Method for detecting flatness of road base

Abstract

The invention belongs to the technical field of road detection, and particularly relates to a road base flatness detection method. The method comprises the steps that S1, a detection area is arranged at the turning position of the mountain road every 10-30 meters, and each detection area is divided into an inner arc area, a transition area and an outer arc area from inside to outside; s2, recording the damaged position of the surface layer of each detection area, and primarily judging the flatness damage type of the base layer according to the damage type and arrangement mode of the surface layer of each detection area; s3, forming a detection group by using a plurality of detection areas with continuous surface layer damage on the same curve on the mountain road, and judging whether base layer corresponding areas of surface layer damage positions are of the same damage type or not; s4, the damaged area of the surface layer serves as the center, and detection is conducted on the peripheral outward extension area of the damaged area through corresponding detection equipment; s5, the flatness state of the base layer A is calculated according to the damage type and the damage area of the corresponding equipment detection base layer, and a final detection result is obtained; the flatness state of a base layer of a laid surface layer can be detected.

Description

Technical Field

[0001] The invention belongs to the technical field of road detection, and in particular relates to a method for detecting the flatness of a road base. Background Art

[0002] The flatness detection of the road base is an important part of road construction quality control, which directly affects the construction quality of the upper pavement structure and the service life of the road. The flatness detection items of the base mainly include voids, temperature cracks and elevation errors. As the load-bearing layer of the road structure, the flatness of the base is not only related to driving comfort and safety, but also determines the uniformity of the surface material and the durability of the structure. If the base flatness does not meet the standards such as voids, temperature cracks and elevation errors, it will lead to uneven surface thickness and local stress concentration, which will cause early diseases such as rutting, cracks, and bumps, and increase the cost of later maintenance.

[0003] Especially on the winding mountain highway, due to the existence of both slopes and curvatures on the winding mountain highway during the road base construction, the construction difficulties are more likely to lead to quality problems. Due to the impact of mountain water erosion on the road base, the mountain blocking the sunlight at different positions of the road, and the elevation error of the base construction, it is easy for the winding mountain highway to be eroded by running water at different positions after long-term use, and the temperature difference and uneven thickness will cause the base to be damaged. Specifically, the temperature difference of the winding mountain highway and the effect of running water erosion during rain will accelerate the damage to the base structure, and the damage to the base will be reflected on the surface layer, causing damage to the surface layer. When the above situations occur on the winding mountain highway, if the road surface is damaged, it is easy to cause the vehicle to lose control during driving. The types of damage caused by running water erosion, temperature differences and base flatness problems correspond to voids, temperature difference cracks and elevation errors. When the existing road surface is damaged, it is often repaired directly without finding out the cause of the damage. When the cause of damage is flatness problems such as holes in the base layer, temperature difference cracks and elevation errors, if the different problems of the base layer are not dealt with accordingly, damage will still occur even after repair, resulting in the need for repeated repairs, or the surface layer will be planed up and the base layer will be repaved regardless of the reason after the surface layer is damaged, resulting in a lot of waste.

[0004] Therefore, compared with the detection of road surface flatness during the construction process, when the road is initially damaged during use, it is necessary to conduct preventive detection of the flatness of the base layer, find out the specific problems corresponding to the flatness of the base layer, and perform corresponding repairs according to the flatness status of the base layer to ensure that the problem is solved fundamentally. Based on this, a method for detecting the flatness of the road base layer is proposed. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a method for detecting the flatness of a road base.

[0006] The object of the present invention can be achieved by the following technical solutions:

[0007] A method for detecting the flatness of a road base layer of the present invention includes the following steps:

[0008] S1: Set a detection area every 10 to 30 meters at the turning of the mountain road, and divide each detection area into an inner arc area, a transition area, and an outer arc area from the inside to the outside;

[0009] S2: Record the types of damage to the surface layer of each detection area, and make a preliminary judgment on the types of damage to the flatness of the base layer according to the types and arrangement patterns of the damage to the surface layer of the detection area, where the types of damage to the flatness of the base layer include cavities, temperature difference cracks, and elevation errors;

[0010] S3: Form a detection group with several consecutive detection areas with damaged surface layers on the same bend of the mountain road, and judge whether the corresponding areas of the base layer at the damaged positions of the surface layer are of the same damaged type;

[0011] S4: According to the initially judged types of damage to the base layer, use corresponding detection equipment to detect the areas extending outward from the damaged area of the surface layer to its surroundings;

[0012] S5: Calculate the flatness state of the base layer according to the types and damaged areas of the damage to the base layer detected by the corresponding equipment, and obtain the final detection result.

