Road surface anti-skid prediction method and system simultaneously considering macro and micro textures

Abstract

The invention relates to the technical field of road anti-skid performance prediction, and provides a road surface anti-skid prediction method and system considering macro and micro textures at the same time, and the method comprises the following steps: extracting aggregate micro textures; measuring to obtain the macroscopic texture of the mixture; according to the extracted microscopic texture of the aggregate and the measured macroscopic texture of the mixture, establishing a road surface anti-skid prediction model; and based on the established road surface anti-skid prediction model, predicting and obtaining an anti-skid performance index of the road surface. According to the method, the pavement anti-skid prediction model is constructed by obtaining the micro-texture of the aggregate and the macro-texture of the mixture, the data fitting effect and the fitting goodness are better, the prediction accuracy of the anti-skid performance of the asphalt pavement can be improved, and therefore important reference is provided for design, construction and maintenance of the anti-skid asphalt pavement.

Description

Technical Field

[0001] The present application relates to the technical field of anti-skid performance prediction of asphalt pavement, and in particular to a pavement anti-skid performance prediction method and system that simultaneously considers macro and micro textures. Background Art

[0002] The skid resistance of the road surface directly affects the safety of driving, and the skid resistance of asphalt pavement is determined by micro-texture and macro-texture. Micro-texture refers to the microscopic structure of the aggregate surface, while macro-texture refers to the macroscopic structure formed between aggregates.

[0003] Aggregates are generally divided into coarse aggregate and fine aggregate. Coarse aggregate (particle size greater than 4.75mm) forms the skeleton structure of the mixture, just like the frame of a building, bearing vehicle loads and transmitting stress. For example, in asphalt concrete pavement, coarse aggregates are interlocked and provide basic strength and stability for the pavement; fine aggregate (particle size less than 4.75mm) mainly fills the gaps in the coarse aggregate, and also helps to improve the working performance of the mixture. It can make the mixture more compact and affect the workability of the mixture to a certain extent, ensuring that the material can be easily spread and compacted during the construction process.

[0004] At present, the prediction of anti-skid performance mainly adopts macro texture, without considering the micro texture of aggregate. This is mainly because the existing road surface texture scanning equipment is not accurate enough and can only collect the macro texture of the road surface, which leads to a large deviation in the prediction of anti-skid performance of asphalt pavement based on macro texture. In order to improve the accuracy of prediction, it is crucial to consider both the micro texture of aggregate and the macro texture of mixture to improve the effect of anti-skid prediction. Summary of the invention

[0005] The present application provides a method and system for predicting the anti-skid performance of a pavement that takes into account both macro and micro textures, which can solve the technical problem in the prior art that the anti-skid performance prediction of asphalt pavements based on macro textures has large deviations.

[0006] In a first aspect, the present application provides a method for predicting road surface skid resistance by considering both macro and micro textures, characterized in that it comprises the following steps:

[0007] Extract aggregate microtexture;

[0008] Measure and obtain the macro texture of the mixture;

[0009] A pavement skid resistance prediction model is established based on the micro texture of the extracted aggregate and the macro texture of the mixture obtained by measurement;

[0010] Based on the established pavement anti-skid prediction model, the pavement anti-skid performance index is predicted and obtained.

[0011] In combination with the first aspect, in one embodiment, the extracting of aggregate microtexture specifically comprises the following steps:

[0012] Wash and dry the aggregate;

[0013] The dried aggregate is scanned microscopically to measure and obtain the microscopic texture of the aggregate.

[0014] In combination with the first aspect, in one embodiment, the measuring and obtaining of the macro texture of the mixture specifically comprises the following steps:

[0015] Prepare asphalt mixture and form rutting plate specimens;

[0016] The rutting plate specimens were scanned to measure the macro texture of the mixture.

