Asphalt pavement performance testing method, device, equipment and storage medium

Abstract

The present application discloses a performance testing method, device, equipment and storage medium for asphalt pavement. The method comprises determining a reference measurement area, collecting rutting depth data for preprocessing, calculating a rutting deformation index based on a rutting depth data sequence, and calculating a relative rutting resistance index using a comprehensive evaluation model in combination with asphalt mixture properties, pavement structure information and rutting deformation index, thereby improving the accuracy and efficiency of asphalt pavement performance testing.

Description

Technical Field

[0001] The present application relates to the field of road engineering technology, and in particular to a performance testing method, device, equipment and storage medium for an asphalt pavement. Background Art

[0002] With the rapid development of transportation infrastructure construction, asphalt pavement has been widely used due to its good driving comfort and maintenance convenience. However, during long-term use, asphalt pavement will be affected by various factors such as vehicle load and climatic conditions, resulting in the occurrence of defects such as rutting and cracks, which in turn affect the performance and safety of the pavement. Therefore, regular testing and evaluation of the performance of asphalt pavement and timely detection and treatment of potential defects are of great significance to ensure smooth roads and driving safety. Traditional asphalt pavement performance testing methods mostly rely on manual visual inspection and simple instrument measurement, which is not only inefficient, but also greatly affected by subjective factors, and it is difficult to accurately and comprehensively reflect the pavement performance status.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0004] The main purpose of the present application is to provide a method, device, equipment and storage medium for testing the performance of an asphalt pavement, so as to solve the problems in the prior art that the performance testing of an asphalt pavement is difficult, inefficient and seriously affected by subjective factors.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] A performance testing method for an asphalt pavement, comprising:

[0007] Determine the benchmark measurement area based on the highway grade and lane marking conditions;

[0008] Collecting rutting depth data in the reference measurement area;

[0009] Preprocessing the collected rutting depth data to obtain an accurate rutting depth data sequence, wherein the rutting depth data sequence includes a maximum rutting depth, a minimum rutting depth, and an average rutting depth;

[0010] Calculating a rutting deformation index based on the rutting depth data sequence according to formula (1);

[0011]

[0012] Where DI is the rutting deformation index, d max is the maximum rutting depth, d minis the minimum rutting depth, d min is the average rutting depth;

[0013] Based on the properties of the asphalt mixture, the pavement structure information and the rutting deformation index, a relative rutting resistance index is calculated using a comprehensive evaluation model, wherein the comprehensive evaluation model includes a hierarchical model;

[0014] Additional performance indicators of asphalt pavement are determined based on the relative rutting resistance index within the same reference measurement area.

[0015] As a further improvement of the present application, the method of calculating the relative rutting resistance index using the comprehensive evaluation model further includes:

[0016] The hierarchical model is established based on the properties of the asphalt mixture, the pavement structure information and the rutting deformation index, wherein the hierarchical model includes a scheme layer, a criterion layer and a target layer arranged in sequence from bottom to top, wherein the elements of the target layer include a relative rutting resistance index for evaluating the asphalt pavement, the elements of the criterion layer include the properties of the asphalt mixture, the pavement structure information and the rutting deformation index, and the elements of the scheme layer include a target road section for measurement;

[0017] Scaling each layer of the hierarchical model, respectively determining a relative index of each layer of the hierarchical model relative to a previous layer, and constructing a judgment matrix based on the relative index;

[0018] Generate the maximum eigenvalue of each judgment matrix and its corresponding eigenvector, wherein the eigenvector is the weight of each layer element relative to the previous layer element;

[0019] Performing a consistency check on each of the judgment matrices, wherein the consistency check includes calculating a consistency index and a consistency ratio of each of the judgment matrices;

[0020] Performing weight synthesis on each of the feature vectors to obtain the total weight of each layer element relative to the target layer;

[0021] The relative rutting resistance index is obtained based on the characteristic vector and the total weight of the elements of each layer relative to the target layer.

[0022] As a further improvement of the present application, the consistency check of each judgment matrix includes:

[0023] According to the elements of each judgment matrix, the consistency index of each judgment matrix is ​​calculated by formula (2), and the consistency index is used to measure the relative consistency between the elements in the judgment matrix;

[0024]

[0025] Among them, CI is the consistency index, λ max is the maximum eigenvalue of the judgment matrix, n is the order of the judgment matrix, including the number of rows or columns;

[0026] Finding an average random consistency index corresponding to the order of the judgment matrix, wherein the average random consistency index is an average value of consistency indexes of judgment matrices of the same order generated randomly for multiple times;

[0027] The ratio of the consistency index to the average random consistency index is used as a consistency ratio, and the consistency ratio is used to measure whether the consistency of the judgment matrix meets the requirements;

[0028] It is determined whether the consistency ratio is less than a preset consistency threshold, and if so, it is determined that the judgment matrix passes the consistency test.

[0029] As a further improvement of the present application, the relative rutting resistance index obtained based on the characteristic vector and the total weight of the elements of each layer relative to the target layer includes:

[0030] Obtaining an actual measurement value of each element of the criterion layer;

[0031] Determining a characteristic linear relationship of each element of the criterion layer based on the actual measurement value;

[0032] The relative anti-rutting ability index is calculated by formula (3);

[0033]

[0034] Where RRI is the relative rutting resistance index, w i is the weight of the i-th criterion layer element, x i is the characteristic linear relationship of the ith element of the criterion layer, f i (x i ) is the function value corresponding to the characteristic linear relationship of the ith element of the criterion layer, and m is the number of elements in the criterion layer.

