Pavement crack maintenance scheme decision-making method, apparatus and device, and storage medium

Abstract

The present invention belongs to the technical field of road maintenance, and relates to a pavement crack maintenance scheme decision-making method, apparatus and device, and a storage medium. The method comprises: on the basis of collected image data and electrical signal data, locating a pavement crack distress to obtain pavement crack distress position information; calculating the image crack degree and overall structural layer modulus of a pavement area corresponding to the pavement crack distress position information; on the basis of the image crack degree and the overall structural layer modulus, determining a plurality of pavement crack distress maintenance schemes; and establishing a pavement performance evaluation system, and on the basis of the pavement serviceability evaluation system and historical pavement maintenance data, evaluating different pavement crack distress maintenance schemes to be implemented in the next year, so as to determine a target pavement crack maintenance scheme. In the present invention, when the annual data of a pavement is insufficient, different maintenance schemes to be implemented in the next year are evaluated by means of establishing a pavement performance evaluation system, so as to determine a target pavement crack maintenance scheme.

Description

A pavement crack maintenance scheme decision method, device, equipment and storage medium TECHNICAL FIELD

[0001] The present application relates to the technical field of road maintenance, and in particular to a pavement crack maintenance scheme decision method, device, equipment and storage medium. BACKGROUND

[0002] Pavement crack maintenance is an important link to ensure the service life and service quality of the pavement, and reasonable maintenance measures are an important means to prolong the service life of the pavement. With more and more highways entering the preventive maintenance and major repair period, how to make scientific decisions and achieve precise and efficient maintenance to maximize the value of funds will become a difficult problem to be solved in future maintenance work.

[0003] In the prior art, the research focus of pavement crack maintenance decision is on the establishment of specific maintenance methods and maintenance decision models, and the establishment of the maintenance decision model is mainly based on the prediction of the change trend of the performance index, a method of fitting the decay of the performance detection data for many years is adopted, and the maintenance intervention time is determined according to the time when the performance index decays to the maintenance critical value.

[0004] However, when the year data of the pavement is less, the error of the maintenance decision method in the prior art used for determining the maintenance intervention time will increase, and when the maintenance effect is evaluated, there is a defect of insufficient use of the pavement structure performance and historical maintenance data, resulting in poor effect of pavement crack maintenance.

[0005] SUMMARY

[0006] Therefore, it is necessary to provide a pavement crack maintenance scheme decision method, device, equipment and storage medium to solve the problem that the error of the maintenance intervention time determination of the prior art increases when the year data of the pavement is less, and the defect of insufficient use of the pavement structure performance and historical maintenance data leads to poor effect of pavement crack maintenance.

[0007] To solve the above problems, the present application provides a pavement crack maintenance scheme decision method, comprising:

[0008] Positioning the pavement crack disease according to the collected image data and electrical signal data to obtain pavement crack disease position information;

[0009] Calculate the image crack degree and the overall modulus of the structure layer of the pavement area corresponding to the pavement crack disease position information;

[0010] Determine a plurality of pavement crack disease maintenance schemes based on the image crack degree and the overall modulus of the structure layer;

[0011] A pavement performance evaluation system is established, and based on the pavement performance evaluation system and historical pavement maintenance data, different pavement crack maintenance schemes intervened in the second year are evaluated to determine a target pavement crack maintenance scheme.

[0012] In a possible implementation, the image crack degree of the pavement area corresponding to the pavement crack disease position information and the overall modulus of the structural layer are calculated, including:

[0013] Obtain radar detection image data from the road section corresponding to the pavement crack disease position information;

[0014] Extract information based on the radar detection image data and calculate the image crack degree;

[0015] Fit the correlation between the image crack degree and the overall modulus of the structural layer, and calculate the overall modulus of the structural layer based on the correlation.

[0016] In a possible implementation, the information extraction based on the radar detection image data and the calculation of the image crack degree include:

[0017] Extract the crack image influence depth at the vertical staggered stripe and the pile number at the vertical staggered stripe from the radar detection image data;

[0018] Calculate the standard deviation of the pile number distribution and the average value of the crack image influence depth according to the crack image influence depth at the vertical staggered stripe and the pile number at the vertical staggered stripe;

[0019] Calculate the image crack degree through the standard deviation of the pile number distribution and the average value of the crack image influence depth.

[0020] In a possible implementation, the correlation between the image crack degree and the overall modulus of the structural layer is fitted, and the overall modulus of the structural layer is calculated based on the correlation, including:

[0021] Perform a drop hammer deflection detection on the road section corresponding to the pavement crack disease position information;

[0022] Fit the correlation between the image crack degree and the overall modulus of the structural layer based on the deflection detection result and the image crack degree;

[0023] Input the image crack degree into the correlation to calculate the overall modulus of the structural layer.

[0024] In a possible implementation, a plurality of pavement crack maintenance schemes are determined based on the image crack degree and the overall modulus of the structural layer, including:

[0025] Analyze the pavement structural layer bearing capacity according to the image crack degree and the overall modulus of the structural layer;

[0026] Different multiple pavement crack disease maintenance schemes are established based on the bearing capacity of the pavement structure layer.