[0017] In combination with the first aspect, in one embodiment, the road surface anti-skid prediction model is established according to the extracted micro texture of the aggregate and the measured macro texture of the mixture, specifically comprising the following steps:

[0018] According to the measured micro-texture of the aggregate, determining a correlation micro-texture index having a correlation with the skid resistance of the pavement exceeding a first preset threshold;

[0019] Determining, based on the macro texture of the mixture obtained by measurement, a correlation macro texture index having a correlation with the anti-skid performance of the pavement exceeding a second preset threshold;

[0020] The anti-skid performance of asphalt mixture is obtained by testing the rutting plate specimens;

[0021] A pavement anti-skid prediction model is established based on the determined associated micro-texture indexes of aggregates and the associated macro-texture indexes of mixtures as well as the anti-skid performance of asphalt mixtures obtained by testing.

[0022] In combination with the first aspect, in one embodiment, the associated microtexture indicators are microtexture average roughness, microtexture root mean square roughness and microtexture depth correlation coefficient.

[0023] In combination with the first aspect, in one embodiment, determining, based on the macro texture of the mixture obtained by measurement, an associated macro texture index having a correlation with the pavement anti-skid performance exceeding a second preset threshold value specifically comprises the following steps:

[0024] The macro texture of the mixture obtained by measurement is calculated according to the texture index formula to obtain a macro texture index;

[0025] The Pearson correlation coefficient is used to calculate the correlation between the anti-slip performance and the macro-texture index, and the macro-texture index whose correlation exceeds the second preset threshold is selected as the associated macro-texture index.

[0026] In combination with the first aspect, in one embodiment, the pavement anti-skid prediction model is established according to the determined associated micro-texture index of the aggregate and the associated macro-texture index of the mixture and the anti-skid performance of the asphalt mixture obtained by testing, specifically comprising the following steps:

[0027] Multivariate linear fitting was performed on the associated micro-texture indices, the associated macro-texture indices of the mixture, and the anti-skid performance of the asphalt mixture obtained by testing, and a pavement anti-skid prediction model was established.

[0028] In combination with the first aspect, in one implementation, the road surface anti-skid prediction model is shown as follows:

[0029] BPN=1.787MPD 宏观 +5.733Ra 宏观 +2.795Δq 宏观 -1.130Ra 微观 +1.189Rq 微观

[0030] +0.470Rδc 微观 +36.290

[0031] Where BPN is the swing value, MPD 宏观 is the average contour depth of the macro texture, Ra 宏观 is the average roughness of the macro texture, Δq 宏观 is the variation of macro texture roughness, Ra 微观 is the average roughness of the micro texture, Rq 微观 for

[0032] Microtexture root mean square roughness, Rδc 微观 is the height difference of the micro texture profile cross section.

[0033] In a second aspect, the present application provides a road skid resistance prediction system that considers both macro and micro textures, including:

[0034] Micro texture acquisition module, used to extract aggregate micro texture;

[0035] Macro texture acquisition module, used to measure and obtain the macro texture of the mixture;

[0036] An anti-skid prediction model establishment module, which, together with the micro-texture acquisition module and the macro-texture acquisition module, establishes a pavement anti-skid prediction model based on the micro-texture of the extracted aggregate and the macro-texture of the mixture obtained by measurement;

[0037] The anti-skid performance prediction module is in communication connection with the anti-skid prediction model establishment module and is used to predict and obtain the anti-skid performance index of the road surface based on the established road surface anti-skid prediction model.

[0038] In conjunction with the second aspect, in one implementation, the anti-slip prediction model building module includes:

[0039] A correlation micro-texture index acquisition unit, used to obtain the micro-texture of the aggregate according to the measurement, and determine the correlation micro-texture index whose correlation with the anti-skid performance of the pavement exceeds a first preset threshold;

[0040] A correlation macro texture index acquisition unit, used to determine, based on the macro texture of the mixture obtained by measurement, a correlation macro texture index having a correlation with the anti-skid performance of the pavement exceeding a second preset threshold;

[0041] The anti-skid performance acquisition unit is used to test the rutting plate specimen to obtain the anti-skid performance of the asphalt mixture;

[0042] The anti-skid prediction model acquisition unit is used to establish a pavement anti-skid prediction model based on the determined associated micro-texture index of the aggregate and the associated macro-texture index of the mixture and the anti-skid performance of the asphalt mixture obtained by testing.