[0035] As a further improvement of the present application, additional performance indicators of asphalt pavement measured based on the relative rutting resistance index in the same reference measurement area include:

[0036] In the reference measurement area, a plurality of measurement points reflecting the flatness condition in the reference measurement area are selected according to a predetermined grid method;

[0037] Performing flatness measurement at each selected measuring point to obtain flatness data of each measuring point;

[0038] According to the relative anti-rutting ability index, a flatness weight is allocated to each measuring point, wherein the flatness weight of each measuring point is proportional to the relative anti-rutting ability index;

[0039] The weighted flatness data of each measuring point is generated based on the flatness weight of each measuring point, and the overall flatness index in the reference measurement area is generated by a preset flatness algorithm.

[0040] As a further improvement of this application, it also includes:

[0041] Setting thresholds of the relative anti-rutting ability index and the flatness index;

[0042] comparing the actual relative rutting resistance index with the set relative rutting resistance index threshold, and comparing the actual flatness index with the set flatness index threshold;

[0043] If the actual relative rutting resistance index is lower than the threshold value, or the actual overall smoothness index is lower than the threshold value, it is determined that the asphalt pavement in the reference measurement area needs to be repaired;

[0044] According to the specific values ​​of the relative rutting resistance index and the smoothness index, combined with the additional performance indicators of the asphalt pavement, the additional performance indicators include crack conditions and the degree of pavement aging, the repair priority and urgency of the asphalt pavement are evaluated.

[0045] As a further improvement of this application, it also includes:

[0046] Collecting historical data of the asphalt pavement, the historical data including a relative rutting resistance index, an overall smoothness index, a repair record, and additional performance indicators of the asphalt pavement;

[0047] Using machine learning algorithms, train one or more prediction models to predict performance trends of asphalt pavement;

[0048] The currently measured relative anti-rutting ability index, overall smoothness index and additional performance index are input into the trained prediction model to obtain the prediction results of asphalt pavement performance;

[0049] Based on the prediction results, combined with the repair history of the asphalt pavement, traffic flow, and climate conditions, an optimization algorithm is used to determine the best repair time and repair strategy.

[0050] To achieve the above objectives, this application also provides the following technical solutions:

[0051] A performance detection device for an asphalt pavement, which is applied to the performance detection method for an asphalt pavement as described above, the performance detection device for an asphalt pavement comprises:

[0052] A measurement area determination module is used to determine a reference measurement area according to the highway grade and lane marking conditions;

[0053] A measurement module, used for collecting rutting depth data in the reference measurement area;

[0054] A preprocessing module, used for preprocessing the collected rutting depth data to obtain an accurate rutting depth data sequence, wherein the rutting depth data sequence includes a maximum rutting depth, a minimum rutting depth and an average rutting depth;

[0055] A calculation module, used for calculating a rutting deformation index based on the rutting depth data sequence according to formula (1);

[0056]

[0057] Where DI is the rutting deformation index, d max is the maximum rutting depth, d min is the minimum rutting depth, d min is the average rutting depth;

[0058] A model evaluation module, for calculating a relative anti-rutting capability index using a comprehensive evaluation model based on asphalt mixture properties, pavement structure information and the rutting deformation index, wherein the comprehensive evaluation model includes a hierarchical model;

[0059] The additional performance module is used to determine the additional performance index of the asphalt pavement based on the relative anti-rutting ability index in the same reference measurement area.

[0060] To achieve the above objectives, this application also provides the following technical solutions:

[0061] An electronic device includes a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the asphalt pavement performance detection method as described above is implemented.

[0062] To achieve the above objectives, this application also provides the following technical solutions:

[0063] A storage medium stores program instructions, which, when executed by a processor, can implement the above-mentioned asphalt pavement performance detection method.

[0064] This application determines the benchmark measurement area by comprehensively considering the highway grade and lane marking conditions, making the performance test more targeted and representative, and fully reflecting the actual use conditions of the asphalt pavement. By collecting rutting depth data and calculating the rutting deformation index, combining multi-dimensional data such as asphalt mixture properties and pavement structure information, and using a comprehensive evaluation model to calculate the relative rutting resistance index, a quantitative evaluation of the rutting resistance of the asphalt pavement is achieved, improving the accuracy and objectivity of the evaluation. In the same benchmark measurement area, other performance indicators of the asphalt pavement, such as the flatness index, are measured based on the relative rutting resistance index, achieving a comprehensive evaluation of multiple performance indicators of the asphalt pavement, providing more comprehensive data support for the maintenance and repair of the pavement. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a schematic diagram of the steps of an embodiment of the asphalt pavement performance testing method of the present application;

[0066] Figure 2 This is a schematic diagram of the functional modules of an embodiment of the asphalt pavement performance detection device of the present application;

[0067] Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device of the present application;

[0068] Figure 4 This is a schematic diagram of the structure of an embodiment of the storage medium of the present application. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0070] The terms "first", "second" and "third" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present application (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0071] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0072] like Figure 1 As shown, this embodiment provides an embodiment of a method for detecting the performance of an asphalt pavement. In this embodiment, the method includes the following steps:

[0073] Step S1, determining a reference measurement area according to the highway grade and lane marking conditions;

[0074] Preferably, a representative area is selected as the reference measurement area according to the highway grade (such as expressway, first-class highway, etc.) and lane marking conditions (such as single lane, multi-lane, with or without divider, etc.). The selection of this area should reflect the typical performance characteristics of the entire road section or similar road sections.