[0027] In a possible implementation, a pavement use performance evaluation system is established, and different pavement crack disease maintenance schemes intervened in the second year are evaluated based on the pavement use performance evaluation system and pavement historical maintenance data to determine a target pavement crack maintenance scheme, including:

[0028] A pavement use performance evaluation system with a pavement use performance improvement index as a target is established based on a pavement damage condition index and a pavement driving quality index;

[0029] Parameters and weight coefficients corresponding to the pavement damage condition index and the pavement driving quality index of different pavement crack disease maintenance schemes are calculated;

[0030] The pavement use performance improvement indexes of different pavement crack disease maintenance schemes intervened in the second year are calculated according to the parameters, the weight coefficients and the pavement historical maintenance data;

[0031] The target pavement crack maintenance scheme is determined based on the pavement use performance improvement indexes of different pavement crack disease maintenance schemes intervened in the second year.

[0032] In a possible implementation, the parameters and the weight coefficients corresponding to the pavement damage condition index and the pavement driving quality index of different pavement crack disease maintenance schemes are calculated, including:

[0033] The improvement correction value and the decay rate improvement value of the pavement damage condition index and the improvement correction value and the decay rate improvement value of the pavement driving quality index of different pavement crack disease maintenance schemes are respectively calculated;

[0034] The weight coefficients corresponding to the improvement correction value and the decay rate improvement value of the pavement damage condition index and the improvement correction value and the decay rate improvement value of the pavement driving quality index are determined by using a principal component analysis method.

[0035] The application further provides a pavement crack maintenance scheme decision device, including:

[0036] A crack positioning module is configured to position pavement crack diseases according to collected image data and electrical signal data to obtain pavement crack disease position information;

[0037] A parameter calculation module is configured to calculate an image crack degree and a structure layer overall modulus of a pavement area corresponding to the pavement crack disease position information;

[0038] A scheme formulation module is configured to determine multiple pavement crack disease maintenance schemes based on the image crack degree and the structure layer overall modulus;

[0039] The evaluation decision module is configured to establish a road surface use performance evaluation system and evaluate different road crack maintenance schemes intervened in the second year based on the road surface use performance evaluation system and historical road maintenance data to determine a target road crack maintenance scheme.

[0040] In a third aspect, the present application further provides a road crack maintenance scheme decision device, comprising a memory and a processor, wherein,

[0041] The memory is configured to store a program.

[0042] The processor is coupled to the memory and configured to execute the program stored in the memory to implement the steps in the road crack maintenance scheme decision method in any of the above implementation manners.

[0043] In a fourth aspect, the present application further provides a computer readable storage medium configured to store a computer readable program or instruction, which, when executed by a processor, can implement the steps in the road crack maintenance scheme decision method in any of the above implementation manners.

[0044] The road crack maintenance scheme decision method provided by the present application can position road crack diseases through image data and electrical signal data, determine the positions of the road crack diseases, accurately position the positions of the road crack diseases, calculate image crack degrees and overall structure layer moduli, analyze the road crack diseases, formulate different maintenance schemes, establish a road surface use performance evaluation system, analyze the maintenance conditions in the second year of the non-intervened maintenance scheme and the different maintenance schemes with only a small amount of historical road maintenance data, evaluate the different maintenance schemes, determine the maintenance effects of the different maintenance schemes when the annual data of the road surface is small, and determine a target road crack maintenance scheme, so that the best effect of road crack maintenance can be achieved. BRIEF DESCRIPTION OF DRAWINGS

[0045] Fig. 1 is a flowchart of an embodiment of the road crack maintenance scheme decision method provided by the present application;

[0046] Fig. 2 is a schematic diagram of an embodiment of crack identification by the ground penetrating radar provided by the present application;

[0047] Fig. 3 is a flowchart of an embodiment of step S102 in Fig. 1 provided by the present application;

[0048] Fig. 4 is a flowchart of an embodiment of step S302 in Fig. 3 provided by the present application;

[0049] Fig. 5 is a flowchart of an embodiment of step S303 in Fig. 3 provided by the present application;

[0050] Fig. 6 is a scatter diagram of an embodiment of the image crack degree and the structural layer overall modulus after logarithmic transformation according to the present application;

[0051] Fig. 7 is a fitting relationship diagram of an embodiment of the image crack degree and the structural layer overall modulus in logarithmic coordinates according to the present application;

[0052] Fig. 8 is a flow diagram of an embodiment of step S104 in Fig. 1 according to the present application;

[0053] Fig. 9 is a structural diagram of an embodiment of a pavement crack maintenance scheme decision device according to the present application;

[0054] Fig. 10 is a structural diagram of a pavement crack maintenance scheme decision device according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] The preferred embodiments of the present application will be described in detail below with reference to the drawings, which form a part of this application. The drawings and the associated descriptions are provided to illustrate the preferred embodiments of the present application and to explain the principles of the present application, but are not intended to limit the scope of the present application.

[0056] Please refer to Fig. 1, which is a flow diagram of an embodiment of a pavement crack maintenance scheme decision method according to the present application. In one specific embodiment of the present application, a pavement crack maintenance scheme decision method is disclosed, which comprises the following steps:

[0057] S101, positioning the pavement crack disease according to the collected image data and electrical signal data to obtain pavement crack disease position information;

[0058] S102, calculating the image crack degree and the structural layer overall modulus of the pavement area corresponding to the pavement crack disease position information;

[0059] S103, determining a plurality of pavement crack disease maintenance schemes based on the image crack degree and the structural layer overall modulus;

[0060] S104, establishing a pavement performance evaluation system and evaluating the different pavement crack disease maintenance schemes intervened in the second year based on the pavement performance evaluation system and the pavement historical maintenance data to determine a pavement crack target maintenance scheme.