[0013] Further, in the S1, the length of the detection area is adjusted according to the angle. When the road curvature of the detection area is larger, the length of a single detection area is shorter; when the road curvature of the detection area is smaller, the length of a single detection area is longer.

[0014] Further, in the S2, it includes step S21: Classify the types of damage to the surface layer spanning multiple detection blocks into pumping, cracks, and rutting, where the cracks include longitudinal cracks, mesh cracks, and transverse cracks.

[0015] Further, in the S2, it also includes step S22: Draw a distribution heat map of the types of damage to the surface layer in the inner arc area, the transition area, and the outer arc area.

[0016] Further, in the S4, it includes step S51: Use a ground penetrating radar, an infrared thermal imager, and a laser profiler to detect the base layer below the concentrated area of the distribution heat map of damages such as pumping, cracks, or rutting in the asphalt surface layer respectively.

[0017] Further, in the S4, it also includes step S42: Divide a nine-square grid with the damaged area of the surface layer as the center and label each area in the nine-square grid in turn.

[0018] Furthermore, the nine areas in the nine-square grid are detected and recorded respectively.

[0019] Furthermore, in the S5, step S51 is also included: calculating the matching degree between the damage and the base layer problem:

[0020]

[0021] Where: M: matching degree;

[0022] W Di : The weight of the i-th surface damage type;

[0023] S Di : Correlation score between surface damage type and base layer problems;

[0024] W Ti : The weight of the result of the i-th detection device;

[0025] S Ti : Conformity score between test results and grassroots issues.

[0026] Furthermore, step S6 is also included: when the base layer damage type is determined to be a base layer elevation error according to the surface layer damage type and the equipment detection result, the road surface layer is removed and the road base layer is exposed, the road base layer is detected and elevation error data is obtained.

[0027] The beneficial effects of the present invention are as follows: by dividing the detection area of the winding mountain highway into an inner arc area, a transition area and an outer arc area from the inside to the outside, and then making an initial judgment on the damaged state of the road surface layer, and then re-detecting the damaged area of the road surface layer through corresponding detection equipment, a weighted calculation is performed by combining the result of the initial judgment with the result of the equipment detection, so that a more accurate result can be obtained, which can be used to detect the flatness state of the base layer after the surface layer has been laid, and at the same time can perform preventive detection on the flatness of the initially damaged base layer, and has a wider scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0029] Figure 1 It is a flow chart of the detection steps of the present invention. DETAILED DESCRIPTION

[0030] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0031] like Figure 1As shown in the figure, a method for detecting the flatness of a road base according to the present invention includes the following steps:

[0032] S1: Set a detection area every 10 to 30 meters at the turning of the mountain road, and divide each detection area into an inner arc area, a transition area, and an outer arc area from the inside to the outside;

[0033] S2: Record the positions where the surface layer of each detection area is damaged, and make a preliminary judgment on the type of damage to the flatness of the road base according to the type and arrangement of the damage to the surface layer of the detection area. The types of damage to the flatness of the road base include cavities, temperature difference cracks, and elevation errors;

[0034] S3: Use several consecutive detection areas with damaged surface layers on the same bend of the mountain road to form a detection group, and judge whether the corresponding areas of the road base at the damaged positions of the surface layer are of the same damaged type; The detection areas with continuously damaged surface layers refer to the areas where the surface layer damage is concentrated and extends to multiple detection areas;

[0035] S4: According to the initially judged type of damage to the road base, use corresponding detection equipment to detect the areas extending from the damaged area of the surface layer to its surroundings respectively;

[0036] S5: Calculate the flatness state of the road base according to the type and area of damage detected by the corresponding equipment, and obtain the final detection result.