[0075] Step S2, collecting rutting depth data in the reference measurement area;

[0076] Preferably, a professional measuring tool (such as a rutting depth meter) is used to measure the rutting depth of the road surface within a determined benchmark measurement area. During the measurement, multiple measurements should be taken along the lane direction at certain intervals (such as every meter or every few meters) to ensure the comprehensiveness and accuracy of the data. The benchmark measurement area is the basis for evaluating the performance of asphalt pavement. Selecting a representative area can ensure that the collected data can reflect the typical characteristics of the entire road section or similar sections. This helps to reduce the evaluation deviation caused by local special circumstances and make the evaluation results more universal and reliable. Rutting depth data is an important indicator for evaluating the rutting resistance of asphalt pavement. By measuring the depth of rutting, the deformation of the road surface under vehicle load can be understood. The size of the rutting depth directly reflects the stability and durability of the road surface and is an important basis for evaluating the performance of the road surface.

[0077] Step S3, preprocessing the collected rutting depth data to obtain an accurate rutting depth data sequence, wherein the rutting depth data sequence includes a maximum rutting depth, a minimum rutting depth, and an average rutting depth;

[0078] It should be noted that the maximum rutting depth refers to the deepest rutting depth measured in the benchmark measurement area. It represents the maximum degree of pavement deformation in the area and is an important reference for evaluating the degree of pavement damage and the limit of rutting resistance. The minimum rutting depth refers to the shallowest rutting depth measured in the benchmark measurement area. Although it may not directly reflect the pavement's rutting resistance, it can be used as a benchmark for comparing the maximum rutting depth, which helps to more fully understand the distribution of pavement deformation. The average rutting depth refers to the average value of the rutting depth of all measuring points in the benchmark measurement area. It reflects the overall level of pavement deformation in the area and is an important indicator for evaluating the overall stability and durability of the pavement.

[0079] Preferably, the collected rutting depth data is preprocessed, including removing outliers, smoothing and other operations, to obtain an accurate rutting depth data sequence. This data sequence should contain the maximum rutting depth, the minimum rutting depth and the average rutting depth in the area. These values ​​will be used for subsequent calculations and analysis.

[0080] Step S4, calculating the rutting deformation index based on the rutting depth data sequence according to formula (1);

[0081]

[0082] Where DI is the rutting deformation index, d max is the maximum rutting depth, d min is the minimum rutting depth, d min is the average rutting depth;

[0083] It should be noted that the rutting deformation index (DI) is calculated based on the preprocessed rutting depth data sequence using formula (1). This index reflects the relative degree of rutting deformation in the area, and the larger the value, the more serious the rutting deformation.

[0084] Step S5, based on the asphalt mixture properties, the pavement structure information and the rutting deformation index, a relative rutting resistance index is calculated using a comprehensive evaluation model, wherein the comprehensive evaluation model includes a hierarchical model;

[0085] Preferably, the relative rutting resistance index is an important result of the comprehensive evaluation model output. It combines multiple factors such as the properties of asphalt mixture, pavement structure information and rutting deformation index to more comprehensively evaluate the rutting resistance of the pavement. This index helps decision makers formulate targeted maintenance and repair strategies to improve the service life and performance of the pavement.

[0086] Combining the properties of asphalt mixture (such as asphalt type, aggregate gradation, etc.), pavement structure information (such as pavement thickness, base type, etc.) and rutting deformation index, a comprehensive evaluation model is used to calculate the relative rutting resistance index. This model takes into account multiple factors that affect pavement performance and can more comprehensively evaluate the rutting resistance of the pavement. Among them, the hierarchical model is a commonly used multi-criteria decision analysis method, which decomposes the problem into multiple levels and multiple factors for comprehensive consideration.

[0087] Step S6: determining an additional performance index of the asphalt pavement based on the relative anti-rutting ability index in the same reference measurement area.

[0088] Preferably, other performance indicators of the asphalt pavement are measured based on the relative rutting resistance index in the same benchmark measurement area. These indicators may include the smoothness, skid resistance, noise level, etc. of the pavement. By combining these indicators, a more comprehensive evaluation of the overall performance of the asphalt pavement can be performed. At the same time, these performance indicators can also provide valuable reference information for the maintenance and repair of the pavement.

[0089] Further, by way of example: suppose that the performance of the asphalt pavement of a certain section of highway needs to be tested. First, according to the grade of the highway (such as a highway designed as a two-way six-lane highway) and the lane marking conditions (such as solid line separation, dotted line separation, etc.), a representative 100-meter-long section is selected as the benchmark measurement area. Next, in this benchmark measurement area, a high-precision rutting depth measuring instrument is used to measure the rutting depth every 5 meters along the center line of each lane to ensure the comprehensiveness and accuracy of the data and obtain a series of rutting depth data. These data are preprocessed. First, those abnormal data caused by measurement errors or other reasons are removed. Then, the remaining data are smoothed to eliminate random fluctuations in the data. Finally, the maximum rutting depth of 8.5 mm, the minimum rutting depth of 3.2 mm, and the average rutting depth of 5.7 mm in this area are calculated (obtained by calculating the average of 18 valid data points).