[0061] In the above embodiment, the different dielectric properties of different pavement structural layers can be used to identify and extract the pavement structural layer thickness by using a ground penetrating radar detection vehicle. Compared with the traditional pavement core sampling method for measuring thickness, the ground penetrating radar has the advantages of deeper measurement depth, higher automation degree and higher precision.

[0062] As a preferred embodiment, the Beijing-Zhuhai Expressway in Hubei Province is analyzed by PAVECHECK software. In the preparation work, the radar detection needs to be divided into multiple sections due to the long mileage of the Beijing-Zhuhai Expressway in Hubei Province. First, the original detection data file stored in the.prj format needs to be accurately imported. This file contains image data of the front of the vehicle captured by the roof camera during radar monitoring and raw electrical signal data of electromagnetic wave detection. Then, the display interval of the dielectric constant is set, and the contrast slider on the right side of the radar image area is adjusted to make the radar image clear and visible.

[0063] Before the detection of each small section of the road surface, the actual mileage marker number R0 on the guardrail at the starting point is read. After starting at the starting point, the initial mileage S0 at the lower left corner of the photo area is recorded. The actual mileage marker number is calculated according to formula (1) to complete the mileage calibration, thereby determining the location information of the road surface crack disease. It should be noted that the marker number at the lower left corner of the photo area will gradually increase in the direction of travel, so there is a "±" symbol before the bracket. R0(S x -S0)=R x (1)

[0064] In the formula: R0 is the actual marker number at the starting point; S x is the marker number at the observation point at the lower left corner of the photo area; S0 is the marker number at the starting point of the photo area; and R x is the actual marker number at the observation point.

[0065] Please refer to FIG. 2, which is a schematic diagram of an embodiment of the identification of cracks by ground penetrating radar provided by the present application. Compared with normal road surface, the propagation path of electromagnetic waves at the crack will be about two crack depths longer. This will result in a longer propagation time, i.e., the time of electromagnetic waves reaching the interface between air and surface layer is prolonged, which is further reflected in the image as color layering error of the image.

[0066] For the image crack degree P, the radar detection of the detected road section is performed by the WB1-21 type ground penetrating radar detection vehicle. The obtained detection data file is imported into the PAVECHECK software. The contrast of the radar image is adjusted by back calculation, so that the vertical layering stripes of the crack road section are exposed. The depth and marker number at the layering stripe are extracted by the difference of the voltage image peak value, thereby calculating the image crack degree.

[0067] For the overall modulus Ex of the structural layer, the deflection detection of the corresponding road section is performed by the 7-150kN type drop hammer deflection detection vehicle. The obtained deflection basin data is input into the SIDMOD software to perform the modulus back calculation of the structure form. Then the correlation between the image crack degree P and the overall modulus Ex of the structural layer is further determined, so as to calculate the overall modulus of the structural layer according to the image crack degree.

[0068] According to the analysis results of the crack degree and the overall modulus of the structural layer, the main target of maintenance is determined, such as improving the road flatness, enhancing the bearing capacity, prolonging the service life, etc. Then, according to the type and severity of the cracks and the performance status of the structural layer, a suitable maintenance scheme is selected. For small-area cracks, local repair methods such as filling and sealing can be used; for large-area cracks or structural layer performance degradation, overall reinforcement or reconstruction may be required.

[0069] For road sections with pavement crack diseases, only one year of historical maintenance data is needed. By comparing different maintenance schemes in the second year, the maintenance effect of different maintenance schemes is evaluated through the pavement performance evaluation system, and the target maintenance scheme for pavement cracks is finally determined to achieve the best effect of pavement crack maintenance.

[0070] Compared with the prior art, the pavement crack maintenance scheme decision method provided by the embodiment can locate the pavement crack disease through image data and electrical signal data, determine the position of the pavement crack disease, accurately locate the position of the pavement crack disease, calculate the image crack degree and the overall modulus of the structural layer, analyze the pavement crack disease, develop different maintenance schemes, establish a pavement performance evaluation system, and only a small amount of historical pavement maintenance data is needed to analyze the maintenance situation in the second year without intervention and with different maintenance schemes. Different maintenance schemes are evaluated, and the maintenance effect of different maintenance schemes can be determined when the road data is less in a year, so that the target maintenance scheme for pavement cracks can be determined to achieve the best effect of pavement crack maintenance.

[0071] Please refer to FIG. 3, which is a flowchart of an embodiment of step S102 in FIG. 1 provided by the present application. In some embodiments of the present application, the image crack degree and the overall modulus of the structural layer of the road area corresponding to the pavement crack disease position information are calculated, including:

[0072] S301, acquiring radar detection image data from the road section corresponding to the pavement crack disease position information;

[0073] S302, information extraction based on radar detection image data and calculation of image crack degree;

[0074] S303, fitting the correlation between the image crack degree and the overall modulus of the structural layer, and calculating the overall modulus of the structural layer based on the correlation.

