CN112962379B - Asphalt paving management system based on big data - Google Patents
Asphalt paving management system based on big data Download PDFInfo
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- CN112962379B CN112962379B CN202110186078.7A CN202110186078A CN112962379B CN 112962379 B CN112962379 B CN 112962379B CN 202110186078 A CN202110186078 A CN 202110186078A CN 112962379 B CN112962379 B CN 112962379B
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- 239000010426 asphalt Substances 0.000 title claims abstract description 178
- 238000009792 diffusion process Methods 0.000 claims abstract description 64
- 230000004927 fusion Effects 0.000 claims abstract description 53
- 230000002950 deficient Effects 0.000 claims abstract description 45
- 238000001514 detection method Methods 0.000 claims abstract description 35
- 239000012492 regenerant Substances 0.000 claims description 48
- 230000007547 defect Effects 0.000 claims description 40
- 238000005070 sampling Methods 0.000 claims description 39
- 201000010099 disease Diseases 0.000 claims description 29
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 29
- 238000007726 management method Methods 0.000 claims description 17
- 238000007689 inspection Methods 0.000 claims description 13
- 238000012216 screening Methods 0.000 claims description 10
- QGJOPFRUJISHPQ-UHFFFAOYSA-N Carbon disulfide Chemical compound S=C=S QGJOPFRUJISHPQ-UHFFFAOYSA-N 0.000 description 3
- 238000000034 method Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 239000007788 liquid Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000013074 reference sample Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 150000002430 hydrocarbons Chemical class 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 239000002344 surface layer Substances 0.000 description 1
Classifications
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- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
- E01C7/00—Coherent pavings made in situ
- E01C7/08—Coherent pavings made in situ made of road-metal and binders
- E01C7/18—Coherent pavings made in situ made of road-metal and binders of road-metal and bituminous binders
- E01C7/187—Repairing bituminous covers, e.g. regeneration of the covering material in situ, application of a new bituminous topping
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
- Y02A30/60—Planning or developing urban green infrastructure
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- Engineering & Computer Science (AREA)
- Architecture (AREA)
- Civil Engineering (AREA)
- Structural Engineering (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses an asphalt paving management system based on big data, which comprises a road condition dividing module, a position condition dividing module, a diffusion condition dividing module and a fusion condition dividing module, wherein the road condition dividing module collects road conditions of old asphalt pavement and divides road sections into suspected defective road sections and suspected intact road sections according to the road conditions, the position condition dividing module further divides the suspected intact road sections into intact road sections and road sections to be detected according to the collected position conditions of the suspected intact road sections, the diffusion condition dividing module divides the pre-detection passing road sections according to diffusion conditions of regenerants in the suspected defective road sections and the road sections to be detected, and the fusion condition dividing module selects old asphalt reusable detection passing road sections of road sections according to fusion conditions of old asphalt and new asphalt of the pre-detection passing road sections.
Description
Technical Field
The invention relates to the field of big data, in particular to an asphalt paving management system based on big data.
Background
Asphalt is a blackish brown complex mixture composed of hydrocarbon compounds with different molecular weights and nonmetallic derivatives thereof, is one of high-viscosity organic liquids, is in a liquid state, has black surface, and is soluble in carbon disulfide. Asphalt is a pavement structure cementing material widely applied in road engineering, asphalt pavement with different structures can be built after the asphalt pavement is matched with mineral materials with different compositions in proportion, the application of the asphalt pavement on highways is wide, and it is estimated that about 12% of asphalt surface layers in China need to be repaired every year from now on, and the conventional maintenance method is to mill and dig old asphalt pavement and then pave new asphalt pavement, but the old asphalt mixture is wasted, and ecological environment pollution is possibly caused.