[0090] Based on the above data, we can calculate the rutting deformation index DI according to the above formula (1). This index can intuitively reflect the degree of rutting deformation in the area. The larger the value, the more serious the rutting deformation. Taking the above data as an example, DI = (8.5-3.2) / 5.7≈0.93 mm. Similarly, the asphalt mixture properties and pavement structure information can be: asphalt type: SBS modified asphalt; aggregate gradation: AC-16; pavement thickness: 18 cm; base type: cement stabilized gravel. Calculated by the comprehensive evaluation model (including the hierarchical model): 0.85 (this value is assumed, and the actual calculation will involve more detailed parameters and weight distribution). In the same benchmark measurement area, other performance indicators of the asphalt pavement, such as pavement flatness, skid resistance, noise level, etc., are determined based on this relative anti-rutting ability index. The determination methods of these indicators are related to the specific measuring instruments and operating procedures, and can be selected and operated according to actual conditions.

[0091] Furthermore, step S5 specifically includes the following steps:

[0092] Step S51, establishing the hierarchical model based on the properties of the asphalt mixture, the pavement structure information and the rutting deformation index, wherein the hierarchical model includes a scheme layer, a criterion layer and a target layer arranged in sequence from bottom to top, wherein the elements of the target layer include a relative rutting resistance index for evaluating the asphalt pavement, the elements of the criterion layer include the properties of the asphalt mixture, the pavement structure information and the rutting deformation index, and the elements of the scheme layer include the target road section to be measured;

[0093] It should be noted that the hierarchical model is a database design pattern and an effective framework for organizing and analyzing complex problems or systems. The core idea of ​​the hierarchical model is to store and manage data or system components in a hierarchical structure, following the principle that "low-level data can constitute upper-level data, and upper-level data can also be subdivided into lower-level data." This model uses a tree structure in database design, which simplifies data query, modification, addition and deletion operations.

[0094] The target layer is the highest level of the hierarchical model and contains the overall goal of evaluating the relative rutting resistance index of asphalt pavement. The criterion layer is the middle level of the hierarchical model and contains the key factors affecting rutting resistance, namely the properties of asphalt mixture, pavement structure information and rutting deformation index. The scenario layer is the lowest level of the hierarchical model and contains the specific measured target sections, which are the objects of evaluation.

[0095] Preferably, detailed properties of asphalt mixtures are collected, including asphalt type, viscosity, softening point, and aggregate grading, crushing value, etc. Obtain pavement structure information, such as the thickness of each pavement layer, material type (such as whether reinforcing materials such as geogrids are used), type and thickness of base and subbase, etc. Measure rutting depth in detail at predetermined intervals (such as every 5 meters or 10 meters) within the selected benchmark measurement area to ensure the accuracy and representativeness of the data.

[0096] Step S52, scaling each layer of the hierarchical model, determining the relative index of each layer of the hierarchical model relative to the previous layer, and constructing a judgment matrix based on the relative index;

[0097] Preferably, in the hierarchical model, the "relative rutting resistance index for evaluating asphalt pavement" is set as the target layer. The criterion layer includes three main aspects: the properties of asphalt mixture, pavement structure information and rutting deformation index. The scheme layer is a specific measurement target section, which can be a single section or a collection of multiple sections. Each layer of the hierarchical model is scaled to determine the relative importance (relative index) of the elements of each layer relative to the previous layer. Based on these relative indices, a judgment matrix is ​​constructed to quantify the relative relationship between the elements.

[0098] Step S53: Generate the maximum eigenvalue of each judgment matrix and its corresponding eigenvector, where the eigenvector is the weight of each layer element relative to the previous layer element.

[0099] Preferably, the maximum eigenvalue of each judgment matrix and its corresponding eigenvector are generated through mathematical calculations. The eigenvector represents the weight of each layer element relative to the previous layer element, that is, the importance of each element in the evaluation.

[0100] Step S54, performing a consistency check on each of the judgment matrices, wherein the consistency check includes calculating a consistency index and a consistency ratio of each of the judgment matrices;

[0101] Preferably, a consistency check is performed on each judgment matrix to ensure that the element relationships in the matrix are logically consistent. The consistency check includes calculating a consistency index and a consistency ratio to evaluate the reliability and validity of the judgment matrix.

[0102] Step S55, weighting each of the feature vectors to obtain the total weight of each layer element relative to the target layer;

[0103] Preferably, the feature vectors that pass the consistency check are weighted and synthesized to obtain the total weight of each layer element relative to the target layer. The relative anti-rutting ability index is calculated by weighted summation or other mathematical methods in combination with the measured data and the total weight.

[0104] Step S56, obtaining the relative anti-rutting ability index based on the characteristic vector and the total weight of the elements of each layer relative to the target layer.

[0105] Preferably, the relative rutting resistance index is a comprehensive index that takes into account multiple factors that affect the rutting resistance of asphalt pavement. This index can be used to evaluate the performance of asphalt pavement, guide maintenance and repair decisions, and optimize pavement design. Through the above embodiments, the rutting resistance of asphalt pavement can be evaluated more comprehensively and objectively, and the service life and safety of the pavement can be improved.

[0106] Furthermore, step S54 further includes:

[0107] Step S541, according to the elements of each judgment matrix, the consistency index of each judgment matrix is ​​calculated by formula (2), and the consistency index is used to measure the relative consistency between the elements in the judgment matrix;

[0108]

[0109] Among them, CI is the consistency index, λ max is the maximum eigenvalue of the judgment matrix, n is the order of the judgment matrix, including the number of rows or columns;

[0110] Preferably, for each constructed judgment matrix, we need to calculate its consistency index (CI). This can be achieved by the following steps:

[0111] Determine the order n of the judgment matrix, that is, the number of rows or columns of the matrix.

[0112] Calculate the maximum eigenvalue λ of the judgment matrixmax This is usually done using an eigenvalue calculation function in a mathematical software or programming library. Make sure that the method you choose can accurately and efficiently calculate the maximum eigenvalue. max , the order n of the judgment matrix is ​​substituted into formula (2) to calculate the consistency index CI. This index is used to measure the relative consistency between the elements in the judgment matrix.

