AU2020386611A1 - Pavement flatness measurement method and system - Google Patents
Pavement flatness measurement method and system Download PDFInfo
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- AU2020386611A1 AU2020386611A1 AU2020386611A AU2020386611A AU2020386611A1 AU 2020386611 A1 AU2020386611 A1 AU 2020386611A1 AU 2020386611 A AU2020386611 A AU 2020386611A AU 2020386611 A AU2020386611 A AU 2020386611A AU 2020386611 A1 AU2020386611 A1 AU 2020386611A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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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
- E01C23/00—Auxiliary devices or arrangements for constructing, repairing, reconditioning, or taking-up road or like surfaces
- E01C23/01—Devices or auxiliary means for setting-out or checking the configuration of new surfacing, e.g. templates, screed or reference line supports; Applications of apparatus for measuring, indicating, or recording the surface configuration of existing surfacing, e.g. profilographs
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Abstract
A pavement flatness measurement method and system. The measurement method comprises: acquiring three-dimensional point cloud data of a pavement; performing reverse modeling by using the point cloud data to obtain a pavement entity model; building an eight-wheel flatness measuring instrument model in equal proportion, and constraining the connection relationship of all parts; enabling the eight-wheel flatness measuring instrument model to move on a specified characteristic line of the pavement entity model by using a motion simulation technology, to obtain a pavement irregularity deviation value of each pavement measurement interval; and calculating the flatness value of the pavement according to the pavement irregularity deviation value. The system comprises an acquisition module (10), a pavement entity model building module (20), a flatness measuring instrument model building module (30), a pavement irregularity deviation value calculation module (40), and a flatness calculation module (50). The measurement method and system can be applied to curved pavements and large-slope pavements, and has strong feasibility and practicability.
Description
Technical field
The present invention relates to the technical field of road surface detection, and in particular to a method and system for detecting road surface flatness.
Technical background
A driving speed of a high-speed ring road in test (race) yards is generally as high as 240 Km/h, which requires extremely high road surface flatness. The traditional method for detecting road surface flatness is to use a 3-meter lean ruler or an eight-wheel flatness measuring instrument to detect. However, the curve section of the high-speed ring road of the test (race) yards generally adopts a double-twisted hyperbolic basin-shaped road surface, and the inclination angle of the high-speed lane thereof is as high as 450 or more. The eight-wheel flatness measuring instrument cannot be used due to its own weight sliding or rolling caused by its excessively large inclination angle. At the same time, due to its double-twisted hyperbolic structure, it cannot be detected by the 3-meter lean ruler.
At present, the flatness detection of the high-speed ring road of the test (race) yards generally uses a total station to collect discrete points, a designed elevation is then compared to obtain difference values, and a mean square error is further calculated as the basis for measuring flatness. The method uses the mean square error as the basis for measuring the flatness, while the standard flatness is based on the "mid-vector" change value as the basis for measuring the flatness, so there is no correlation between the two. According to the experiments, the mean square error calculated by using the total station to collect the points on the flat road surface and the detection result of the eight-wheel flatness measuring instrument differ up to tens of times. Moreover, there is no standard basis for calculating the mean square error by collecting discrete points with a total station (local and overseas). It is a difficult problem to detect the flatness of high-speed ring road curved road surfaces and large slope road surfaces. There is no related technology and method published in related literature.
Summary of the invention
To overcome the deficiencies or defects existing in the prior art, an objective of the present invention is to provide a method and system for detecting flatness suitable for basin-shaped curved road surfaces and large slope road surfaces, which can also be used for detecting the flatness of conventional roads.
In order to achieve the above objective, the present invention provides a method for detecting road surface flatness, comprising the following steps:
acquiring road surface three-dimensional point cloud data;
performing reverse modeling by using the point cloud data to obtain a road surface entity model;
building an eight-wheel flatness measuring instrument model in equal proportions, and constraining connection relationships of respective components;
enabling the eight-wheel flatness measuring instrument model to move on a specified characteristic line of the road surface entity model by using a motion simulation technology to obtain a road surface unevenness deviation value of each road surface measurement interval; and
calculating a flatness value of the road surface according to the road surface unevenness deviation value.
Further, after acquiring road surface three-dimensional point cloud data, the method for detecting road surface flatness further comprises: performing filtering and classifying processing on the road surface three-dimensional point cloud data to obtain pure road surface point cloud data; and performing reverse modeling by using the obtained pure road surface point cloud data to obtain the road surface entity model.