Disclosure of Invention
The invention aims to provide an asphalt paving management system and method based on big data, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an asphalt paving management system based on big data comprises a road condition dividing module, a position condition dividing module, a diffusion condition dividing module and a fusion condition dividing module, wherein the road condition dividing module collects road conditions of old asphalt pavement and divides road sections into suspected defective road sections and suspected intact road sections according to the road conditions, the position condition dividing module further divides the suspected intact road sections into intact road sections and road sections to be detected according to the collected position conditions of the suspected intact road sections, the diffusion condition dividing module divides pre-detection passing road sections according to diffusion conditions of regenerants in the suspected defective road sections and the road sections to be detected, and the fusion condition dividing module selects old asphalt reusable detection passing road sections of road sections according to fusion conditions of old asphalt and new asphalt of the pre-detection passing road sections
More optimally, the road condition dividing module comprises a road condition collecting module, a disease degree extracting module and a disease degree comparing module, wherein the road condition collecting module is used for dividing an old asphalt road surface into a plurality of small road sections and collecting road condition images of the road surfaces of all the small road sections, the disease degree extracting module is used for extracting the disease degree of all the small road sections from the road condition images collected by the road condition collecting module, and the disease degree comparing module compares the disease degree of all the small road sections with a disease degree threshold value and divides the small road sections into suspected defective road sections and suspected intact road sections according to the comparison.
More optimally, the position condition dividing module comprises a position acquisition module and a defect road section judging module, wherein the defect road section judging module judges whether adjacent two side road sections around the suspected perfect road section are suspected defect road sections or not according to the position condition around the suspected perfect road section acquired by the position acquisition module so as to divide the perfect road section and the road section to be detected; the diffusion condition dividing module comprises a first sampling road section selecting module, a diffusion degree detecting module and a diffusion degree comparing module, wherein the first sampling road section selecting module is used for selecting a plurality of intact road sections as a first sampling road section, old asphalt of the first sampling road section is used as a reference sample of the diffusion degree and the fusion degree of the old asphalt, the diffusion degree detecting module is used for detecting the diffusion degree of a regenerant in the old asphalt of the first sampling road section, a road section to be detected and a suspected defect road section, and the diffusion degree comparing module is used for comparing the diffusion degree of the regenerant in the old asphalt of the road section to be detected and the suspected defect road section with the diffusion degree of the regenerant in the old asphalt of the first sampling road section and selecting a road section which is not passed through in advance according to the diffusion degree.
More preferably, the diffusion condition dividing module further comprises a non-inspection defect road section screening module, wherein the non-inspection defect road section screening module is used for screening suspected defect road sections adjacent to the pre-inspection failed road section from the suspected defect road sections; the fusion condition dividing module comprises a preselected road section selecting module, a fusion degree detecting module and a fusion degree comparing module, wherein the preselected road section selecting module selects a preselected road section from the preselected road sections according to the diffusion degree of a regenerant in old asphalt of each preselected road section, the fusion degree detecting module is used for detecting the fusion degree of the old asphalt of a first sampling road section and new asphalt and the fusion degree of the old asphalt of the preselected road section and the new asphalt, and the fusion degree comparing module is used for comparing the fusion degree of the old asphalt of the first sampling road section and the new asphalt and the fusion degree of the old asphalt of the preselected road section and the new asphalt, and selecting a reusable detection passing road section of the old asphalt of the road section from the preselected road section according to the comparison.
An asphalt paving management method based on big data, the management method comprising:
Step S1: collecting road conditions of an old asphalt pavement, and dividing the old asphalt pavement into a suspected defective road section and a suspected intact road section according to the road conditions;
step S2: collecting the position condition of a suspected perfect road section, and dividing the suspected perfect road section into a perfect road section and a road section to be detected according to the position condition;
Step S3: selecting a regenerant according to the property of old asphalt of a complete road section, comparing the diffusion degree of the regenerant in the complete road section, the road section to be detected and the suspected defect road section, and dividing a pre-detection passing road section from the suspected defect road section and the road section to be detected according to the diffusion degree;
Step S4: and selecting a road section with recycled asphalt according to the fusion degree of the old asphalt and the new asphalt of each road section.
More preferably, the step S1 further includes:
Dividing an old asphalt pavement into a plurality of small road sections, collecting road condition images of the road surfaces of all the small road sections, extracting the disease degree of each small road section from the road condition images, wherein if the disease degree of a certain small road section is greater than or equal to a disease degree threshold value, the small road section is a suspected defective road section, and if the disease degree of a certain small road section is less than the disease degree threshold value, the small road section is a suspected perfect road section.