[0113] Step S542, searching for an average random consistency index corresponding to the order of the judgment matrix, wherein the average random consistency index is an average value of consistency indexes of judgment matrices of the same order generated randomly for multiple times;

[0114] Preferably, a pre-prepared RI table is consulted, which lists the values ​​of the average random consistency index RI corresponding to the judgment matrices of different orders. The RI value corresponding to the order n determined in step 1 is found in the RI table. This value will be used for the subsequent consistency ratio calculation. If there is no corresponding value in the RI table, the step of estimating by interpolation or other approximate methods has been omitted because it is not the main implementation method.

[0115] Step S543, taking the ratio of the consistency index to the average random consistency index as a consistency ratio, wherein the consistency ratio is used to measure whether the consistency of the judgment matrix meets the requirements;

[0116] Preferably, the consistency index CI calculated above is divided by the average random consistency index RI found in step 2, that is, CR = CI / RI. Perform simple mathematical operations to obtain the value of the consistency ratio CR. This value is used to measure whether the consistency of the judgment matrix meets the requirements.

[0117] Step S544, determining whether the consistency ratio is less than a preset consistency threshold, and if so, determining that the judgment matrix passes the consistency check.

[0118] Preferably, a consistency threshold (such as 0.1) is set according to actual needs or industry standards. This threshold is used to determine whether the consistency of the judgment matrix is ​​acceptable. Compare the consistency ratio CR calculated above with the set threshold. If CR is less than or equal to the threshold, it means that the consistency of the judgment matrix meets the requirements; otherwise, it means that the consistency is poor and the judgment matrix may need to be adjusted or reconstructed.

[0119] Furthermore, step S56 further includes:

[0120] Step S561, obtaining the actual measurement value of each element of the criterion layer;

[0121] Preferably, for the performance test of asphalt pavement, it is necessary to first collect the actual measurement data of each element in the criterion layer (such as pavement material strength, temperature, humidity, etc.). These data can be obtained through field experiments, professional equipment measurement or related monitoring systems to ensure the accuracy and reliability of the data. The collected actual measurement values ​​will be used for the subsequent determination of the characteristic linear relationship and the calculation of the relative anti-rutting ability index.

[0122] Step S562, determining a characteristic linear relationship of each element of the criterion layer based on the actual measurement value;

[0123] Preferably, after obtaining the actual measured values ​​of each element in the criterion layer, the relationship between these data and the anti-rutting ability needs to be analyzed. By studying historical data and drawing on expert experience, the linear relationship or nonlinear relationship (here specifically linear relationship) between each element and the anti-rutting ability is determined. For the linear relationship, regression analysis and other methods can be used to obtain the characteristic linear equation of each element, which describes the direct impact of element changes on the anti-rutting ability.

[0124] Step S563, calculating the relative anti-rutting ability index by formula (3);

[0125]

[0126] Where RRI is the relative rutting resistance index, w i is the weight of the i-th criterion layer element, x i is the characteristic linear relationship of the ith element of the criterion layer, f i (x i ) is the function value corresponding to the characteristic linear relationship of the ith element of the criterion layer, and m is the number of elements in the criterion layer.

[0127] Preferably, after determining the characteristic linear relationship and corresponding weight of each element of the criterion layer, the relative anti-rutting ability index is calculated using formula (3). In this formula, w i represents the weight of the i-th criterion layer element, f i (x i ) represents the function value corresponding to the characteristic linear relationship of the i-th element. First, substitute the actual measured value into the characteristic linear equation f of each element i (x i ), calculate the corresponding function value f i (x i ). Then, these function values ​​are multiplied by their corresponding weights and the products are summed to obtain the relative rutting resistance index RRI.

[0128] Furthermore, step S6 specifically includes the following steps:

[0129] Step S61, in the reference measurement area, selecting a plurality of measurement points reflecting the flatness condition in the reference measurement area according to a predetermined grid method;

[0130] Preferably, determine the scope and size of the reference measurement area. This area should be a typical area that can represent the overall performance of the asphalt pavement. Then, use a predetermined grid method, such as a uniformly divided square or rectangular grid, to cover the entire reference measurement area. Select a measurement point at the intersection or center point of each grid to ensure that these measurement points can evenly and comprehensively reflect the flatness condition in the reference measurement area. Record the location information of each measurement point for subsequent measurement and data analysis.

[0131] Step S62, performing flatness measurement on each selected measuring point to obtain flatness data of each measuring point;

[0132] Preferably, at each selected measuring point, use a professional flatness measuring instrument (such as a flatness meter, laser rangefinder, etc.) to measure the flatness. When measuring, ensure that the instrument is placed stably and the measuring direction is consistent with the main traffic direction of the asphalt pavement. Record the flatness data of each measuring point, which can be a quantitative indicator of elevation difference, unevenness, and road surface flatness. Organize the flatness data of all measuring points into a data table or database for subsequent processing and analysis.

[0133] Step S63, allocating a flatness weight to each of the measuring points according to the relative anti-rutting ability index, wherein the flatness weight of each of the measuring points is proportional to the relative anti-rutting ability index;

[0134] Preferably, a flatness weight is assigned to each measuring point based on the relative rutting resistance index calculated previously. Here, the flatness weight of the measuring point with a higher relative rutting resistance index should be increased accordingly, because they have a more significant impact on the overall pavement performance. The flatness weight of each measuring point can be determined by a linear ratio or a nonlinear function. Ensure that the weight allocation process is open and transparent and meets the actual situation and needs of asphalt pavement performance evaluation.