Further, the specified characteristic line is a position where flatness detection needs to be performed.
Further, the enabling the eight-wheel flatness measuring instrument model to move on the specified characteristic line of the road surface entity model by using the motion simulation technology to obtain the road surface unevenness deviation value of each road surface measurement interval specifically comprises:
tracking a motion trajectory between a distance measuring wheel and a main frame of the eight-wheel flatness measuring instrument model by using a tracking technology of motion simulation, plotting it into a motion curve, and then obtaining the road surface unevenness deviation value.
The "tracking" technology is a commonly used technical means in motion simulation, which can track a motion trajectory of an object.
Further, the calculating the flatness value of the road surface according to the road surface unevenness deviation value comprises:
calculating a flatness of each road surface measurement interval according to the road surface unevenness deviation value:
Z(i-d02 n-1
wherein u; represents a calculated value of the flatness of each road surface measurement interval, di represents a road surface unevenness deviation value of each road surface measurement interval, d represents an average value of the road surface unevenness deviation values, and n represents the number of pieces of test data for calculating the flatness of the road surface measurement intervals.
Further, the method for detecting road surface flatness further comprises:
according to the flatness of each road surface measurement interval, calculating an average value of flatness, a standard deviation of flatness and a coefficient of variation of road sections in each road surface measurement interval.
On the other hand, the present invention provides a system for detecting road surface flatness, comprising an acquisition module, a module for constructing road surface entity model, a module for constructing flatness measuring instrument model, a module for calculating road surface unevenness deviation value, and a module for calculating flatness,
wherein the acquisition module is used for acquiring road surface three-dimensional point cloud data;
the module for constructing road surface entity model is used for performing reverse modeling by using the point cloud data to obtain a road surface entity model;
the module for constructing flatness measuring instrument model is used for building an eight-wheel flatness measuring instrument model in equal proportions, and constraining connection relationships of respective components; the module for calculating road surface unevenness deviation value is used for enabling the eight-wheel flatness measuring instrument model to move on a specified characteristic line of the road surface entity model by using a motion simulation technology to obtain a road surface unevenness deviation value of each road surface measurement interval; and the module for calculating flatness is used for calculating a flatness value of the road surface according to the road surface unevenness deviation value.
Further, the system for detecting road surface flatness further comprises a processing module connected to the acquisition module, wherein the processing module is used for performing filtering and classification processing on the road surface three-dimensional point cloud data acquired by the acquisition module to obtain pure road surface point cloud data.
Further, the module for calculating road surface unevenness deviation value is specifically used for:
tracking a motion trajectory between a distance measuring wheel and a main frame of the eight-wheel flatness measuring instrument model by using a tracking technology of motion simulation, plotting it into a motion curve, and then obtaining the road surface unevenness deviation value of each road surface measurement interval.
Further, the module for calculating flatness is specifically used for:
calculating a flatness of each road surface measurement interval according to the road surface unevenness deviation value:
2 di J(7- n-1
wherein u; represents a calculated value of the flatness of each road surface measurement interval, di represents a road surface unevenness deviation value of each road surface measurement interval, d represents an average value of the road surface unevenness deviation values, and n represents the number of pieces of test data for calculating the flatness of the road surface measurement intervals.
According to the flatness of each road surface measurement interval, an average value of flatness, a standard deviation of flatness and a coefficient of variation of road sections in each road surface measurement interval are further calculated.
Compared with the prior art, the present invention has the following technical effects: in the present invention, the road surface entity model is constructed by performing reverse modeling using the three-dimensional point cloud data of the road surface to be detected; the eight-wheel flatness measuring instrument model is built in equal proportions, and the connection relationships of respective components are constrained; the eight-wheel flatness measuring instrument model is enabled to move on the road surface entity model; and the motion trajectory between the distance measuring wheel and the main frame of the eight-wheel flatness measuring instrument model is tracked by using the "tracking" technology of motion simulation, it is plot into a motion curve, and then the road surface unevenness deviation value of each road surface measurement interval is obtained, thereby calculating the flatness of the road surface. The road surface flatness detection scheme provided by the present invention has simple principles, strong feasibility and strong practicability, and can meet various road surface flatness detection requirements, especially for effectively solving the difficult problem of detecting the flatness of the double-twisted hyperbolic basin-shaped curved road surfaces and large slope road surfaces. It provides a practical solution for detecting the flatness of high-speed ring road surfaces in the test (race) yards, and can also be used for the flatness detection of conventional road surfaces such as expressways.