[0037] In the prior art, for the detection of the flatness of the road base, it is generally carried out after the road base is poured during the construction process, and then the road surface layer is poured after the detection of the flatness of the road base is completed, so as to ensure that the pressure of the surface layer on the road base is balanced when the vehicle is driving, and avoid uneven stress and damage of the surface layer. However, there is still a probability that the surface layer is damaged after both the road base and the surface layer are poured. Since the reasons for the damage to the surface layer may be cavities, temperature difference cracks or elevation errors in the road base, and since it is impossible to detect the flatness state of the road base without damaging the surface layer, in order to reduce the single repair cost, the surface layer is often repaired directly without detection. At this time, it is impossible to judge the type of damage to the road base, and thus it is also impossible to carry out corresponding repairs according to the type of damage, resulting in repeated damage to the surface layer.

[0038] Therefore, compared with the detection of the road surface flatness during the construction process, when the road shows initial damage during use, it is necessary to conduct preventive detection of the flatness of the road base, and then detect and locate the flatness of the road base. At the same time, in order to be able to specifically detect the flatness state of the road base, so as to facilitate corresponding repairs according to the flatness state of the road base after the surface layer is damaged, and ensure that the problem of surface layer damage is fundamentally solved.

[0039] In this embodiment, the reason for dividing the detection area into an inner arc area, a transition area, and an outer arc area from the inside to the outside is:

[0040] The probabilities of various damages to the base course occurring in different areas are different. Since the inner arc area of the mountainous road closer to the mountain side is more severely eroded by the seepage water from the mountain, it is more likely to have cavities caused by water erosion. While the outer arc area far from the mountain side is affected by sunlight for a long time, it is more likely to have temperature difference expansion and contraction cracks in the base course due to the temperature difference between day and night. At this time, if there is a difference in the flatness of the base course caused by construction errors, there is a greater probability of damage to the road surface course.

[0041] Moreover, since the base course paving construction technology is mostly longitudinal construction along the road surface, the elevation error of the base course along the road surface is more reflected in the longitudinal direction of the road surface. Therefore, for the damage to the road surface course caused by the elevation error of the base course, the damage should be distributed along the longitudinal extension direction of the road.

[0042] Therefore, the detection area is divided into an inner arc area, a transition area, and an outer arc area from the inside to the outside.

[0043] By dividing the road into areas, the flatness state of the base course can be initially judged according to the damage state of the surface course before detection, which is convenient for subsequent detection.

[0044] Since when the surface course is composed of asphalt, different flatness states of the road base course will be reflected on the surface course. Therefore, in order to initially judge the damage state of the road surface course before detecting the flatness state of the road base course and use the corresponding equipment to detect the state of the road base course according to the initial judgment result, the detection efficiency can be improved.

[0045] In some roads, due to lack of maintenance for a long time, it may lead to a large damaged area of the road base course or the surface course, resulting in the damaged area spanning multiple detection areas. A large area of damage is very likely caused by the same type of damage to the road base course, resulting in damage to the surface course. Therefore, several detection areas need to be combined into a detection group for detection.

[0046] After initially judging the damage state of the road surface course, it is necessary to expand the detection outward from the damaged position as the center. Since the damage to some road surface courses is small, or the base course under the surrounding area of the damaged part has been damaged but has not yet been reflected on the surface course, it is necessary to expand the detection from the damaged part of the surface course to the undamaged area of the external surface course to play a preventive detection role and prevent omissions in the detection of the base course.

[0047] When detecting roads with relatively high altitudes such as mountain winding roads, severe cold and icing may occur. When detecting under such conditions using equipment, the detection data may be prone to deviation due to icing after the road base is damaged. Therefore, directly judging the damage state of the road base only based on the detection results obtained by the detection equipment may lead to misjudgment. Therefore, it is necessary to combine the existing damage state of the road surface for combined judgment to ensure the reliability of the detection results.