[0135] Step S64: generating weighted flatness data of each measuring point based on the flatness weight of each measuring point, and generating an overall flatness index in the reference measurement area by using a preset flatness algorithm.

[0136] Preferably, after determining the flatness weight of each measuring point, these weights are multiplied with the corresponding flatness data to obtain weighted flatness data. The advantage of this is that the flatness data of different measuring points can be weighted according to their importance to the overall road performance, thereby more accurately reflecting the overall flatness condition in the reference measurement area. Finally, the weighted flatness data is processed and analyzed using a preset flatness algorithm (such as weighted average method, root mean square error method, etc.) to generate an overall flatness index in the reference measurement area. This index can be used as one of the important bases for evaluating the performance of asphalt pavement and provide decision support for the maintenance and management of the pavement.

[0137] Furthermore, step S6 specifically includes the following steps:

[0138] Step S65, setting thresholds of the relative anti-rutting ability index and the flatness index;

[0139] Preferably, a threshold value of the relative rutting resistance index is determined based on historical data, industry standards or expert advice, and this threshold value represents the minimum acceptance standard for the rutting resistance of the asphalt pavement.

[0140] Similarly, set the threshold value of the smoothness index. This threshold reflects the minimum requirement for the smoothness of the asphalt road surface to ensure driving safety and comfort. Record these thresholds in the maintenance management system for subsequent comparison and evaluation.

[0141] Step S66, comparing the actual relative anti-rutting ability index with the set relative anti-rutting ability index threshold, and comparing the actual flatness index with the set flatness index threshold;

[0142] Preferably, the actual relative anti-rutting ability index of the asphalt pavement in the reference measurement area is obtained. The actual relative anti-rutting ability index is compared with a set threshold value to determine whether it is lower than the threshold value. At the same time, the actual overall flatness index in the reference measurement area is obtained. The actual overall flatness index is compared with a set threshold value to determine whether it is lower than the threshold value.

[0143] Step S67, if the actual relative rutting resistance index is lower than the threshold value, or the actual overall flatness index is lower than the threshold value, it is determined that the asphalt pavement in the reference measurement area needs to be repaired;

[0144] Preferably, if the actual relative rutting resistance index is lower than its threshold, it indicates that the rutting resistance of the asphalt pavement is insufficient and repair is required. If the actual overall flatness index is lower than its threshold, it indicates that the flatness of the asphalt pavement is not up to standard and repair is also required. In any case, when it is determined that repair is required, the benchmark measurement area should be recorded and marked to facilitate the formulation of subsequent maintenance plans.

[0145] Step S68, based on the specific values ​​of the relative rutting resistance index and the smoothness index, combined with the additional performance indicators of the asphalt pavement, the additional performance indicators include crack conditions and pavement aging degree, to evaluate the repair priority and urgency of the asphalt pavement.

[0146] Preferably, the performance of the asphalt pavement is comprehensively evaluated in combination with the specific values ​​of the relative anti-rutting ability index and the smoothness index. Additional performance indicators of the asphalt pavement are considered, such as crack conditions and the degree of pavement aging. These indicators can be obtained through visual inspection, instrument measurement or professional evaluation. According to the comprehensive evaluation results, the repair priority of the asphalt pavement is determined. For example, areas with severe performance degradation, dense cracks or severe aging should be given a higher repair priority. At the same time, the urgency of the repair is evaluated. Problem areas that seriously affect driving safety or cause traffic congestion should be marked as urgent repair needs so that maintenance work can be arranged as soon as possible. The evaluation results are recorded in the maintenance management system, and corresponding maintenance plans and budgets are formulated.

[0147] Furthermore, step S68 further includes:

[0148] Step S681, collecting historical data of the asphalt pavement, the historical data including the relative rutting resistance index, overall smoothness index, repair record and additional performance index of the asphalt pavement;

[0149] Preferably, a database or data warehouse is established to store and organize historical data of asphalt pavement. The historical data of relative anti-rutting ability index, overall smoothness index, repair records and additional performance indicators (such as crack conditions, pavement aging degree, etc.) of asphalt pavement are collected from various sources (such as road maintenance departments, traffic monitoring systems, weather records, etc.).

[0150] Ensure that the collected data is accurate and complete, and store it in a unified data format. Preprocess historical data, including data cleaning, outlier processing, missing value filling, etc., to improve data quality.

[0151] Step S682, using a machine learning algorithm to train one or more prediction models to predict the performance change trend of the asphalt pavement;

[0152] Preferably, a suitable machine learning algorithm, such as linear regression, support vector machine, neural network, etc., is selected for training the prediction model. Features are extracted from historical data, including relative rutting resistance index, overall flatness index, additional performance indicators, and possible time series characteristics (such as season, year, etc.). The extracted features are divided into a training set and a test set, where the training set is used to train the model and the test set is used to evaluate the model performance. The selected machine learning algorithm is trained using the training set, and the model parameters are adjusted to optimize the model performance. The performance of the trained model is evaluated using the test set to ensure that the model has good prediction accuracy and generalization ability.

[0153] Step S683, inputting the currently measured relative anti-rutting ability index, overall flatness index and additional performance index into the trained prediction model to obtain the prediction result of the asphalt pavement performance;

[0154] Preferably, the data of the relative rutting resistance index, the overall smoothness index and the additional performance index currently measured are obtained. These data are input into the trained prediction model to predict the asphalt pavement performance. The model will output the prediction results of the asphalt pavement performance in the future (such as a few months or a few years), including the change trend of the relative rutting resistance index and the smoothness index.