Brief description of the drawings
The embodiments of the present invention will be described in detail below in conjunction with the drawings.
Fig. 1 is a schematic flow diagram of a method for detecting road surface flatness;
Fig. 2 shows a high-precision road surface three-dimensional point cloud obtained by the 3D scanner;
Fig. 3 shows a pure road surface point cloud obtained by processing;
Fig. 4 is a road surface entity model obtained by reverse modeling of point cloud data;
Fig. 5 is a schematic diagram of a built eight-wheel flatness measuring instrument model;
Fig. 6 is a schematic diagram of a movement curve of a distance measuring wheel relative to a main frame acknowledged by a computer motion simulation "tracking" technology in real time; and
Fig. 7 is a schematic structural diagram of a system for detecting road surface flatness.
Detailed description of the embodiments
In order to further explain the features of the present invention, please refer to the following detailed description of the present invention and the drawings. The drawings are for reference and explanation purposes only, and are not used to limit the protection scope of the present invention.
As shown in Fig. 1, the present embodiment discloses a method for detecting road surface flatness, comprising the following steps S1 to S5:
S1. Acquiring road surface three-dimensional point cloud data.
It needs to be noted that the manner of acquiring the road surface three-dimensional point cloud data includes, but is not limited to, a 3D scanner, a backpack scanner, or a total station measuring dense points, and other methods. The acquired road surface point cloud data is as shown in Fig. 2.
S2. Performing reverse modeling by using the point cloud data to obtain a road surface entity model.
It needs to be noted that, in the present embodiment, the point cloud is encapsulated and build into a surface through reverse modeling of the point cloud to obtain a road surface model that fits an actual situation. The method of performing reverse modeling of the point cloud data to obtain the road surface entity model includes, but is not limited to, a method of encapsulating and then smoothing, a method of extracting characteristic lines, constructing a surface, and then constructing a body from the surface, and other methods. Those skilled in the art can select an appropriate reverse modeling method according to the actual situation with the purpose of obtaining the road surface entity model that fits the actual situation, as shown in Fig. 4.
S3. Building an eight-wheel flatness measuring instrument model in equal proportions, and constraining connection relationships of respective components.
Specifically, the model is built in equal proportions according to the eight-wheel flatness measuring instrument entity, as shown in Fig. 5. A traction part 1 is mainly composed of connecting plugs and tie rods, and is connected to a front axle 2 through nuts. The front axle 2 is installed on an eight-wheel system 3 composed of eight motorcycle wheels of pneumatic tire type through front and rear frames, and the wheels are connected through wheel frames 9. A displacement sensor 4, namely, a frequency modulation inductive displacement measurement system, is installed on a main frame 6, the main frame 6 is installed on the front axle 2 and a rear axle 8, and the main frame 6 includes a telescopic square tube, a guide structure and a rear frame. A locking mechanism 5 is used for controlling the stop and movement of the eight-wheel car. A measuring wheel 7 is further installed on a body of the main frame 6, and is composed of a pressure spring, a lifting mechanism, a rubber wheel, and a distance sensor. It is constrained so that the distance measuring wheel 7 is vertically slidably connected to the main frame 6. The distance measuring wheel 7 can move freely relative to the frame body in a vertical direction, and the rest are fixedly connected. The motorcycle wheels of the eight-wheel system 3 can roll along a direction of the main frame.
S4. Enabling the eight-wheel flatness measuring instrument model to move on a specified characteristic line of the road surface entity model by using a motion simulation technology to obtain a road surface unevenness deviation value of each road surface measurement interval.
S5. Calculating a flatness value of the road surface according to the road surface unevenness deviation value.
Preferably, after acquiring the road surface three-dimensional point cloud data, the above step S1 further comprises: performing filtering, classifying and other processing on the road surface three-dimensional point cloud data to obtain pure road surface point cloud data, as shown in Fig. 3.
It needs to be noted that the method of processing the point cloud data in the present embodiment includes, but is not limited to, several processing methods such as filtering and classification. The point cloud data can also be segmented, divided into blocks, and so on according to the amount of data. The purpose of the processing is to obtain a pure road surface point cloud.