[0075] In the above embodiment, for the radar image crack degree P, this embodiment uses a WB1-21 type ground penetrating radar detection vehicle to detect the road section to be detected. The collected detection data file is imported into the PAVECHECK software, and the contrast of the radar image is adjusted to make the vertical staggered strip of the crack section clear. Thus, the image crack degree is calculated based on the radar detection image data.

[0076] Referring to FIG. 4, FIG. 4 is a flowchart of an embodiment of step S302 in FIG. 3 provided by the present application. In some embodiments of the present application, the image crack degree is calculated based on the radar detection image data, including:

[0077] S401, extracting the crack image influence depth at the vertical staggered strip and the stake number at the vertical staggered strip according to the radar detection image data;

[0078] S402, calculating the stake number distribution standard deviation and the crack image influence depth average value according to the crack image influence depth at the vertical staggered strip and the stake number at the vertical staggered strip;

[0079] S403, calculating the image crack degree by the stake number distribution standard deviation and the crack image influence depth average value.

[0080] In the above embodiment, by the difference of the voltage image peak value, this embodiment can extract the depth and the stake number at the staggered strip. Finally, according to the calculation formula of formula (2) to formula (5), this embodiment can obtain the radar image crack degree P of the corresponding road section.

[0081] In the formula, x i is the stake number at the vertical staggered strip in the radar image; d i is the crack image influence depth at each vertical staggered strip; n is the number of vertical staggered strips; is the stake number average value considering the crack influence depth; s is the stake number distribution standard deviation considering the crack influence depth; is the crack image influence depth average value; P is the image crack degree.

[0082] Referring to FIG. 5, FIG. 5 is a flowchart of an embodiment of step S303 in FIG. 3 provided by the present application. In some embodiments of the present application, the correlation between the image crack degree and the overall modulus of the structure layer is fitted, and the overall modulus of the structure layer is calculated based on the correlation, including:

[0083] S501, performing a falling weight deflectometer detection on the road section corresponding to the pavement crack disease position information;

[0084] S502, fitting based on the deflection detection result and the image crack degree to obtain a correlation between the image crack degree and the overall modulus of the structural layer;

[0085] S503, inputting the image crack degree into the correlation to calculate the overall modulus of the structural layer.

[0086] In the above embodiment, for the overall modulus Ex of the pavement structural layer, the 7-150kN type drop hammer deflection detection vehicle is used to detect the deflection of the corresponding road section, the obtained deflection detection result is input into the SIDMOD software for processing, and the modulus of each layer of the pavement is inversely calculated. Finally, the modulus of the surface layer and the base layer combined layer in the inverse calculation result is substituted into formula (6) to (8) with the radar measured thickness data of the corresponding point, and the overall modulus E X and the equivalent thickness h x of the pavement structural layer is calculated. The calculation result of the road section is presented in the form of an arithmetic mean value.

[0087] In the formula, E x is the overall modulus of the pavement structural layer, h1 is the thickness of the surface layer, h2 is the thickness of the base layer combined layer, E1 is the inverse calculated modulus of the surface layer, E2 is the inverse calculated modulus of the base layer combined layer, h x is the overall structural layer thickness after conversion, k u is the interlayer contact condition coefficient, the interlayer is continuous k u 1, and the sliding k u 0.

[0088] The present application provides three road section embodiments to describe the calculated overall modulus E x and the equivalent thickness h x of the structural layer.

[0089] The No. 1 road section is located in the Hubei section G50 of the Beijing-Hong Kong-Macao Expressway, and the PAVECHECK pile number section is K7+625-K7+700. The overall modulus E x and the equivalent thickness h x of the pavement structural layer of the K7+625-K7+700 section are calculated, and the calculation result is shown in Table 1.

[0090] Table 1 Calculation result table of the overall modulus and the equivalent thickness of the pavement structural layer of the No. 1 road section

[0091] The No. 2 road section is located in the Hubei section G50 of the Beijing-Hong Kong-Macao Expressway, and the PAVECHECK pile number section is K9+508-K9+611. The overall modulus E x and the equivalent thickness h x of the pavement structural layer are calculated, and the calculation result is shown in Table 2.

[0092] Table 2 Road surface structure layer modulus and equivalent thickness calculation results table of No. 2 section

[0093] No. 3 section is located on the Hubei section of Beijing-Hong Kong-Macao Expressway G50 section, the pile number section in PAVECHECK is K11+587-K11+713, and the road surface structure layer modulus E x and equivalent thickness h x are calculated. As shown in Table 3:

[0094] Table 3 Road surface structure layer modulus and equivalent thickness calculation results table of No. 3 section

[0095] Please refer to FIG. 6, which is a scatter diagram of an embodiment of the image crack degree and the structure layer modulus after taking logarithm according to the present application, please refer to FIG. 7, which is a fitting relationship diagram of an embodiment of the image crack degree and the structure layer modulus in logarithmic coordinates according to the present application, and the connection of the two types of indexes is observed by dotting method. Since the order of magnitude of the road surface structure layer modulus E x is large, in order to better observe the correlation of the two, the image crack degree and the road surface structure layer modulus are all taken as logarithmic coordinates with base 10, as shown in Table 4. The data is plotted in the rectangular coordinate system in the form of scatter diagram, as shown in FIG. 6.

