An intelligent maintenance decision method and system for asphalt pavement cracks

Abstract

The application discloses an asphalt pavement crack intelligent maintenance decision method and system, and relates to the technical field of road engineering and digital maintenance management, and comprises the following steps: receiving asphalt pavement crack disease characteristic data, wherein the asphalt pavement crack disease characteristic data comprises crack morphology, crack direction (collected when the crack morphology is strip-shaped), crack position, crack plane size and crack depth distribution; judging and classifying crack causes according to the asphalt pavement crack disease characteristic data to obtain crack cause classification results; classifying the crack cause classification results and the crack morphology, obtaining classified crack types, digitally encoding the classified crack types, and obtaining classification results and encoding results; dividing crack states according to the crack plane size to obtain crack state grades; and matching the classification results with the crack state grades, and determining crack repair measures and repair materials according to the matching results.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of road engineering and digital maintenance management, in particular to an intelligent maintenance decision method and system for asphalt pavement cracks. BACKGROUND

[0002] Cracks are a common disease in the service process of asphalt pavement. The current "Highway Technical Condition Evaluation Standard" (JTG5210-2018) (hereinafter referred to as "the Standard") divides the crack disease of asphalt pavement into four types: alligator cracking, block cracking, longitudinal cracking and transverse cracking. Different types of cracks have different causes and timing, and different maintenance and repair methods are used. If cracks are not repaired in time or the repair measures are not appropriate, they are likely to further expand and cause pumping or secondary cracking, affecting the safety and stability of road infrastructure and reducing the quality of services it can provide. Currently, the development of repair schemes for crack diseases often relies on human experience and is highly subjective. With the increasing digitalization of road operation, maintenance and repair, digital management platforms are widely used in asphalt pavement maintenance and management practices. As an important part of asphalt pavement maintenance, the development of disease repair schemes should be a key function module of the digital management platform. For digital management platforms, building an intelligent decision-making framework for crack repair will reduce repair costs while ensuring repair quality, reduce uncertainty and low reliability caused by manual decision-making, and effectively improve repair efficiency.

[0003] Under this background, some scholars and technical personnel have studied the construction and application of asphalt pavement digital management platforms. In terms of intelligent maintenance decision-making, related research has focused on theories based on the whole life cycle and Pareto optimality, taking into account economic expenditure, traffic flow, environmental protection and other factors, and combining pavement performance evaluation indicators to make macro-level maintenance decisions. This method can guide the overall maintenance planning of the owner, but lacks an intelligent decision-making system for specific disease repair methods. In daily maintenance and repair, the repair of asphalt pavement cracks is in a "repair as you go" state, that is, cracks are repaired as soon as they are found during routine inspection. This method can quickly handle crack diseases and ensure the quality of road use, but since the repair method is mainly manual, it cannot guarantee the adaptability of the repair measures to the disease, leading to secondary diseases after repair; or a high-quality repair is performed at the early stage of crack development, resulting in increased repair costs. SUMMARY

[0004] To solve the problems mentioned in the background art, the purpose of the present application is to provide an asphalt pavement crack intelligent maintenance decision method and system, which solves the problem that there is no mature digitalized and automated framework for formulating asphalt pavement disease repair schemes. The present application effectively connects crack feature extraction, crack cause and state analysis, and crack repair measures through an intelligent decision framework for repair schemes, which is beneficial to improving the digitalization level of maintenance and repair decision-making.

[0005] The purpose of the present application can be achieved by the following technical solution: an asphalt pavement crack intelligent maintenance decision method, the method comprising the following steps:

[0006] Receiving asphalt pavement crack disease feature data, wherein the asphalt pavement crack disease feature data includes crack morphology, crack direction, crack location, crack plane size, and crack depth distribution;

[0007] Judging and classifying the crack causes according to the asphalt pavement crack disease feature data to obtain crack cause classification results;

[0008] Classifying the crack cause classification results and the crack morphology by type to obtain classified crack types, and digitally encoding the classified crack types to obtain classification results and encoding results;

[0009] Dividing the crack state according to the crack plane size to obtain crack state levels;

[0010] Matching the classification results with the crack state levels, and determining crack repair measures and repair materials according to the matching results.

[0011] Preferably, the asphalt pavement crack disease feature data is obtained by image recognition technology and laser scanning technology, and the crack direction is collected when the crack morphology is strip-shaped.

[0012] Preferably, the process of judging and classifying the crack causes according to the asphalt pavement crack disease feature data to obtain crack cause classification results comprises:

[0013] Classifying the crack disease according to the crack morphology and crack direction;

[0014] On the basis of the morphology classification, analyzing the crack causes according to the crack location, crack plane size, and crack depth distribution, and classifying for crack disease repair to obtain crack cause classification results;

[0015] Preferably, the crack disease is classified into transverse cracks, longitudinal cracks, and network cracks according to the crack morphology and crack direction.