Specifically, in the above step S4: by using a motion simulation technology, the eight-wheel flatness measuring instrument model is enabled to move on a specified characteristic line of the road surface entity model; by using the "tracking" technology of motion simulation, a motion trajectory between the distance measuring wheel 7 and the main frame 6 of the eight-wheel flatness measuring instrument model is tracked and is plotted into a motion curve; and then the road surface unevenness deviation value is obtained. The specified characteristic line of the road surface entity model refers to a position of the road surface that actually needs to be detected, which are generally an area where the wheels are crushed when driving. In general:
(1) The built eight-wheel flatness model is moved on the specified characteristic line of the constructed road surface model by applying the motion simulation technology. The speed of movement is 5 km/h, and it should not exceed 12 km/h.
(2) The motion trajectory between the distance measuring wheel 7 and the main frame 6 of the eight-wheel flatness measuring instrument model is tracked by using the "tracking" technology of motion simulation, and is plotted into a motion curve.
(3) The flatness standard deviation of a calculation interval of 100 m is automatically calculated according to displacement values collected every 10 cm interval, recording a test length, the number of times the curve amplitude is greater than a certain value (3 mm, 5 mm, 8 mm, 10 mm or the like), and a one-way (convex or concave) cumulative value of the curve amplitude are recorded, and a curve graph of a midpoint road surface deviation based on a 3 m frame is obtained.
Specifically, in the above step S5: a flatness value of the road surface is calculated according to the road surface unevenness deviation value. Specifically:
a flatness of each road surface measurement interval is calculated according to the road surface unevenness deviation value:
$d - di2 n-1
wherein u; represents a calculated value of the flatness of each road surface measurement interval, di represents a road surface unevenness deviation displacement value of each road surface measurement interval, d represents an average value of the road surface unevenness deviation displacement values, and n represents the number of pieces of test data for calculating the flatness of the road surface measurement intervals.
Specifically, the method for detecting road surface flatness further comprises:
according to the flatness of each road surface measurement interval, calculating an average value of flatness, a standard deviation of flatness and a coefficient of variation of road sections in each road surface measurement interval.
As shown in FIG. 7, the present embodiment discloses a system for detecting road surface flatness, comprising an acquisition module 10, a module for constructing road surface entity model 20, a module for constructing flatness measuring instrument model 30, a module for calculating road surface unevenness deviation value 40, and a module for calculating flatness 50.
The acquisition module 10 is used for acquiring road surface three-dimensional point cloud data;
the module for constructing road surface entity model 20 is used for performing reverse modeling by using the point cloud data to obtain a road surface entity model;
the module for constructing flatness measuring instrument model 30 is used for building an eight-wheel flatness measuring instrument model in equal proportions, and constraining connection relationships of respective components;
the module for calculating road surface unevenness deviation value 40 is used for enabling the eight-wheel flatness measuring instrument model to move on a specified characteristic line of the road surface entity model by using a motion simulation technology to obtain a road surface unevenness deviation value of each road surface measurement interval; and
the module for calculating flatness 50 is used for calculating a flatness value of the road surface according to the road surface unevenness deviation value.
Specifically, the system for detecting road surface flatness further comprises a processing module connected to the acquisition module, wherein the processing module is used for performing filtering and classification processing on the road surface three-dimensional point cloud data acquired by the acquisition module to obtain pure road surface point cloud data, and performing reverse modeling by using the pure road surface point cloud data to obtain the road surface entity model.
Specifically, the module for calculating road surface unevenness deviation value 40 is specifically used for: tracking a motion trajectory between the distance measuring wheel 7 and the main frame 6 of the eight-wheel flatness measuring instrument model by using the "tracking" technology of motion simulation, plotting it into a motion curve, and then obtaining the road surface unevenness deviation value of each road surface measurement interval.
Specifically, the module for calculating flatness 50 is specifically used for:
calculating a flatness of each road surface measurement interval according to the road surface unevenness deviation value:
(7- d2 n-1
wherein u; represents a calculated value of the flatness of each road surface measurement interval, di represents a road surface unevenness deviation value of each road surface measurement interval, d represents an average value of the road surface unevenness deviation values, and n represents the number of pieces of test data for calculating the flatness of the road surface measurement intervals.
The preferred embodiments of the present invention are only described as above, but are not intended to limit the present invention, and any modification, equivalent substitution, improvement, etc. within the spirit and principle of the present invention should be included within the protection scope of the present invention.
Claims (10)
1. A method for detecting road surface flatness, comprising:
acquiring road surface three-dimensional point cloud data;
performing reverse modeling by using the point cloud data to obtain a road surface entity model;
building an eight-wheel flatness measuring instrument model in equal proportions, and constraining connection relationships of respective components;
enabling the eight-wheel flatness measuring instrument model to move on a specified characteristic line of the road surface entity model by using a motion simulation technology to obtain a road surface unevenness deviation value of each road surface measurement interval; and
calculating a flatness value of the road surface according to the road surface unevenness deviation value.