[0043] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0044] The pavement anti-skid prediction model constructed by obtaining the micro-texture of aggregate and the macro-texture of mixture has better data fitting effect, which can improve the prediction accuracy of the anti-skid performance of asphalt pavement, thus providing an important reference for the design, construction and maintenance of anti-skid asphalt pavement. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of the process flow of the pavement skid resistance prediction method that considers both macro and micro textures for this application;

[0046] Figure 2 A working scene diagram of road surface texture scanning for a road surface anti-skid texture tester;

[0047] Figure 3 It is the macroscopic three-dimensional texture map of the road surface;

[0048] Figure 4 This is a scene diagram for testing the road surface's anti-skid performance;

[0049] Figure 5 A comparison chart of the predicted swing values ​​and the measured swing values ​​calculated by the road surface anti-skid prediction model that considers both macro- and micro-textures and micro-textures provided in this application;

[0050] Figure 6 This is a comparison chart between the predicted swing values ​​calculated by the pavement skid resistance prediction model that only considers macro and micro textures and the measured swing values. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings 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.

[0052] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings 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 includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0053] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0054] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0055] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0056] First, some technical terms in the present application are explained to facilitate those skilled in the art to understand the present application.

[0057] BPN: British Pendulum Number, which is the result of pendulum instrument measurement;

[0058] MPD: Mean Profile Depth, average profile depth;

[0059] Ra:Arithmetic Average Roughness, arithmetic average roughness;

[0060] Rq:Root Mean Square Roughness, root mean square roughness

[0061] Rδc: height difference of profile section;

[0062] R 2 : Coefficient of determination.

[0063] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0064] First, as Figure 1 As shown, the present application provides a road surface anti-skid prediction method that considers both macro and micro textures, comprising the following steps:

[0065] Step S1: extracting aggregate micro texture;

[0066] Step S2: measuring and obtaining the macro texture of the mixture;

[0067] Step S3: Based on the extracted micro texture of aggregate and the measured macro texture of mixture, a pavement anti-skid prediction model that considers both macro and micro textures is established;

[0068] Step S4: Based on the established road surface anti-skid prediction model, predict and obtain the road surface anti-skid performance index.

[0069] The pavement anti-skid prediction model constructed in this application by obtaining the micro-texture of aggregate and the macro-texture of mixture has better data fitting effect and fitting goodness, which can improve the prediction accuracy of the anti-skid performance of asphalt pavement, thereby providing an important reference for the design, construction and maintenance of anti-skid asphalt pavements.

[0070] In one embodiment, the step S1: extracting aggregate micro texture specifically comprises the following steps:

[0071] Step S11: washing and drying the aggregate;

[0072] Step S12: Place the dried aggregate on the stage of a laser confocal scanning microscope for scanning measurement. The scanning area is 640 μm×640 μm. The microscopic texture data of the aggregate is obtained by measurement, as shown in Table 1:

[0073] Table 1 Microstructure data of aggregates

[0074] Aggregate Type Ra(μm) Rq(μm) Rδc(μm) Feldspathic sandstone 10.795 12.585 23.584 Quartz sandstone 8.739 10.706 19.03 Red Sandstone 8.873 10.975 19.14 Limestone 4.158 4.991 9.012 Basalt 7.082 8.725 15.057 Amphibolite 6.423 8.151 13.49 Diabase 6.313 7.847 13.876 Gabbro 7.452 10.31 13.524 granite 9.787 12.367 21.307