[0048] Therefore, the detection area of the mountain winding road is divided into an inner arc area, a transition area, and an outer arc area from the inside to the outside. Then, a preliminary judgment is made on the damage state of the road surface. Next, the damaged area of the road surface is re-detected using the corresponding detection equipment. By performing weighted calculation based on the results of the preliminary judgment and the results of the equipment detection, a more accurate result can be obtained. This can be used to detect the flatness state of the road base after the surface layer has been laid, and at the same time, preventive detection of the flatness of the preliminarily damaged road base can be achieved. Compared with the prior art where only the flatness of the road base before the surface layer is laid is detected during the construction process, the solution in this embodiment can be used to detect the flatness state of the road base after the surface layer has been laid, and its application range is wider.

[0049] Furthermore, since there are many curves on the mountain winding road and there is a certain slope for each of them, the complexity of the road is relatively high, and the damaged areas of curves at different angles are different. Therefore, the detection difficulty is also different. In order to adjust the range of the detection area according to different situations, in one embodiment, in S1, the length of the detection area is adjusted according to the angle. When the road curvature of the detection area is larger, the length of a single detection area is shorter; when the road curvature of the detection area is smaller, the length of a single detection area is longer.

[0050] At the curve of the mountain winding road, a detection area is set every 10 - 30 meters, and the specific length is dynamically adjusted according to the curve curvature. For example, when the curve radius R ≤ 50 meters (sharp curve), the detection area length is set to 10 meters; when 50 meters < R ≤ 150 meters (medium curve), the detection area length is 20 meters; when R > 150 meters (gentle curve), the detection area is extended to 30 meters. Each detection area is divided into an inner arc area, a transition area, and an outer arc area along the cross-sectional direction, with widths of 1.5 meters, 2.0 meters, and 1.5 meters respectively. After the division is completed, the GPS positioning system is used to record the coordinates of each detection area to ensure that the detection range is accurately covered.

[0051] Furthermore, in the said S2, it includes step S21: classifying the surface layer damage types that span multiple detection blocks into pumping, cracks, and rutting, where the cracks include longitudinal cracks, reticulated cracks, and transverse cracks.

[0052] When the road surface layer is made of asphalt and longitudinal cracks or pumping occur, it is possible that voids have been generated after the base layer has been eroded by running water. When the road surface layer is made of asphalt and mesh cracks or slight rutting occur, it may be due to elevation errors in the base layer. When the road surface layer is made of asphalt and deep rutting or transverse cracks occur, it is possible that there are temperature difference expansion and contraction cracks in the base layer. Since different flatness states of the road base layer may result in various types of damage after being reflected on the road surface layer, direct detection by equipment may also lead to misjudgment. It is necessary to obtain the final detection result based on the damage state, the position on the road surface layer, and by combining the detection data of the equipment to ensure the accuracy of the detection result.

[0053] Furthermore, in S2, it also includes step S22: drawing a distribution heat map of the surface layer damage types in the inner arc area, transition area, and outer arc area. In order to more clearly determine the concentrated area of damage and then find the central position of the damaged base layer according to the concentrated area of damage, an unmanned aerial vehicle (UAV) equipped with a high-resolution camera can be used to conduct an aerial survey of the detection area. The damage types can be identified or manually marked through image processing software, and the position coordinates are marked. A three-dimensional damage distribution heat map is generated using a heat map generation software platform, where red represents a high damage density and blue represents a low density. The heat map can more conveniently and intuitively determine the central area of the damaged base layer.