[0155] Step S684, based on the prediction results, combined with the repair history of the asphalt pavement, traffic flow, and climate conditions, an optimization algorithm is used to determine the best repair time and repair strategy.

[0156] Preferably, based on the prediction results, a comprehensive analysis is conducted in combination with the repair history of the asphalt pavement (such as repair frequency, repair effect, etc.), traffic flow (such as vehicle flow, proportion of heavy-loaded vehicles, etc.) and climatic conditions (such as rainfall, temperature, etc.). Optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) are used to search for the best repair time and repair strategy. These algorithms can find the optimal solution based on preset objective functions (such as minimizing repair costs, maximizing pavement service life, etc.). During the search process, various constraints are considered, such as budget constraints, construction time windows, impact on traffic, etc. One or more repair plans are finally determined, including the specific time and location of the repair and the repair measures taken (such as local repairs, comprehensive renovations, etc.). The determined repair plan is recorded in the maintenance management system, and the relevant departments are notified to implement it.

[0157] like Figure 2 As shown, this embodiment also provides an embodiment of an asphalt pavement performance detection device 200. In this embodiment, the asphalt pavement performance detection device 200 is applied to the asphalt pavement performance detection method in the above embodiment. The asphalt pavement performance detection device 200 includes:

[0158] The measurement area determination module 201 is used to determine the reference measurement area according to the highway grade and lane marking conditions;

[0159] A measurement module 202, for collecting rutting depth data in the reference measurement area;

[0160] A preprocessing module 203 is used to preprocess the collected rutting depth data to obtain an accurate rutting depth data sequence, wherein the rutting depth data sequence includes a maximum rutting depth, a minimum rutting depth and an average rutting depth;

[0161] A calculation module 204 is used to calculate a rutting deformation index based on the rutting depth data sequence according to formula (1);

[0162]

[0163] Where DI is the rutting deformation index, d max is the maximum rutting depth, d min is the minimum rutting depth, d min is the average rutting depth;

[0164] A model evaluation module 205, for calculating a relative anti-rutting capability index using a comprehensive evaluation model based on asphalt mixture properties, pavement structure information and the rutting deformation index, wherein the comprehensive evaluation model includes a hierarchical model;

[0165] The additional performance module 206 is used to determine the additional performance index of the asphalt pavement based on the relative rutting resistance index in the same reference measurement area.

[0166] It should be noted that this embodiment is a functional module device embodiment based on the above method embodiment. The optimization, expansion, limitation and examples of this embodiment can be referred to the above method embodiment, and this embodiment will not be repeated.

[0167] This embodiment improves the accuracy of rutting depth data through precise data preprocessing, and then uses a comprehensive evaluation model to comprehensively consider multiple factors such as asphalt mixture properties, pavement structure information, and rutting deformation index to obtain a more reliable relative rutting resistance index. At the same time, combined with a machine learning prediction model, it achieves an accurate prediction of the performance trend of asphalt pavement, providing decision makers with scientific repair timing and strategic recommendations. In addition, the present invention also improves detection efficiency and reduces maintenance costs through reasonable resource allocation and optimization.

[0168] like Figure 3 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 3 includes a processor 31 and a memory 32 coupled to the processor 31.

[0169] The memory 32 stores program instructions for implementing the layout method of the chemical pump body processing equipment of any of the above embodiments.

[0170] The processor 31 is used to execute program instructions stored in the memory 32 to perform the layout of the chemical pump body processing equipment.