[0075] In one embodiment, the step S2: measuring and obtaining the macro texture of the mixture specifically comprises the following steps:

[0076] Step S21: Mixing aggregate, asphalt and mineral powder to prepare asphalt mixture, and forming a rutting plate specimen;

[0077] Step S22: Use a road surface texture tester (such as Figure 2 The domestically produced PATT-II pavement anti-skid texture tester shown in the figure is used as an example to scan the rutting plate specimen. The scanning area is 10 cm × 10 cm. The macro texture of the mixture is measured. Figure 3 The macro texture data is shown in Table 2:

[0078] Table 2 Tested mixture BPN and mixture macro texture data

[0079]

[0080] In one embodiment, the step S3: establishing a pavement anti-skid prediction model based on the extracted micro texture of the aggregate and the measured macro texture of the mixture, specifically comprises the following steps:

[0081] Step S31: obtaining the micro texture of the aggregate by measurement, and determining an associated micro texture index having a correlation with the anti-skid performance of the pavement exceeding a first preset threshold;

[0082] Step S32: determining, based on the macro texture of the mixture obtained by measurement, an associated macro texture index whose correlation with the anti-skid performance of the pavement exceeds a second preset threshold;

[0083] Step S33: Figure 4 As shown, the anti-skid performance BPN of asphalt mixture is obtained by testing the rutting plate specimen;

[0084] Step S34: establishing a pavement anti-skid prediction model based on the determined associated micro-texture index of the aggregate and the associated macro-texture index of the mixture and the anti-skid performance of the asphalt mixture obtained through testing.

[0085] In one embodiment, the step S31: obtaining the micro-texture of the aggregate by measurement, and determining the associated micro-texture index having a correlation with the pavement anti-skid performance exceeding a first preset threshold value, is specifically implemented as follows:

[0086]

[0087] Where: x i ,y i are two related variables, n is the number of samples, is the mean of the two variable samples, representing the micro-texture parameters and the pavement anti-skid performance respectively.

[0088] This application selects the micro texture average roughness Ra with a correlation greater than 0.6 微观 , micro texture root mean square roughness Rq 微观 And the height difference Rδc of the micro texture profile section 微观 As the associated microtexture index, it is used to construct the multivariate linear fitting parameters of the pavement skid resistance prediction model.

[0089] In one embodiment, the step S32: determining, based on the macro texture of the mixture obtained by measurement, an associated macro texture index having a correlation with the pavement anti-skid performance exceeding a second preset threshold, specifically comprises the following steps:

[0090] Step S321: Calculate the macro texture index MPD of the measured mixture according to the texture index formula 宏观 , Ra 宏观 , Rq 宏观 , Δq 宏观 , Rsk 宏观 , Rku 宏观 ;

[0091] Step S322: Pearson correlation coefficient is used to calculate the correlation between the anti-slip performance and the macro texture index, as shown in the following formula:

[0092]

[0093] Where: x i ,y i are two related variables, n is the number of samples, is the mean of the two variable samples, representing the macro texture parameters and the pavement anti-skid performance respectively;

[0094] That is, each macro texture index is substituted into the above formula to calculate its correlation with the anti-slip performance BPN.

[0095] Table 3 Pearson correlation coefficient between pendulum value and three-dimensional texture

[0096]

[0097]

[0098] Note: * indicates significant correlation at the 0.05 level (two-sided)

[0099] Step S323: Selecting a macro texture indicator whose correlation exceeds a second preset threshold as an associated macro texture indicator.

[0100] Generally speaking, |r|<0.2 is an extremely weak correlation, 0.2<|r|<0.4 is a weak correlation, 0.4<|r|<0.6 is a moderate correlation, 0.6<|r|<0.8 is a strong correlation, and |r| greater than 0.8 is an extremely strong correlation.