[0054] Furthermore, in S4, it includes step S51: detecting the base layer below the concentrated area of the distribution heat map of damages such as pumping, cracks, or rutting that occur in the asphalt surface layer by using a ground-penetrating radar (GPR), an infrared thermal imager (IRT), and a laser profiler respectively. The principle of the ground-penetrating radar (GPR) for detecting base layer voids is mainly that its high-frequency electromagnetic waves (500 MHz - 2.5 GHz) penetrate the asphalt surface layer. When encountering an air void or loose area, the dielectric constant changes suddenly, generating a strong reflection signal. After the ground-penetrating radar detection, verification is required. Suspected void areas are marked and judged in combination with the damage state of the road surface layer, and the final detection result is obtained based on the degree of coincidence between the two. The principle of the infrared thermal imaging (IRT) for detecting temperature cracks is mainly that due to the difference in heat conduction caused by base layer cracks, when absorbing heat during the day or dissipating heat at night, a temperature gradient is formed between the crack area and the intact area. When operating, a time period needs to be selected, generally 1 hour after sunrise (heat accumulation period) or 2 hours after sunset (heat dissipation period). After the detection, the data needs to be interpreted. At the crack, due to air insulation or heat storage of moisture, a continuous temperature anomaly (temperature difference ≥ 2 °C) appears. After infrared thermal imaging, further judgment is made in combination with the damage state of the road surface layer to obtain the final detection result. The longitudinal and transverse elevations of the road surface are measured by a laser profiler to generate a flatness map. Then, through data analysis, since roads at high altitudes such as mountain roads are easily affected by factors such as road icing, it is easy to cause errors in the detection results. Therefore, it is also necessary to make a judgment in combination with the damage state of the road surface layer.

[0055] Further, in S4, it further includes step S42: Divide a nine-square grid centered on the damaged area of the surface layer and sequentially number each area in the nine-square grid. Since the damaged extension area of some road surface layers is relatively large, it is necessary to detect outward from the damaged position of the road surface layer. After some base layers are damaged, they have not yet reacted and caused damage to the corresponding position of the surface layer. Therefore, according to the division of the nine-square grid, the surrounding of the damaged position can be detected to further determine the specific scope and trend of the base layer damage.

[0056] Further, sequentially detect the nine areas in the nine-square grid and record them separately.

[0057] In one embodiment, in S5, it further includes step S51: Calculate the matching degree between the damage and the base layer problem:

[0058]

[0059] Where: M: Matching degree (0% - 100%, the larger the value, the higher the matching degree);

[0060] W Di : The weight of the i-th type of surface layer damage (based on the directional strength of the damage to the base layer problem);

[0061] S Di : The correlation score between the surface layer damage type and the base layer problem (0 - 1, can be set to 0, 0.5, 1);

[0062] W Ti : The weight of the result of the i-th detection device (based on the detection accuracy and reliability);

[0063] S Ti : The conformity score between the detection result and the base layer problem (0 - 1, can be set to 0, 0.5, 1).

[0064] Since the probabilities of voids, temperature difference expansion cracks, and base layer elevation errors occurring in the inner arc area, transition area, and outer arc area of the road are different, different weights can be assigned according to the probabilities of voids, temperature difference expansion cracks, and base layer elevation errors occurring in the inner arc area, transition area, and outer arc area respectively. Different weights can also be assigned according to the damage degree of the road surface layer. The value of the weight can be based on the probability ratio of the corresponding base layer damage state in the previous surface layer state.

[0065] For example, longitudinal cracks or slurry pumping, mesh cracks or slight rutting, deep rutting or transverse cracks will be assigned weights on the surface layer of the inner arc area of the road. Since the probability of longitudinal cracks or slurry pumping caused by cavities in the inner arc area is relatively high, a weight of 0.5 is assigned. Since the distance corresponding to the same angle in the inner arc area is smaller than that in the outer arc area, the probability of mesh cracks or slight rutting caused by elevation error is slightly smaller, and a weight of 0.4 is assigned. The temperature of the inner arc area close to the mountain is stable, so the probability of deep rutting or transverse cracks caused by temperature difference expansion cracks is the lowest, so a weight of 0.3 is assigned. At the same time, the ground penetrating radar, infrared thermal imager and laser profiler have different detection accuracy for damage such as slurry pumping, cracks or rutting. Specifically, the sensitivity and diagnostic ability of different equipment to base problems, as well as the data accuracy and repeatability of the equipment in specific scenarios, are different. The weight values are set according to the differences of different equipment. For example, the weight of ground penetrating radar is 0.5, the weight of infrared thermal imager is 0.4, and the weight of laser profiler is 0.3. Then, by setting the matching degree judgment standard, M≥80% is judged as a strong match, 50%≤M<80% is judged as a medium match, and M<50% is judged as a weak match.

