is used to represent the sensitive degree of different depths of the asphalt pavement on the aging; determining the second physicochemical correlation data based on the environmental state historical data, the pavement structure position data, the historical aging grade data, the environmental factor weight coefficient, and the structure position weight coefficient; The determination of the second physicochemical correlation data based on the environmental state historical data, the pavement structure position data, the historical aging grade data, the environmental factor weight coefficient, and the structure position weight coefficient includes: calculating a first difference between the environmental state historical data and the historical aging grade data; calculate a second difference degree between the pavement structure position data and the historical aging grade data; respectively, the first difference degree, the second difference degree is taken inverse processing, normalization processing, obtains first calculation result and second calculation result; based on the environmental factor weight coefficient and the structure position weight coefficient, the first calculation result and the second calculation result are weighted and summed to obtain the second physicochemical correlation data; the first physicochemical correlation data and the second physicochemical correlation data are input into a preset physicochemical evaluation model, and the target evaluation result is output, wherein the target evaluation result includes an aging stage classification result corresponding to the asphalt pavement and a physicochemical index decline rate corresponding to the asphalt pavement. including a processor and a memory, the memory stores a plurality of instructions; the processor loads instructions from the memory to execute the steps in the method of any one of claims 1-2.

4. A terminal, characterized by comprising: The medium stores a plurality of instructions, and the instructions are suitable for being loaded by the processor to execute the steps in the method of any one of claims 1-2.

5. A medium characterized by, ​

Images