More preferably, the step S2 further includes: and respectively detecting whether the adjacent two-side road sections of each suspected intact road section are suspected defect road sections, if not, judging that the suspected intact road section is an intact road section, otherwise, judging that the suspected road section is a road section to be detected.
More preferably, the step S3 further includes:
Step S31: randomly selecting a plurality of intact road sections as first sampling road sections, collecting and mixing old asphalt of all the first sampling road sections, selecting a proper regenerant according to the interest of the old asphalt of the first sampling road sections, mixing the regenerant into the mixed old asphalt of the first sampling road sections, and collecting the diffusion degree Kw of the regenerant in the old asphalt of the first sampling road sections;
Step S32: respectively collecting old asphalt of each road section to be detected, mixing the regenerant selected in the step S31 into the old asphalt of the road section to be detected, detecting the diffusion degree Kj of the regenerant in the old asphalt of each road section to be detected,
If (Kw-Kj) is less than or equal to the diffusion fluctuation threshold value, the road section to be detected is a pre-detection passing road section,
If (Kw-Kj) is larger than the diffusion fluctuation threshold, the road section to be detected is a pre-detected failed road section, and the suspected defect road section adjacent to the pre-detected failed road section is a non-detected defect road section;
Step S33: screening out non-inspection defective road sections in the suspected defective road sections, respectively collecting old asphalt around defects in each suspected defective road section and old asphalt around non-defects in the suspected defective road sections, mixing the old asphalt belonging to the same suspected defective road section, mixing the regenerant selected in the step S31 into the old asphalt of the suspected defective road section, detecting the diffusion degree Kq of the regenerant in the old asphalt of each suspected defective road section, and if (Kw-Kq) is smaller than or equal to a fluctuation threshold value, the suspected defective road section is a pre-inspection passing road section, and if (Kw-Kq) is larger than the fluctuation threshold value, the suspected defective road section is a pre-inspection non-passing road section.
More preferably, the step S4 further includes:
Step S41: adding new asphalt to the collected and mixed old asphalt of all the first sampling road sections after adding the regenerant selected in the step S31 for fusion, and collecting the fusion degree Rc of the old asphalt and the new asphalt of the first sampling road sections;
step S42: selecting a preselected road section from the pre-detection passing road sections according to the degree of diffusion of the regenerant in the old asphalt of each pre-detection passing road section;
Step S43: adding the regenerant selected in the step S31 into the old asphalt respectively collected each pre-selected road section, adding the new asphalt for fusion, and respectively collecting the fusion degree Ry of the old asphalt and the new asphalt of the pre-selected road section, wherein if (Rc-Ry) is smaller than or equal to a fusion fluctuation threshold value, the pre-selected road section is a detection passing road section, wherein the old asphalt of the intact road section and the detection passing road section is used as recycled asphalt.
More preferably, the step S42 further includes: and sorting the pre-detected passing road sections according to the diffusion degree of the regenerant in the old asphalt of each pre-detected passing road section from large to small, and selecting the pre-detected passing road section of which the rank is half as the pre-selected road section.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the diffusion degree of the regenerant in the old asphalt of the first sampling road section, the road section to be detected and the suspected defect road section is compared, the fusion degree of the old asphalt of the first sampling road section and the new asphalt is compared with the fusion degree of the old asphalt of the preselected road section and the new asphalt, and the old asphalt of the proper road section is selected from the old asphalt road surface to be used as recycled asphalt, so that the utilization rate of the old asphalt is improved, the environmental pollution is reduced, the quality of the old asphalt used as recycled asphalt is ensured, and the service life of the road paved by using the new and old mixed asphalt is prolonged.