[0016] Preferably, the crack morphology includes strip and network; the crack orientation is the included angle between the crack and the driving direction of the route when the crack morphology is strip; the crack position includes the wheel track band, the wheel track band edge, the construction division, the staggered table and other positions; the crack planar size includes the crack width and the crack length when the crack is strip, and the crack block size and the average crack width when the crack is network; the crack depth distribution includes the crack depth and the crack width distribution along the depth.

[0017] Preferably, the process of classifying the crack cause classification results and the crack morphology into categories to obtain the classified crack types includes:

[0018] According to the crack cause classification results and the crack morphology, different types of cracks are classified into strip load type cracks, strip temperature type cracks, strip structure type cracks, network temperature type cracks and network load type cracks.

[0019] Preferably, the process of digitizing the classified crack types includes:

[0020] The strip crack morphology is coded as 10, the network crack morphology is coded as 01, the load type crack cause is coded as 100, the temperature type crack cause is coded as 010, and the structure type crack cause is coded as 001.

[0021] Preferably, according to the digitized code, the coding result is obtained, such as:

[0022] The strip load type crack is 10100, the strip temperature type crack is 10010, the strip structure type crack is 10001, the network temperature type crack is 01010, and the network load type crack is 01100.

[0023] Preferably, the crack repair measures include crack sealing and filling, slot crack pouring, joint tape repair, pressure grouting repair technology, fill-dig repair and micro-surfacing, and the repair materials include ordinary asphalt, modified emulsified asphalt, high molecular polymer and epoxy resin mortar.

[0024] In a second aspect, in order to achieve the above object, the present application discloses an asphalt pavement crack intelligent maintenance decision system, which comprises:

[0025] A data receiving module is configured to receive asphalt pavement crack disease characteristic data, wherein the asphalt pavement crack disease characteristic data includes crack morphology, crack orientation, crack position, crack planar size and crack depth distribution.

[0026] A judgment and classification module is configured to judge and classify the crack cause according to the asphalt pavement crack disease characteristic data to obtain crack cause classification results.

[0027] The classification coding module is used for classifying the fracture cause classification result and the fracture morphology, obtaining a classified fracture type, digitizing the classified fracture type, and obtaining a classification result and a coding result.

[0028] The state division module is used for dividing the fracture state according to the fracture plane size, and obtaining a fracture state grade.

[0029] The matching maintenance module is used for matching the classification result with the fracture state grade, and determining the fracture maintenance measure and the maintenance material according to the matching result.

[0030] In another aspect of the present application, in order to achieve the above-mentioned purpose, a device is disclosed, comprising:

[0031] One or more processors;

[0032] A memory for storing one or more programs;

[0033] When one or more of the programs are executed by one or more of the processors, the one or more processors implement the asphalt pavement fracture intelligent maintenance decision method as described above.

[0034] The present application has the following beneficial effects:

[0035] The present application matches the fracture appearance feature, the fracture cause, the fracture development state and the fracture repair measure, and proposes a method for intelligently deciding the asphalt pavement repair scheme.

[0036] The present application effectively solves the problem that the current repair scheme is highly subjective and has poor pertinence, and according to the fracture appearance feature, the fracture cause and the fracture development state, a corresponding repair scheme is formulated, which will ensure the repair effect and reduce the repair cost.

[0037] The present application sets the fracture type digitization coding, which is convenient for interfacing with the digital management platform.

[0038] The present application has simple logical relationship, and is easy to program in various software, and can be well integrated into various digital platforms of highway operation management. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows, and obviously, other drawings can also be obtained by those skilled in the art without creative labor on the premise of not paying creative labor;

[0040] Figure 1 is a method flowchart of the present application;

[0041] Figure 2is a schematic diagram of the workflow of the present application;

[0042] Figure 3 is a schematic diagram of the crack features of the automatic collection of asphalt pavement cracks of the present application;

[0043] Figure 4 is a schematic diagram of the system structure of the present application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0045] As Figure 1 shown, an asphalt pavement crack intelligent maintenance decision-making method, the method comprising the following steps:

[0046] receiving asphalt pavement crack disease feature data, wherein the asphalt pavement crack disease feature data includes crack morphology, crack direction, crack location, crack plane size and crack depth distribution;

[0047] It needs to be further explained that in the specific implementation process, the asphalt pavement crack disease feature data is shown in Table 1:

[0048] Table 1 Crack feature and characterization index

[0049]

[0050] It needs to be further explained that in the specific implementation process, the crack morphology, crack location, crack plane size, crack depth distribution and other features are collected and recorded by using pavement disease detection equipment, as shown in Table 2:

[0051] Table 2 Crack feature detection and collection data statistics table

[0052]

[0053] The crack morphology classification standard is shown in Table 3:

[0054] Table 3 Crack morphology classification standard

[0055]

[0056] According to the asphalt pavement crack disease feature data, the crack causes are judged and classified to obtain crack cause classification results;

[0057] In the embodiment, the process of judging and classifying the crack causes according to the asphalt pavement crack disease characteristic data includes the following steps:

[0058] The crack diseases are classified according to the crack morphology and crack direction: the cracks are divided into transverse cracks, longitudinal cracks and network cracks, and the classification standard is shown in Table 4.