[0101] This application selects MPD with a correlation greater than 0.6 宏观 , Ra 宏观 , Rq 宏观 , Δq 宏观 As the associated macro-texture index, it is used to construct the multivariate linear fitting parameters of the pavement skid resistance prediction model.

[0102] In one embodiment, the road surface anti-skid prediction model is established according to the determined associated micro-texture index of the aggregate and the associated macro-texture index of the mixture and the anti-skid performance of the asphalt mixture obtained by testing, specifically comprising the following steps:

[0103] Multivariate linear fitting was performed on the associated micro-texture indices, the associated macro-texture indices of the mixture, and the anti-skid performance of the asphalt mixture obtained by testing, and a pavement anti-skid prediction model was established.

[0104] In one embodiment, the road surface anti-skid prediction model is as follows:

[0105]

[0106] Where BPN is the swing value, MPD 宏观 is the average contour depth of the macro texture, Ra 宏观 is the average roughness of the macro texture, Δq 宏观 is the variation of macro texture roughness, Ra 微观 is the average roughness of the micro texture, Rq 微观 is the root mean square roughness of the micro texture, Rδc 微观 is the height difference of the micro texture profile cross section.

[0107] Comparison between the predicted value and the measured value of the road skid resistance prediction model considering both macro and micro textures Figure 5 shown.

[0108] From the fitting results, it can be seen that the correlation of the pavement anti-skid prediction model fitting considering both the three-dimensional macro texture and aggregate micro texture indexes is significantly improved, R 2 Reached 0.8458.

[0109] In contrast, only the associated macro-texture index and the anti-skid performance of the asphalt mixture obtained by the test are used to construct a pavement anti-skid prediction model that only considers the macro-texture. Specifically, the multivariate linear equation is used to fit the relationship between the macro-texture parameters MPD, Ra, △q and the anti-skid performance BPN as shown in the following formula:

[0110] BPN=4.011MPD+6.604Ra 宏观 +3.770Δq 宏观 +40.678 (3)

[0111] Where MPD 宏观 is the average contour depth of the macro texture, Ra 宏观 is the average roughness of the macro texture, Δq 宏观 is the variation of macro texture roughness.

[0112] The comparison between the predicted value and the measured value of the pavement skid resistance prediction model considering only the macro-micro texture is shown in Figure 2. Figure 6 shown.

[0113] From the fitting results, it can be seen that the pavement anti-skid prediction model fitting only considering the macro texture index has a low correlation, R 2 It is only 0.6251, which has a large relative error.

[0114] In one embodiment, the step S4: predicting and obtaining the anti-skid performance index of the road surface based on the established road surface anti-skid prediction model is specifically implemented as follows:

[0115] Input the relevant micro-texture index of the aggregate of the road surface to be predicted for anti-skid and the relevant macro-texture index of the mixture into formula (3) to obtain the BPN prediction value of the road surface.

[0116] In a second aspect, the present application provides a road skid resistance prediction system that considers both macro and micro textures, including:

[0117] Micro texture acquisition module, used to extract aggregate micro texture;

[0118] Macro texture acquisition module, used to measure and obtain the macro texture of the mixture;

[0119] An anti-skid prediction model establishment module, which, together with the micro-texture acquisition module and the macro-texture acquisition module, establishes a pavement anti-skid prediction model based on the micro-texture of the extracted aggregate and the macro-texture of the mixture obtained by measurement;

[0120] The anti-skid performance prediction module is in communication connection with the anti-skid prediction model establishment module and is used to predict and obtain the anti-skid performance index of the road surface based on the established road surface anti-skid prediction model.