[0096] After analyzing the road surface structure layer modulus E x of each deflection measuring point in the transverse crack 3 section, it is found that 4000MPa can be used as the critical value for preventive maintenance and corrective maintenance.

[0097] Table 4 Image crack degree and road surface structure layer modulus of No. 1-3 section

[0098] As shown in FIG. 6, the logarithmic values of the image crack degree P under longitudinal and transverse cracks and the road surface structure layer modulus E x have good non-linear negative correlation. The trend is fitted based on the Boltzmann formula by using the non-linear curve fitting function of the drawing software, and the goodness of fit R can reach 0.9968. The established fitting formula can realize the prediction of the road surface structure layer modulus E x of the section of the Hubei section of Beijing-Hong Kong-Macao Expressway only by radar image crack degree P when ground penetrating radar detection is performed on the cracked pavement of the section, or realize the prediction of the radar image crack degree P only by calculating the road surface structure layer modulus E x based on the falling weight deflectometer detection result in the case of known crack disease.

[0099] In some embodiments of the present application, a plurality of pavement crack disease maintenance schemes are determined based on the image crack degree and the overall modulus of the structural layer, comprising:

[0100] The bearing capacity of the pavement structural layer is analyzed according to the image crack degree and the overall modulus of the structural layer.

[0101] Different plurality of pavement crack disease maintenance schemes are established based on the bearing capacity of the pavement structural layer.

[0102] In the above embodiments, the quantitative evaluation value of the bearing capacity of each layer and the overall bearing capacity of the pavement is calculated according to the image crack degree and the overall modulus of the structural layer, so as to determine the area with insufficient bearing capacity under crack disease.

[0103] The analysis results of the bearing capacity of the 1-3 road sections and the introduction of the corrective maintenance of the 1-3 road sections in 2016 are summarized, as shown in Table 5.

[0104] Table 5 Summary table of specific disease characteristics and maintenance methods of 1-3 road sections

[0105] Referring to FIG. 8, FIG. 8 is a flowchart of an embodiment of step S104 in FIG. 1 provided by the present application. In some embodiments of the present application, a pavement performance evaluation system is established, and different pavement crack disease maintenance schemes intervened in the second year are evaluated based on the pavement performance evaluation system and the pavement historical maintenance data to determine the target pavement crack maintenance scheme, comprising:

[0106] S801, a pavement performance evaluation system with a pavement performance improvement index as the target is established based on a pavement damage condition index and a pavement riding quality index;

[0107] S802, parameters and weight coefficients corresponding to the pavement damage condition index and the pavement riding quality index of different pavement crack disease maintenance schemes are calculated;

[0108] S803, the pavement performance improvement index of different pavement crack disease maintenance schemes intervened in the second year is calculated according to the parameters, the weight coefficients and the pavement historical maintenance data;

[0109] S804, the target pavement crack maintenance scheme is determined based on the pavement performance improvement index of different pavement crack disease maintenance schemes intervened in the second year.

[0110] In the above embodiments, the parameters corresponding to the pavement riding quality index are the pavement damage condition index (PCI) and the pavement riding quality index (RQI), and the quantitative evaluation of the improvement of the pavement performance by the maintenance means is made from the two aspects of the pavement damage condition index (PCI) and the pavement riding quality index (RQI).

[0111] Each type of index can be quantitatively calculated from the increase of its value itself and the decrease of the absolute value of its decay rate. Thus, an evaluation system for the improvement of road surface performance by maintenance means can be constructed, that is, a PQI evaluation system, expressed as formula (10): PQI = A x w A + B x w B + C x w C + D x w D (10);

[0112] In the formula, PQI is a road surface performance improvement index; A is a PCI improvement correction value (%), w A is a corresponding weight coefficient; B is a PCI decay rate improvement value (%), w B is a corresponding weight coefficient; C is a RQI improvement correction value (%), w C is a corresponding weight coefficient; D is a RQI decay rate improvement value (%), w D is a corresponding weight coefficient.

[0113] According to formula (10), the PQI of the 1-3 road sections under the maintenance intervention in 2016 is calculated, and the results are shown in Table 6:

[0114] Table 6 PQI index calculation results of 1-3 road sections under maintenance intervention in 2016

[0115] As can be seen from Table 6, for the 1-3 road sections of the road surface under the transverse crack disease, the order of the road surface performance improvement effect by the maintenance means in 2016 from good to bad is the 3rd road section, the 2nd road section and the 1st road section.

[0116] In some embodiments of the present application, the parameters and weight coefficients corresponding to the road damage condition index and the road driving quality index of different road crack disease maintenance schemes are calculated, including:

[0117] The improvement correction value and the decay rate improvement value of the road damage condition index and the improvement correction value and the decay rate improvement value of the road driving quality index of different road crack disease maintenance schemes are calculated respectively;

[0118] The weight coefficients corresponding to the improvement correction value and the decay rate improvement value of the road damage condition index and the improvement correction value and the decay rate improvement value of the road driving quality index are determined by using principal component analysis method.