Drawings
FIG. 1 is a schematic block diagram of a big data based asphalt paving management system of the present invention;
FIG. 2 is a flow chart of a big data based asphalt paving management method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, in an embodiment of the present invention, an asphalt paving management system and a method based on big data, where the management system includes a road condition classification module, a position condition classification module, a diffusion condition classification module and a fusion condition classification module, where the road condition classification module collects road conditions of old asphalt pavement and classifies road segments into a suspected defective road segment and a suspected perfect road segment according to the road conditions, the position condition classification module further classifies the suspected perfect road segment into a perfect road segment and a road segment to be detected according to the collected position conditions of the suspected perfect road segment, the diffusion condition classification module classifies a pre-detection passing road segment according to diffusion conditions of regenerant in the suspected defective road segment and the road segment to be detected, and the fusion condition classification module selects an old asphalt reusable detection passing road segment of a road segment according to a fusion condition of old asphalt and new asphalt of the pre-detection passing road segment
The road condition dividing module comprises a road condition collecting module, a disease degree extracting module and a disease degree comparing module, wherein the road condition collecting module is used for dividing an old asphalt road surface into a plurality of small road sections and collecting road condition images of the road surfaces of all the small road sections, the disease degree extracting module is used for extracting the disease degree of all the small road sections from the road condition images collected by the road condition collecting module, and the disease degree comparing module is used for comparing the disease degree of all the small road sections with a disease degree threshold value and dividing the small road sections into suspected defective road sections and suspected intact road sections according to the comparison.
The position condition dividing module comprises a position acquisition module and a defect road section judging module, wherein the defect road section judging module judges whether adjacent two side road sections around the suspected perfect road section are suspected defect road sections or not according to the position condition around the suspected perfect road section acquired by the position acquisition module so as to divide the perfect road section and the road section to be detected; the diffusion condition dividing module comprises a first sampling road section selecting module, a diffusion degree detecting module and a diffusion degree comparing module, wherein the first sampling road section selecting module is used for selecting a plurality of intact road sections as a first sampling road section, old asphalt of the first sampling road section is used as a reference sample of the diffusion degree and the fusion degree of the old asphalt, the diffusion degree detecting module is used for detecting the diffusion degree of a regenerant in the old asphalt of the first sampling road section, a road section to be detected and a suspected defect road section, and the diffusion degree comparing module is used for comparing the diffusion degree of the regenerant in the old asphalt of the road section to be detected and the suspected defect road section with the diffusion degree of the regenerant in the old asphalt of the first sampling road section and selecting a road section which is not passed through in advance according to the diffusion degree.
The diffusion condition dividing module further comprises a non-detection defect road section screening module, wherein the non-detection defect road section screening module is used for screening suspected defect road sections adjacent to the pre-detection non-passing road sections from the suspected defect road sections; the fusion condition dividing module comprises a preselected road section selecting module, a fusion degree detecting module and a fusion degree comparing module, wherein the preselected road section selecting module selects a preselected road section from the preselected road sections according to the diffusion degree of a regenerant in old asphalt of each preselected road section, the fusion degree detecting module is used for detecting the fusion degree of the old asphalt of a first sampling road section and new asphalt and the fusion degree of the old asphalt of the preselected road section and the new asphalt, and the fusion degree comparing module is used for comparing the fusion degree of the old asphalt of the first sampling road section and the new asphalt and the fusion degree of the old asphalt of the preselected road section and the new asphalt, and selecting a reusable detection passing road section of the old asphalt of the road section from the preselected road section according to the comparison.