[0059] Table 4 Crack morphology classification standard

[0060]

[0061] The classification results of the cracks collected in the embodiment are shown in Table 5.

[0062] Table 5 Crack disease morphology classification results

[0063]

[0064] On the basis of the morphology classification, the crack causes are analyzed according to the crack position, crack plane size and crack depth distribution, and classified for disease repair: the cause analysis and classification are shown in Table 6.

[0065] Table 6 Crack disease cause analysis and classification

[0066]

[0067] The crack cause analysis results and classification results are shown in Table 7.

[0068] Table 7 Crack disease cause classification results

[0069] Acquisition of cracks Genesis analysis Classification name Crack 1 Temperature crack Type 1 Crack 2 Load fatigue crack Type 3 Crack 3 Load fatigue failure Type 9

[0070] The crack cause classification results and the crack morphology are classified, the classified crack types are obtained, the classified crack types are digitally coded, and the classification results and coding results are obtained;

[0071] It needs to be further explained that in the specific implementation process, the digital coding rules are shown in Table 8, and the final classification results and coding results are shown in Table 9:

[0072] Table 8 Crack type digital coding rules

[0073]

[0074] Table 9 Crack type classification results and digital coding results

[0075]

[0076] The collected crack classification results and coding results are shown in Table 10.

[0077] Classification results of the cracks collected in Table 10 and coding results

[0078]

[0079] According to the crack plane size, the crack state is divided to obtain the crack state grade; in this embodiment, the division basis is shown in Table 11 and Table 12:

[0080] Table 11: Crack state grade division standard

[0081]

[0082] Table 12: Crack state grade division standard

[0083]

[0084] The parameters a1, a2, a3, a4, a5 and b2, b3, b4, b5 can be selected according to actual engineering needs, and the methods that can be selected include: determining the parameter value according to the specification, selecting the parameter value based on the reliability theory according to the actual situation of crack distribution in the project, etc. In this embodiment, a1=3, a2=0.2, a3=2, a4=0.5, a5=1, b2=0.5, b3=5, b4=1, and b5=2 are preferred.

[0085] The state grade of the collected cracks in this embodiment is shown in Table 13.

[0086] Table 13: Classification results of crack state indicators

[0087]

[0088] The classification results are matched with the crack state grade, and the crack repair measures and repair materials are determined according to the matching results. In this embodiment, the crack repair measures include crack sealing and filling, slotting and crack pouring, taping band repair, pressure grouting repair technology, fill-dig repair, and micro-surfacing, and the repair materials include ordinary asphalt, modified emulsified asphalt, high molecular polymer, and epoxy resin mortar.

[0089] The matching results are shown in Table 14 as follows:

[0090] Table 14: Automatic matching of crack repair measures

[0091]

[0092] It needs to be further explained that in the specific implementation process, the repair scheme is shown in Table 15.

[0093] Table 15: Crack repair methods

[0094]

[0095] In another aspect, as shown in the figure, the embodiment of the present application also provides an intelligent asphalt pavement crack maintenance decision system, comprising: Figure 4

[0096] The data receiving module is configured to receive asphalt pavement crack disease characteristic data, wherein the asphalt pavement crack disease characteristic data comprises crack morphology, crack position, crack planar size and crack depth distribution.

[0097] The judgment and classification module is configured to judge and classify crack causes according to the asphalt pavement crack disease characteristic data, to obtain crack cause classification results.

[0098] The classification and coding module is configured to classify the crack cause classification results and the crack morphology by category, to obtain classified crack types, to digitize the classified crack types, and to obtain classification results and coding results.

[0099] The state division module is configured to divide crack states according to crack planar size, to obtain crack state grades.

[0100] The matching maintenance module is configured to match the classification results with the crack state grades, to determine crack maintenance measures and maintenance materials according to the matching results.

[0101] Based on the same inventive concept, the present application also provides a computer device, which comprises one or more processors and a memory for storing one or more computer programs; the program comprises program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is configured to implement one or more instructions, and is specifically configured to load and execute one or more instructions in the computer storage medium to implement the above method.

[0102] ​​​​​It should be further noted that based on the same inventive concept, the present application further provides a computer storage medium, which stores a computer program, and the computer program is run by a processor to execute the above method. The storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus.