[0119] In the above embodiments, referring to the classification limit of PCI in the Technical Specification for Maintenance of Highway Asphalt Pavement, as shown in Table 7, the difference between the upper limit and the lower limit of the grade range of all PCI values of each road section is determined to obtain the maximum PCI improvement range ΔPCI maxFinally, the PCI promotion correction value A is obtained by calculating the ratio of the PCI promotion amplitude ΔPCI and the maximum PCI promotion amplitude ΔPCI max The calculation formula is shown in equation (11):

[0120] Table 7 PCI grading boundary table in Technical Specification for Maintenance of Highway Asphalt Pavement

[0121] In the formula, ΔPCI is the PCI promotion amplitude, and ΔPCImax is the maximum PCI promotion amplitude.

[0122] According to the Sun decay model, the absolute value of the PCI decay rate is reduced by a percentage to obtain B when calculating the PCI decay rate improvement value B. It can be known that the absolute value of the PCI decay rate reduction amplitude itself is a percentage between 0 and 1, so the percentage after taking the value is still itself.

[0123] Similarly, for the RQI index, the RQI promotion correction value C and the RQI decay rate improvement value D can be obtained by similar calculation method and percentage processing.

[0124] After the percentage processing of the sub-indexes is completed, the weights of each index need to be determined. The PQI evaluation system can be regarded as a comprehensive evaluation system composed of four factors. Since the relevance between the four factors is unknown, a multi-factor weight determination method is used to determine the weight of each sub-index.

[0125] The patent uses principal component analysis to determine the main influencing factors in A, B, C, and D, and quantifies them as the weights of each sub-index:

[0126] (1) Construct the original data matrix X:

[0127] The original data refers to the value of each index collected before principal component analysis. In this section, the original data refers to the PCI promotion correction value A, the PCI decay rate improvement value B, the RQI promotion correction value C, and the RQI decay rate improvement value D of road sections 1-3. The form is shown in equation (12):

[0128] In the formula, x ij is the value of the jth variable in the ith sample.

[0129] In this embodiment, the number of road sections is 3, and the number of index types is 4. Therefore, in the original data matrix, the value of m is 3, and the value of p is 4, and a 3×4 original data matrix can be constructed.

[0130] In the process of constructing the original data, the positive and negative of the index data need to be judged. For A, B, C, D four kinds of indicators, the greater the value, the higher the contribution to the overall, are all positive data, so there is no need to transform the most original data.

[0131] (2) Standardization of the original matrix:

[0132] The standardization of the original matrix is to eliminate the inoperability between various types of data caused by different dimensions of the original data. The commonly used methods are mean standardization, difference standardization and ratio standardization. Here, the mean standardization method is used to standardize the original data, as shown in equations (13) and (14):

[0133] In the formula: z ij is the value of the jth variable in the ith group of samples after standardization; x j is the average value of the jth column data in the original data matrix; s j is the standard deviation of the jth column data in the original data matrix.

[0134] (3) Calculate the correlation coefficient matrix:

[0135] The correlation coefficient matrix refers to the matrix composed of the correlation coefficients between each pair of indicators. Its definition can be written as a p x p matrix R = (r jk ) p×p , where the expression of the correlation coefficient r jk is shown in equation (15):

[0136] In the formula: r jk = r kj ; r jj = 1.

[0137] The correlation coefficient matrix is composed of correlation coefficients, which reflect the degree of association between different types of indicators. Therefore, the correlation coefficient of element j and element k is equal to the correlation coefficient of element k and element j, and the correlation coefficient of element j and element j is 1.

[0138] (4) Solve the eigenvalue and eigenvector:

[0139] From the definition of the equation for solving the eigenvector in higher linear algebra, the characteristic equation as shown in equation (16) can be constructed to solve the corresponding eigenvalue and its eigenvector. |λ E -R| = 0 (16)

[0140] Through the above formula, p eigenvalues λ E and the eigenvectors L g under the corresponding eigenvalues can be solved.E The variance of the principal component is reflected in the numerical value. The greater the variance, the greater the influence of the principal component on the evaluation object.

[0141] (5) Determine the principal component:

[0142] Each eigenvalue λ E corresponds to a characteristic vector, and each characteristic vector corresponds to a principal component. The calculation formula is shown in equation (17), and its structure can be briefly summarized as the linear accumulation of the pth value in the characteristic vector and the standardized pth index column vector. g = lg1Z1+lg2Z2+lg3Z3+…+lg p Z p (17)

[0143] In the formula: l g is the characteristic vector element, and the characteristic vector L g = (l g1 , l g2 , …, l gp ) T

[0144] F g is the gth principal component.

[0145] For a total of p principal components, first sort the eigenvalues λ E , i.e. the variance, in descending order according to λ1≥λ2≥λ3≥…≥λp≥0. According to the same order, list its corresponding principal components F1, F2, …, F p . Among them, F p is the pth principal component.

[0146] Select the first k principal components with cumulative variance contribution rate greater than 80% to describe the original object. The selection principle and mathematical expression of the result are shown in equations (18) and (19).

[0147] For the principal component analysis process, use data analysis software to complete. Output the component matrix, which contains data such as the initial factor load f ij , but l ij and f ij have a conversion relationship as shown in equation (20).

[0148] Finally, multiply l ij and the corresponding original data of each section under each type of index and sum them up to obtain the partial index V ij . Then calculate the proportion of each partial index V ij in the total Vij and the ratio of the values as the weight of each sub-index.