[0121] In one embodiment, the anti-slip prediction model building module includes:

[0122] A correlation micro-texture index acquisition unit, used to obtain the micro-texture of the aggregate according to the measurement, and determine the correlation micro-texture index whose correlation with the anti-skid performance of the pavement exceeds a first preset threshold;

[0123] A correlation macro texture index acquisition unit, used to determine, based on the macro texture of the mixture obtained by measurement, a correlation macro texture index having a correlation with the anti-skid performance of the pavement exceeding a second preset threshold;

[0124] The anti-skid performance acquisition unit is used to test the rutting plate specimen to obtain the anti-skid performance of the asphalt mixture;

[0125] The anti-skid prediction model acquisition unit is used to establish a pavement anti-skid prediction model based on the determined associated micro-texture index of the aggregate and the associated macro-texture index of the mixture and the anti-skid performance of the asphalt mixture obtained by testing.

[0126] Among them, the functional implementation of each module in the above-mentioned road surface anti-skid prediction system that simultaneously considers macro and micro textures corresponds to the steps in the above-mentioned road surface anti-skid prediction method embodiment that simultaneously considers macro and micro textures, and their functions and implementation processes will not be repeated here one by one.

[0127] On the third aspect, an embodiment of the present application provides a road surface anti-skid prediction device that takes into account both macro and micro textures. The road surface anti-skid prediction device that takes into account both macro and micro textures can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0128] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to interconnect the devices inside the road surface anti-skid prediction device that considers both macro and micro textures, and an interface used to interconnect the road surface anti-skid prediction device that considers both macro and micro textures with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0129] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0130] The processor may be a general-purpose processor, which may call the road surface anti-skid prediction program taking into account macro-micro textures stored in the memory, and execute the road surface anti-skid prediction method taking into account macro-micro textures provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the road surface anti-skid prediction program taking into account macro-micro textures is called may refer to the various embodiments of the road surface anti-skid prediction method taking into account macro-micro textures in the present application, which will not be described in detail here.

[0131] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.

[0132] The readable storage medium of the present application stores a road surface anti-skid prediction program that simultaneously considers macro and micro textures. When the road surface anti-skid prediction program that simultaneously considers macro and micro textures is executed by a processor, the steps of the road surface anti-skid prediction method that simultaneously considers macro and micro textures as described above are implemented.

[0133] Among them, the method implemented when the road surface anti-skid prediction program that simultaneously considers macro and micro textures is executed can refer to the various embodiments of the road surface anti-skid prediction method that simultaneously considers macro and micro textures of the present application, and will not be repeated here.

[0134] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0135] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0136] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A road surface anti-skid prediction method that considers both macro and micro textures, characterized in that: The following steps are involved: Extract aggregate microtexture; Measure and obtain the macro texture of the mixture; A pavement skid resistance prediction model is established based on the micro texture of the extracted aggregate and the macro texture of the mixture obtained by measurement; Based on the established pavement anti-skid prediction model, the pavement anti-skid performance index is predicted and obtained.

2. The method for predicting road surface skid resistance by considering both macro and micro textures as claimed in claim 1, characterized in that: The method of extracting aggregate micro texture specifically comprises the following steps: Wash and dry the aggregate; The dried aggregate is scanned microscopically to measure and obtain the microscopic texture of the aggregate.

3. The method for predicting road surface anti-skid performance by considering both macro and micro textures as claimed in claim 1, characterized in that: The measurement to obtain the macro texture of the mixture specifically includes the following steps: Prepare asphalt mixture and form rutting plate specimens; The rutting plate specimens were scanned to measure the macro texture of the mixture.

4. The method for predicting road surface anti-skid performance by considering both macro and micro textures as claimed in claim 1, characterized in that: The method of establishing a pavement anti-skid prediction model based on the extracted micro-texture of the aggregate and the measured macro-texture of the mixture specifically includes the following steps: According to the measured micro-texture of the aggregate, determining a correlation micro-texture index having a correlation with the pavement skid resistance performance exceeding a first preset threshold; Determining, based on the macro texture of the mixture obtained by measurement, a correlation macro texture index having a correlation with the anti-skid performance of the pavement exceeding a second preset threshold; The anti-skid performance of asphalt mixture is obtained by testing the rutting plate specimens; A pavement anti-skid prediction model is established based on the determined associated micro-texture indexes of aggregates and the associated macro-texture indexes of mixtures as well as the anti-skid performance of asphalt mixtures obtained by testing.