An asphalt paving management method based on big data, the management method comprising:
Step S1: dividing an old asphalt pavement into a plurality of small road sections, collecting road condition images of the road surfaces of all the small road sections, extracting the disease degree of each small road section from the road condition images, wherein if the disease degree of a certain small road section is greater than or equal to a disease degree threshold value, the small road section is a suspected defective road section, and if the disease degree of a certain small road section is less than the disease degree threshold value, the small road section is a suspected perfect road section;
Step S2: acquiring the position condition of a suspected perfect road section, respectively detecting whether two adjacent road sections of each suspected perfect road section are suspected defect road sections, if not, judging that the suspected perfect road section is a perfect road section, otherwise, judging that the suspected road section is a road section to be detected;
Step S3: selecting a regenerant according to the property of old asphalt of a complete road section, comparing the diffusion degree of the regenerant in the complete road section, the road section to be detected and the suspected defect road section, and dividing a pre-detection passing road section from the suspected defect road section and the road section to be detected according to the diffusion degree:
Step S31: randomly selecting a plurality of intact road sections as first sampling road sections, collecting and mixing old asphalt of all the first sampling road sections, selecting a proper regenerant according to the interest of the old asphalt of the first sampling road sections, mixing the regenerant into the mixed old asphalt of the first sampling road sections, and collecting the diffusion degree Kw of the regenerant in the old asphalt of the first sampling road sections;
Step S32: respectively collecting old asphalt of each road section to be detected, mixing the regenerant selected in the step S31 into the old asphalt of the road section to be detected, detecting the diffusion degree Kj of the regenerant in the old asphalt of each road section to be detected,
If (Kw-Kj) is less than or equal to the diffusion fluctuation threshold value, the road section to be detected is a pre-detection passing road section,
If (Kw-Kj) is greater than the diffusion fluctuation threshold, the road section to be detected is a pre-detected failed road section, and the suspected defect road section adjacent to the pre-detected failed road section is a non-detected defect road section,
Step S33: screening out non-inspection defective road sections in the suspected defective road sections, respectively collecting old asphalt around defects in each suspected defective road section and old asphalt around non-defects in the suspected defective road sections, mixing the old asphalt belonging to the same suspected defective road section, mixing the regenerant selected in the step S31 into the old asphalt of the suspected defective road section, detecting the diffusion degree Kq of the regenerant in the old asphalt of each suspected defective road section, and if (Kw-Kq) is smaller than or equal to a fluctuation threshold value, the suspected defective road section is a pre-inspection passing road section, and if (Kw-Kq) is larger than the fluctuation threshold value, the suspected defective road section is a pre-inspection non-passing road section; screening out non-inspection defective road sections in the suspected defective road sections can reduce the detection quantity of the suspected defective road sections, so that the detection efficiency is further improved;
Step S4: selecting a road section with recycled asphalt according to the fusion degree of old asphalt and new asphalt of each road section:
Step S41: adding new asphalt to the collected and mixed old asphalt of all the first sampling road sections after adding the regenerant selected in the step S31 for fusion, and collecting the fusion degree Rc of the old asphalt and the new asphalt of the first sampling road sections;
step S42: and sorting the pre-detected passing road sections according to the diffusion degree of the regenerant in the old asphalt of each pre-detected passing road section from large to small, and selecting the pre-detected passing road section of which the rank is half as the pre-selected road section.
Step S43: adding the regenerant selected in the step S31 into the old asphalt respectively collected each pre-selected road section, adding the new asphalt for fusion, and respectively collecting the fusion degree Ry of the old asphalt and the new asphalt of the pre-selected road section, wherein if (Rc-Ry) is smaller than or equal to a fusion fluctuation threshold value, the pre-selected road section is a detection passing road section, wherein the old asphalt of the intact road section and the detection passing road section is used as recycled asphalt. Old asphalt with higher diffusion degree and higher fusion degree is selected to be recycled and mixed with new asphalt, so that the quality of the mixed asphalt can be improved, and the use effect of the mixed asphalt is ensured.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (3)
1. An asphalt paving management system based on big data is characterized in that: the management system comprises a road condition dividing module, a position condition dividing module, a diffusion condition dividing module and a fusion condition dividing module, wherein the road condition dividing module collects road conditions of old asphalt pavement and divides road sections into suspected defective road sections and suspected perfect road sections according to the road conditions, the position condition dividing module further divides the suspected perfect road sections into perfect road sections and road sections to be detected according to the collected position conditions of the suspected perfect road sections, the diffusion condition dividing module divides the pre-detection passing road sections according to diffusion conditions of regenerants in the suspected defective road sections and the road sections to be detected, and the fusion condition dividing module selects old asphalt reusable detection passing road sections of the road sections according to fusion conditions of old asphalt and new asphalt of the pre-detection passing road sections;