[0149] According to formula (20), the value l in the characteristic vector can be obtained ij The results are shown in Table 8:

[0150] Table 8 f ij Solve l jj Case table

[0151] The solving results of the four sub-index weight coefficients are shown in Table 9:

[0152] Table 9 Solving results of sub-index weight coefficients under the PQI index system based on principal component analysis method

[0153] In order to better implement the pavement crack maintenance scheme decision method in the embodiment of the application, on the basis of the pavement crack maintenance scheme decision method, please refer to FIG. 9, which is a structural schematic diagram of an embodiment of a pavement crack maintenance scheme decision device provided by the application. The embodiment of the application provides a pavement crack maintenance scheme decision device 900, which comprises:

[0154] The crack positioning module 901 is configured to position the pavement crack disease according to the collected image data and electrical signal data to obtain pavement crack disease position information.

[0155] The parameter calculation module 902 is configured to calculate the image crack degree and the overall modulus of the structural layer of the pavement area corresponding to the pavement crack disease position information.

[0156] The scheme making module 903 is configured to determine a plurality of pavement crack disease maintenance schemes based on the image crack degree and the overall modulus of the structural layer.

[0157] The evaluation and decision module 904 is configured to establish a pavement service performance evaluation system and evaluate different pavement crack disease maintenance schemes intervened in the second year based on the pavement service performance evaluation system and pavement historical maintenance data to determine a target pavement crack maintenance scheme.

[0158] It should be noted that the device 900 provided in the above embodiment can implement the technical solutions described in the above method embodiments, and the principles of the implementation of the above modules or units can be referred to the corresponding content in the above method embodiments, which will not be described here.

[0159] Please refer to Fig. 10, which is a structural schematic diagram of a pavement crack maintenance scheme decision device provided by an embodiment of the present application. Based on the pavement crack maintenance scheme decision method described above, the present application also provides a pavement crack maintenance scheme decision device accordingly. The pavement crack maintenance scheme decision device can be a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server, or other computing devices. The pavement crack maintenance scheme decision device 1000 includes a processor 1001, a memory 1002, and a display 1003. Fig. 10 only shows part of the components of the pavement crack maintenance scheme decision device, but it should be understood that all the components shown are not required to be implemented, and more or fewer components can be alternatively implemented.

[0160] The memory 1002 can be an internal storage unit of the pavement crack maintenance scheme decision device 1000 in some embodiments, such as a hard disk or a memory of the pavement crack maintenance scheme decision device 1000. The memory 1002 can also be an external storage device of the pavement crack maintenance scheme decision device 1000 in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, or the like equipped on the pavement crack maintenance scheme decision device 1000. Further, the memory 1002 can include both the internal storage unit and the external storage device of the pavement crack maintenance scheme decision device 1000. The memory 1002 is used to store application software and various data installed on the pavement crack maintenance scheme decision device 1000, such as program codes installed on the pavement crack maintenance scheme decision device 1000. The memory 1002 can also be used to temporarily store data that has been output or will be output. In an embodiment, the memory 1002 stores a pavement crack maintenance scheme decision program 1004, which can be executed by the processor 1001 to implement the pavement crack maintenance scheme decision method of the embodiments of the present application.

[0161] The processor 1001 can be a central processing unit (CPU), a microprocessor, or other data processing chip in some embodiments, used to run program codes or process data stored in the memory 1002, such as to execute the pavement crack maintenance scheme decision method.

[0162] The display 1003 can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, etc. in some embodiments. The display 1003 is used to display information of the pavement crack maintenance scheme decision device 1000 and to display a visualized user interface. The components 1001-1003 of the pavement crack maintenance scheme decision device 1000 communicate with each other through a system bus.

[0163] In an embodiment, the steps in the pavement crack maintenance scheme decision method as above are implemented when the processor 1001 executes the pavement crack maintenance scheme decision program 1004 in the memory 1002.

[0164] The embodiment also provides a computer readable storage medium having stored thereon a pavement crack maintenance scheme decision program, which, when executed by a processor, implements the following steps:

[0165] Positioning pavement crack diseases according to the collected image data and electrical signal data to obtain pavement crack disease position information;

[0166] Calculating image crack degree and structure layer overall modulus of a pavement area corresponding to the pavement crack disease position information;

[0167] Determining a plurality of pavement crack disease maintenance schemes based on the image crack degree and the structure layer overall modulus;

[0168] Establishing a pavement service performance evaluation system and evaluating different pavement crack disease maintenance schemes intervened in the second year based on the pavement service performance evaluation system and pavement historical maintenance data to determine a pavement crack target maintenance scheme.

[0169] To sum up, the pavement crack maintenance scheme decision method provided by the embodiment positions pavement crack diseases through image data and electrical signal data, determines pavement crack disease positions, accurately positions pavement crack disease positions, calculates image crack degree and structure layer overall modulus, analyzes pavement crack diseases, formulates different maintenance schemes, establishes a pavement service performance evaluation system, analyzes un-intervened maintenance schemes and maintenance conditions in the second year of intervened different maintenance schemes with only a small amount of pavement historical maintenance data, evaluates different maintenance schemes, determines maintenance effects of different maintenance schemes when there is little annual data of the pavement, and determines a pavement crack target maintenance scheme, so as to achieve the best effect of pavement crack maintenance.