5. The method for predicting road surface anti-skid performance by considering both macro and micro textures as claimed in claim 4, characterized in that: The associated microtexture indicators are microtexture average roughness, microtexture root mean square roughness and microtexture depth correlation coefficient.

6. The method for predicting road surface anti-skid performance by considering both macro and micro textures as claimed in claim 4, characterized in that: The step of determining, based on the macro texture of the mixture obtained by measurement, an associated macro texture index having a correlation with the pavement skid resistance performance exceeding a second preset threshold value specifically comprises the following steps: The macro texture of the mixture obtained by measurement is calculated according to the texture index formula to obtain a macro texture index; The Pearson correlation coefficient is used to calculate the correlation between the anti-slip performance and the macro-texture index, and the macro-texture index whose correlation exceeds the second preset threshold is selected as the associated macro-texture index.

7. The method for predicting road surface anti-skid performance by considering both macro and micro textures as claimed in claim 1, characterized in that: The method of establishing a pavement anti-skid prediction model based on the determined associated micro-texture index of the aggregate and the associated macro-texture index of the mixture and the anti-skid performance of the asphalt mixture obtained by testing specifically includes the following steps: Multivariate linear fitting was performed on the associated micro-texture indices, the associated macro-texture indices of the mixture, and the anti-skid performance of the asphalt mixture obtained by testing, and a pavement anti-skid prediction model was established.

8. The method for predicting road surface anti-skid performance by considering both macro and micro textures as claimed in claim 7, characterized in that: The road surface anti-skid prediction model is shown in the following formula: BPN=1.787MPD 宏观 +5.733Ra 宏观 +2.795Δq 宏观 -1.130Ra 微观 +1.189Rq 微观 +0.470Rδc 微观 +36.290 Where BPN is the swing value, MPD 宏观 is the average contour depth of the macro texture, Ra 宏观 is the average roughness of the macro texture, Δq 宏观 is the variation of macro texture roughness, Ra 微观 is the average roughness of the micro texture, Rq 微观 is the root mean square roughness of the micro texture, Rδc 微观 is the height difference of the micro texture profile cross section.

9. A road skid prediction system that considers both macro and micro textures, characterized in that: include: Micro texture acquisition module, used to extract aggregate micro texture; Macro texture acquisition module, used to measure and obtain the macro texture of the mixture; An anti-skid prediction model establishment module, which, together with the micro-texture acquisition module and the macro-texture acquisition module, establishes a pavement anti-skid prediction model based on the micro-texture of the extracted aggregate and the macro-texture of the mixture obtained by measurement; The anti-skid performance prediction module is in communication connection with the anti-skid prediction model establishment module and is used to predict and obtain the anti-skid performance index of the road surface based on the established road surface anti-skid prediction model.

10. The road surface anti-skid prediction system considering both macro and micro textures as claimed in claim 9, characterized in that: The anti-slip prediction model building module includes: A correlation micro-texture index acquisition unit, used to obtain the micro-texture of the aggregate according to the measurement, and determine the correlation micro-texture index whose correlation with the anti-skid performance of the pavement exceeds a first preset threshold; A correlation macro texture index acquisition unit, used to determine, based on the macro texture of the mixture obtained by measurement, a correlation macro texture index having a correlation with the anti-skid performance of the pavement exceeding a second preset threshold; The anti-skid performance acquisition unit is used to test the rutting plate specimen to obtain the anti-skid performance of the asphalt mixture; The anti-skid prediction model acquisition unit is used to establish a pavement anti-skid prediction model based on the determined associated micro-texture index of the aggregate and the associated macro-texture index of the mixture and the anti-skid performance of the asphalt mixture obtained by testing.

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