[0171] The processor 31 may also be referred to as a CPU (Central Processing Unit). The processor 31 may be an integrated circuit chip having signal processing capabilities. The processor 31 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0172] Further, Figure 4 This is a schematic diagram of the structure of a storage medium of an embodiment of the present application. The storage medium 4 of the embodiment of the present application stores program instructions 41 that can implement all the above methods, wherein the program instructions 41 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0173] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0174] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0175] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for detecting the performance of an asphalt pavement, characterized in that: include: Determine the benchmark measurement area based on the highway grade and lane marking conditions; Collecting rutting depth data in the reference measurement area; Preprocessing the collected rutting depth data to obtain an accurate rutting depth data sequence, wherein the rutting depth data sequence includes a maximum rutting depth, a minimum rutting depth, and an average rutting depth; Calculating a rutting deformation index based on the rutting depth data sequence according to formula (1); Where DI is the rutting deformation index, d max is the maximum rutting depth, d min is the minimum rutting depth, d min is the average rutting depth; Based on the properties of the asphalt mixture, the pavement structure information and the rutting deformation index, a relative rutting resistance index is calculated using a comprehensive evaluation model, wherein the comprehensive evaluation model includes a hierarchical model; Determining additional performance indicators of asphalt pavement based on the relative rutting resistance index within the same reference measurement area; The calculation of the relative rutting resistance index using the comprehensive evaluation model also includes: The hierarchical model is established based on the properties of the asphalt mixture, the pavement structure information and the rutting deformation index, wherein the hierarchical model includes a scheme layer, a criterion layer and a target layer arranged in sequence from bottom to top, wherein the elements of the target layer include a relative rutting resistance index for evaluating the asphalt pavement, the elements of the criterion layer include the properties of the asphalt mixture, the pavement structure information and the rutting deformation index, and the elements of the scheme layer include a target road section for measurement; Scaling each layer of the hierarchical model, respectively determining a relative index of each layer of the hierarchical model relative to a previous layer, and constructing a judgment matrix based on the relative index; Generate the maximum eigenvalue of each judgment matrix and its corresponding eigenvector, wherein the eigenvector is the weight of each layer element relative to the previous layer element; Performing a consistency check on each of the judgment matrices, wherein the consistency check includes calculating a consistency index and a consistency ratio of each of the judgment matrices; Performing weight synthesis on each of the feature vectors to obtain the total weight of each layer element relative to the target layer; Obtaining the relative anti-rutting capability index based on the characteristic vector and the total weight of the elements of each layer relative to the target layer; The consistency check of each judgment matrix comprises: According to the elements of each judgment matrix, the consistency index of each judgment matrix is ​​calculated by formula (2), and the consistency index is used to measure the relative consistency between the elements in the judgment matrix; Among them, CI is the consistency index, λ max is the maximum eigenvalue of the judgment matrix, n is the order of the judgment matrix, including the number of rows or columns; Finding an average random consistency index corresponding to the order of the judgment matrix, wherein the average random consistency index is an average value of consistency indexes of judgment matrices of the same order generated randomly for multiple times; The ratio of the consistency index to the average random consistency index is used as a consistency ratio, and the consistency ratio is used to measure whether the consistency of the judgment matrix meets the requirements; Determine whether the consistency ratio is less than a preset consistency threshold, and if so, determine that the judgment matrix passes the consistency test; The relative rutting resistance index obtained based on the characteristic vector and the total weight of the elements of each layer relative to the target layer includes: Obtaining an actual measurement value of each element of the criterion layer; Determining a characteristic linear relationship of each element of the criterion layer based on the actual measurement value; The relative anti-rutting ability index is calculated by formula (3); Where RRI is the relative rutting resistance index, w i is the weight of the i-th criterion layer element, x i is the characteristic linear relationship of the ith element of the criterion layer, f i (x i ) is the function value corresponding to the characteristic linear relationship of the ith element of the criterion layer, and m is the number of elements in the criterion layer.

2. The asphalt pavement performance testing method according to claim 1, characterized in that: Additional performance indicators of asphalt pavement determined based on the relative rutting resistance index within the same benchmark measurement area include: In the reference measurement area, a plurality of measurement points reflecting the flatness condition in the reference measurement area are selected according to a predetermined grid method; Performing flatness measurement at each selected measuring point to obtain flatness data of each measuring point; According to the relative anti-rutting ability index, a flatness weight is allocated to each measuring point, wherein the flatness weight of each measuring point is proportional to the relative anti-rutting ability index; The weighted flatness data of each measuring point is generated based on the flatness weight of each measuring point, and the overall flatness index in the reference measurement area is generated by a preset flatness algorithm.

3. The asphalt pavement performance testing method according to claim 2, characterized in that: The method further comprises: Setting thresholds of the relative anti-rutting ability index and the flatness index; comparing the actual relative rutting resistance index with the set relative rutting resistance index threshold, and comparing the actual flatness index with the set flatness index threshold; If the actual relative rutting resistance index is lower than the threshold value, or the actual overall smoothness index is lower than the threshold value, it is determined that the asphalt pavement in the reference measurement area needs to be repaired; According to the specific values ​​of the relative rutting resistance index and the smoothness index, combined with the additional performance indicators of the asphalt pavement, the additional performance indicators include crack conditions and the degree of pavement aging, the repair priority and urgency of the asphalt pavement are evaluated.

4. The asphalt pavement performance testing method according to claim 3, characterized in that: The method further comprises: Collecting historical data of the asphalt pavement, the historical data including a relative rutting resistance index, an overall smoothness index, a repair record, and additional performance indicators of the asphalt pavement; Using machine learning algorithms, train one or more prediction models to predict performance trends of asphalt pavement; The currently measured relative anti-rutting ability index, overall smoothness index and additional performance index are input into the trained prediction model to obtain the prediction results of asphalt pavement performance; Based on the prediction results, combined with the repair history of the asphalt pavement, traffic flow, and climate conditions, an optimization algorithm is used to determine the best repair time and repair strategy.

5. A performance detection device for an asphalt pavement, which is applied to the performance detection method for an asphalt pavement as claimed in any one of claims 1 to 4, characterized in that: The asphalt pavement performance detection device comprises: A measurement area determination module is used to determine a reference measurement area according to the highway grade and lane marking conditions; A measurement module, used for collecting rutting depth data in the reference measurement area; A preprocessing module, used for preprocessing the collected rutting depth data to obtain an accurate rutting depth data sequence, wherein the rutting depth data sequence includes a maximum rutting depth, a minimum rutting depth and an average rutting depth; A calculation module, used for calculating a rutting deformation index based on the rutting depth data sequence according to formula (1); Where DI is the rutting deformation index, d max is the maximum rutting depth, d min is the minimum rutting depth, d min is the average rutting depth; A model evaluation module, for calculating a relative anti-rutting capability index using a comprehensive evaluation model based on asphalt mixture properties, pavement structure information and the rutting deformation index, wherein the comprehensive evaluation model includes a hierarchical model; The additional performance module is used to determine the additional performance index of the asphalt pavement based on the relative anti-rutting ability index in the same reference measurement area.

6. An electronic device, characterized in that: It comprises a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the performance detection method of the asphalt pavement as described in any one of claims 1 to 4 is implemented.

7. A storage medium, characterized in that: The storage medium stores program instructions, and when the program instructions are executed by the processor, the asphalt pavement performance detection method according to any one of claims 1 to 4 can be implemented.

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