[0170] Those skilled in the art can understand that all or part of the processes of the above-mentioned embodiment methods can be completed by instructing the relevant hardware by a computer program, and the program can be stored in a computer readable storage medium. The computer readable storage medium is a disk, an optical disk, a read-only memory, a random access memory, etc.

[0171] The above description is merely preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method of pavement crack maintenance program decision-making, characterized by, The method comprises the following steps: locating the road crack disease according to the collected image data and electrical signal data to obtain road crack disease position information; calculating the image crack degree and the overall modulus of the structure layer of the road area corresponding to the road crack disease position information; determining a plurality of road crack disease maintenance schemes based on the image crack degree and the overall modulus of the structure layer; establishing a road use performance evaluation system, and evaluating different road crack disease maintenance schemes intervened in the second year based on the road use performance evaluation system and historical road maintenance data to determine a target road crack maintenance scheme.

2. The pavement cracking maintenance program decision method of claim 1, wherein, The calculation of the image crack degree and the overall modulus of the structure layer of the road area corresponding to the road crack disease position information comprises the following steps: obtaining radar detection image data from the road section corresponding to the road crack disease position information; extracting information based on the radar detection image data and calculating the image crack degree; fitting the correlation between the image crack degree and the overall modulus of the structure layer, and calculating the overall modulus of the structure layer based on the correlation.

3. The pavement cracking maintenance program decision method of claim 2, wherein, The information extraction based on the radar detection image data and the calculation of the image crack degree comprise the following steps: extracting the crack image influence depth at the vertical staggered stripe and the pile number at the vertical staggered stripe according to the radar detection image data; calculating the standard deviation of the pile number distribution and the average value of the crack image influence depth according to the crack image influence depth at the vertical staggered stripe and the pile number at the vertical staggered stripe; calculating the image crack degree through the standard deviation of the pile number distribution and the average value of the crack image influence depth.

4. The pavement cracking maintenance program decision method of claim 2, wherein, The fitting of the correlation between the image crack degree and the overall modulus of the structure layer, and the calculation of the overall modulus of the structure layer based on the correlation comprise the following steps: performing a falling weight deflectometer detection on the road section corresponding to the road crack disease position information; fitting the correlation between the image crack degree and the overall modulus of the structure layer based on the deflectometer detection result and the image crack degree; inputting the image crack degree into the correlation to calculate the overall modulus of the structure layer.

5. The pavement cracking maintenance program decision method of claim 1 wherein, The determination of a plurality of road crack disease maintenance schemes based on the image crack degree and the overall modulus of the structure layer comprises the following steps: analyzing the road structure layer bearing capacity according to the image crack degree and the overall modulus of the structure layer; establishing different road crack disease maintenance schemes based on the road structure layer bearing capacity.

6. The pavement cracking maintenance program decision method of claim 1 wherein, The establishment of the road use performance evaluation system and the evaluation of different road crack disease maintenance schemes intervened in the second year based on the road use performance evaluation system and historical road maintenance data to determine a target road crack maintenance scheme comprise the following steps: establishing a road use performance evaluation system with a road use performance improvement index as the target based on a road damage condition index and a road driving quality index; calculating the parameters and weight coefficients corresponding to the road damage condition index and the road driving quality index of different road crack disease maintenance schemes; calculating the road use performance improvement index of different road crack disease maintenance schemes intervened in the second year according to the parameters, the weight coefficients and the historical road maintenance data. The pavement crack target maintenance scheme is determined based on pavement performance improvement indexes of different pavement crack disease maintenance schemes intervened in the second year.

7. The pavement cracking maintenance program decision method of claim 6 wherein, The parameters and weight coefficients corresponding to the pavement damage condition index and the pavement driving quality index of the different pavement crack disease maintenance schemes are calculated, including: The promotion correction value and the decay rate improvement value of the pavement damage condition index and the promotion correction value and the decay rate improvement value of the pavement driving quality index of the different pavement crack disease maintenance schemes are calculated respectively. The weight coefficients corresponding to the promotion correction value and the decay rate improvement value of the pavement damage condition index and the promotion correction value and the decay rate improvement value of the pavement driving quality index are determined by using the principal component analysis method.

8. A road crack maintenance plan decision-making device, characterized in that: Including: The crack positioning module is configured to position the pavement crack disease according to the collected image data and the electrical signal data to obtain pavement crack disease position information. The parameter calculation module is configured to calculate the image crack degree and the overall modulus of the structure layer of the pavement area corresponding to the pavement crack disease position information. The scheme formulation module is configured to determine a plurality of pavement crack disease maintenance schemes based on the image crack degree and the overall modulus of the structure layer. The evaluation and decision module is configured to establish a pavement performance evaluation system and determine a pavement crack target maintenance scheme based on the pavement performance evaluation system and historical pavement maintenance data of the different pavement crack disease maintenance schemes intervened in the second year.

9. A pavement crack maintenance program decision device characterized by, Including a memory and a processor, wherein, The memory is configured to store a program. The processor is coupled with the memory and is configured to execute the program stored in the memory to implement the steps in the pavement crack maintenance scheme decision method of any one of claims 1 to 7. A computer readable program or instruction is stored, and the program or instruction is executed by a processor to implement the steps in the pavement crack maintenance scheme decision method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, ​

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