[0103] In the description of the present application, the description of the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0104] The above shows and describes the basic principles, main features and advantages of the present disclosure. It should be understood by those skilled in the art that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, various changes and improvements can be made to the present disclosure, and all these changes and improvements fall within the scope of the present disclosure.

Claims

1. An asphalt pavement crack intelligent maintenance decision method, characterized in that, The method comprises the following steps: Receiving asphalt pavement crack disease characteristic data, wherein the asphalt pavement crack disease characteristic data comprises crack morphology, crack direction, crack position, crack planar size and crack depth distribution; Judging and classifying crack causes according to the asphalt pavement crack disease characteristic data to obtain crack cause classification results; Classifying the crack cause classification results and the crack morphology to obtain classified crack types, and digitally encoding the classified crack types to obtain classification results and encoding results; The crack morphology comprises strip-shaped and net-shaped; the crack direction is an included angle between the crack and the driving direction of the route when the crack morphology is strip-shaped; the crack position comprises a wheel track band, an outer edge of the wheel track band, a construction division, and a dislocation; the crack planar size comprises crack width and crack length when the crack is strip-shaped, and comprises crack block size and average crack width when the crack is net-shaped; the crack depth distribution comprises crack depth and crack width distribution along the depth; The process of classifying the crack cause classification results and the crack morphology to obtain classified crack types comprises: Classifying different types of cracks into strip-shaped load-type cracks, strip-shaped temperature-type cracks, strip-shaped structure-type cracks, net-shaped temperature-type cracks and net-shaped load-type cracks according to the crack cause classification results and the crack morphology; Dividing crack states according to the crack planar size to obtain crack state grades; Matching the classification results with the crack state grades, and determining crack repair measures and repair materials according to the matching results.

2. The intelligent asphalt pavement crack maintenance decision method of claim 1, wherein, The asphalt pavement crack disease characteristic data is obtained through image recognition technology and laser scanning technology, and the crack direction is collected when the crack morphology is strip-shaped.

3. The intelligent asphalt pavement crack maintenance decision method of claim 1, wherein, The process of judging and classifying crack causes according to the asphalt pavement crack disease characteristic data to obtain crack cause classification results comprises: Classifying crack diseases according to the crack morphology and the crack direction; Classifying crack causes and facing crack disease repair according to the crack position, the crack planar size and the crack depth distribution on the basis of the morphology classification to obtain crack cause classification results; The crack diseases are classified into transverse cracks, longitudinal cracks and net-shaped cracks according to the crack morphology and the crack direction.

4. The intelligent asphalt pavement crack maintenance decision method of claim 1, wherein, The process of digitally encoding the classified crack types comprises: Encoding strip-shaped crack morphology as 10, encoding net-shaped crack morphology as 01, encoding load-type crack causes as 100, encoding temperature-type crack causes as 010, and encoding structure-type crack causes as 001.

5. The intelligent asphalt pavement crack maintenance decision method of claim 4, wherein, According to the digital encoding, the encoding results are obtained, such as: Strip-shaped load-type cracks are 10100, strip-shaped temperature-type cracks are 10010, strip-shaped structure-type cracks are 10001, net-shaped temperature-type cracks are 01010, and net-shaped load-type cracks are 01100.

6. The intelligent asphalt pavement crack maintenance decision method of claim 1, wherein, The crack repair measures comprise crack sealing and filling, slotting and crack sealing, taping repair, pressure grouting repair technology, fill-dig repair and micro-surfacing, and the repair materials comprise ordinary asphalt, modified emulsified asphalt, high polymer and epoxy resin mortar.

7. An asphalt pavement crack intelligent maintenance decision system, which adopts the asphalt pavement crack intelligent maintenance decision method of any one of claims 1 to 6, characterized in that, Comprise: The data receiving module is configured to receive asphalt pavement crack disease characteristic data, wherein the asphalt pavement crack disease characteristic data comprises crack morphology, crack direction, crack position, crack plane size, and crack depth distribution. The judgment and classification module is configured to judge and classify crack causes according to the asphalt pavement crack disease characteristic data, and obtain crack cause classification results. The classification and coding module is configured to classify the crack cause classification results and the crack morphology, obtain classified crack types, digitize the classified crack types, and obtain classification results and coding results. The state division module is configured to divide crack states according to crack plane size, and obtain crack state grades. The matching and repairing module is configured to match the classification results with the crack state grades, determine crack repairing measures and repairing materials according to matching results.

8. An apparatus, comprising: The method comprises: one or more processors; a memory for storing one or more programs; when one or more programs are executed by one or more processors, so that one or more processors implement an asphalt pavement crack intelligent maintenance decision method according to any one of claims 1-6.

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