The asphalt paving management method based on big data comprises the following steps:
Step S1: collecting road conditions of an old asphalt pavement, and dividing the old asphalt pavement into a suspected defective road section and a suspected intact road section according to the road conditions;
step S2: collecting the position condition of a suspected perfect road section, and dividing the suspected perfect road section into a perfect road section and a road section to be detected according to the position condition;
Step S3: selecting a regenerant according to the property of old asphalt of a complete road section, comparing the diffusion degree of the regenerant in the complete road section, the road section to be detected and the suspected defect road section, and dividing a pre-detection passing road section from the suspected defect road section and the road section to be detected according to the diffusion degree;
Step S4: selecting a road section with recycled asphalt according to the fusion degree of old asphalt and new asphalt of each road section;
The step S1 further includes:
Dividing an old asphalt pavement into a plurality of small road sections, collecting road condition images of the road surfaces of all the small road sections, extracting the disease degree of each small road section from the road condition images, wherein if the disease degree of a certain small road section is greater than or equal to a disease degree threshold value, the small road section is a suspected defective road section, and if the disease degree of a certain small road section is less than the disease degree threshold value, the small road section is a suspected perfect road section;
The step S2 further includes: detecting whether adjacent two-side road sections of each suspected perfect road section are suspected defect road sections or not respectively, if not, the suspected perfect road section is a perfect road section, otherwise, the suspected perfect road section is a road section to be detected;
The step S3 further includes:
Step S31: randomly selecting a plurality of intact road sections as first sampling road sections, collecting and mixing old asphalt of all the first sampling road sections, selecting a proper regenerant according to the old asphalt of the first sampling road sections, mixing the regenerant into the mixed old asphalt of the first sampling road sections, and collecting the diffusion Kw of the regenerant in the old asphalt of the first sampling road sections;
Step S32: respectively collecting old asphalt of each road section to be detected, mixing the regenerant selected in the step S31 into the old asphalt of the road section to be detected, detecting the diffusion degree Kj of the regenerant in the old asphalt of each road section to be detected,
If (Kw-Kj) is less than or equal to the diffusion fluctuation threshold value, the road section to be detected is a pre-detection passing road section,
If (Kw-Kj) is larger than the diffusion fluctuation threshold, the road section to be detected is a pre-detected failed road section, and the suspected defect road section adjacent to the pre-detected failed road section is a non-detected defect road section;
Step S33: screening out non-inspection defective road sections in the suspected defective road sections, respectively collecting old asphalt around defects in each suspected defective road section and old asphalt around non-defects in the suspected defective road sections, mixing the old asphalt belonging to the same suspected defective road section, mixing the regenerant selected in the step S31 into the old asphalt of the suspected defective road section, detecting the diffusion degree Kq of the regenerant in the old asphalt of each suspected defective road section, and if (Kw-Kq) is smaller than or equal to a fluctuation threshold value, the suspected defective road section is a pre-inspection passing road section, and if (Kw-Kq) is larger than the fluctuation threshold value, the suspected defective road section is a pre-inspection non-passing road section.
2. A big data based asphalt paving management system of claim 1, wherein: the step S4 further includes:
Step S41: adding new asphalt to the collected and mixed old asphalt of all the first sampling road sections after adding the regenerant selected in the step S31 for fusion, and collecting the fusion degree Rc of the old asphalt and the new asphalt of the first sampling road sections;
step S42: selecting a preselected road section from the pre-detection passing road sections according to the degree of diffusion of the regenerant in the old asphalt of each pre-detection passing road section;
Step S43: adding the regenerant selected in the step S31 into the old asphalt respectively collected each pre-selected road section, adding the new asphalt for fusion, and respectively collecting the fusion degree Ry of the old asphalt and the new asphalt of the pre-selected road section, wherein if (Rc-Ry) is smaller than or equal to a fusion fluctuation threshold value, the pre-selected road section is a detection passing road section, wherein the old asphalt of the intact road section and the detection passing road section is used as recycled asphalt.
3. A big data based asphalt paving management system according to claim 2, wherein: the step S42 further includes: and sorting the pre-detected passing road sections according to the diffusion degree of the regenerant in the old asphalt of each pre-detected passing road section from large to small, and selecting the pre-detected passing road section of which the rank is half as the pre-selected road section.
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|---|---|---|---|---|
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