[0066] After completing the above settings, assuming that deep rutting or transverse cracks appear on the road surface in the inner arc area, this feature matches the thermal expansion cracks, but because the location does not match, the associated score S is assigned. D The value is 0.5, and the temperature difference data detected by the infrared thermal imager is abnormal, so the correlation score S is assigned. T is 1. Based on the above data, the matching degree of the basic problem is simulated and calculated:

[0067]

[0068] Since the match is moderate, local drilling verification is required. When the match is weak, re-testing or adjustment of model parameters is required. When the match is strong, corresponding repairs are performed immediately.

[0069] Furthermore, when there is an error in the flatness of the base layer obtained by detection and calculation, in order to further maintain the flatness of the road base layer, it is necessary to detect the flatness data of the road base layer more accurately. In one embodiment, step S6 is also included: when the base layer damage type is judged to be a base layer elevation error based on the surface damage type and the equipment detection results, the road surface layer is removed and the road base layer is exposed, the road base layer is detected and the elevation error data is obtained.

[0070] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments of equivalent changes within the scope of the technical solution of the present invention by using the above-disclosed technical content. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for detecting the flatness of a road base course, characterized in that: The following steps are involved: S1: Set up a detection zone every 10 to 30 meters at the bend of the winding mountain road, and divide each detection zone into an inner arc zone, a transition zone and an outer arc zone from the inside to the outside; S2: Record the type of surface damage in each test area, and make a preliminary judgment on the type of base flatness damage based on the type of surface damage in the test area, where the base flatness damage types include voids, temperature difference cracks and elevation errors; S3: forming a detection group with several consecutive detection areas of damaged surface layers in the same curve on the winding mountain road, and judging whether the corresponding areas of the damaged surface layers in the base layer are of the same damage type; S4: Based on the initially determined type of base layer damage, the damaged area of the surface layer is used as the center to detect the surrounding areas through corresponding testing equipment; S5: Calculate the flatness state of the base layer according to the damaged type and damaged area of the base layer detected by the corresponding equipment and obtain the final detection result.

2. The flatness detection method of a road base according to claim 1, characterized in that: In S1, the length of the detection area is adjusted with the angle. When the road curvature of the detection area is larger, the length of a single detection area is shorter. When the road curvature of the detection area is smaller, the length of a single detection area is longer.

3. The flatness detection method for a road base according to claim 1, characterized in that: In said S2, step S21 is included: classifying the surface layer damage types across multiple detection blocks into pumping, cracks and rutting, wherein the cracks include longitudinal cracks, mesh cracks and transverse cracks.

4. A flatness detection method for a road base according to claim 3, characterized in that: In the S2, step S22 is also included: drawing a distribution thermodynamic map of the surface layer damage types in the inner arc zone, the transition zone and the outer arc zone.

5. A flatness detection method for a road base according to claim 4, characterized in that: In said S4, step S41 is included: using ground penetrating radar, infrared thermal imager and laser profiler to detect the base layer below the concentrated area of the distribution thermal map of damage such as pumping, cracks or rutting on the asphalt surface layer.

6. The flatness detection method for a road base according to claim 5, characterized in that: In the above S4, step S42 is also included: dividing the damaged area of the surface layer into nine squares around it, and labeling each area in the nine squares in sequence.

7. A flatness detection method for a road base according to claim 6, characterized in that: The nine areas in the nine-square grid are detected and recorded respectively.

8. A flatness detection method for a road base according to claim 6, characterized in that: In the S5, step S51 is also included: calculating the matching degree between the damage and the base layer problem: Where: M: matching degree; W Di : The weight of the i-th type of surface layer damage; S Di : Correlation score of surface layer damage type and subgrade problems; W Ti : The weight of the result of the i-th detection device; S Ti : Compliance score of the detection result with the grass-roots problems.

9. A flatness detection method for a road base according to claim 1, characterized in that: The method also includes step S6: when the base layer damage type is determined to be a base layer elevation error according to the surface layer damage type and the equipment detection result, the road surface layer is removed to expose the road base layer, the road base layer is detected and elevation error data is obtained.

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