2. The method for detecting road surface flatness according to claim 1, wherein after acquiring road surface three-dimensional point cloud data, further comprising:
performing filtering and classifying processing on the road surface three-dimensional point cloud data to obtain pure road surface point cloud data; and performing reverse modeling by using the obtained pure road surface point cloud data to obtain the road surface entity model.
3. The method for detecting road surface flatness according to claim 1, wherein the specified characteristic line is a position where flatness detection needs to be performed.
4. The method for detecting road surface flatness according to claim 1, wherein the enabling the eight-wheel flatness measuring instrument model to move on the specified characteristic line of the road surface entity model by using the motion simulation technology to obtain the road surface unevenness deviation value of each road surface measurement interval specifically comprises:
tracking a motion trajectory between a distance measuring wheel and a main frame of the eight-wheel flatness measuring instrument model by using a tracking technology of motion simulation, plotting it into a motion curve, and then obtaining the road surface unevenness deviation value.
5. The method for detecting road surface flatness according to claim 1, wherein the calculating the flatness value of the road surface according to the road surface unevenness deviation value comprises:
calculating a flatness of each road surface measurement interval according to the road surface unevenness deviation value:
( - d2 n-1
wherein u; represents a calculated value of the flatness of each road surface measurement interval, di represents a road surface unevenness deviation displacement value of each road surface measurement interval, d represents an average value of the road surface unevenness deviation displacement values, and n represents the number of pieces of test data for calculating the flatness of the road surface measurement intervals.
6. The method for detecting road surface flatness according to claim 5, further comprising:
according to the flatness of each road surface measurement interval, calculating an average value of flatness, a standard deviation of flatness and a coefficient of variation of road sections in the road surface measurement intervals.
7. A system for detecting road surface flatness, comprising an acquisition module, a module for constructing road surface entity model, a module for constructing flatness measuring instrument model, a module for calculating road surface unevenness deviation value, and a module for calculating flatness,
wherein the acquisition module is used for acquiring road surface three-dimensional point cloud data;
the module for constructing road surface entity model is used for performing reverse modeling by using the point cloud data to obtain a road surface entity model;
the module for constructing flatness measuring instrument model is used for building an eight-wheel flatness measuring instrument model in equal proportions, and constraining connection relationships of respective components;
the module for calculating road surface unevenness deviation value is used for enabling the eight-wheel flatness measuring instrument model to move on a specified characteristic line of the road surface entity model by using a motion simulation technology to obtain a road surface unevenness deviation value of each road surface measurement interval; and
the module for calculating flatness is used for calculating a flatness value of the road surface according to the road surface unevenness deviation value.
8. The system for detecting road surface flatness according to claim 7, further comprising a processing module connected to the acquisition module, wherein the processing module is used for performing filtering and classification processing on the road surface three-dimensional point cloud data acquired by the acquisition module to obtain pure road surface point cloud data.
9. The system for detecting road surface flatness according to claim 7, wherein the module for calculating road surface unevenness deviation value is specifically used for:
tracking a motion trajectory between a distance measuring wheel and a main frame of the eight-wheel flatness measuring instrument model by using a tracking technology of motion simulation, plotting it into a motion curve, and then obtaining the road surface unevenness deviation value of each road surface measurement interval.
10. The system for detecting road surface flatness according to claim 7, wherein the module for calculating flatness is specifically used for:
calculating a flatness of each road surface measurement interval according to the road surface unevenness deviation value:
2 di J(7- n-1
wherein u; represents a calculated value of the flatness of each road surface measurement interval, di represents a road surface unevenness deviation value of each road surface measurement interval, d represents an average value of the road surface unevenness deviation values, and n represents the number of pieces of test data for calculating the flatness of the road surface measurement intervals; and according to the flatness of each road surface measurement interval, further calculating an average value of flatness, a standard deviation of flatness and a coefficient of variation of road sections in each road surface measurement interval.
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| CN201911143562.0 | 2019-11-20 | ||
| CN201911143562.0A CN111021206B (en) | 2019-11-20 | 2019-11-20 | Road surface flatness detection method and system |
| PCT/CN2020/089615 WO2021098161A1 (en) | 2019-11-20 | 2020-05-11 | Pavement flatness measurement method and system |
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| CN111021206B (en) * | 2019-11-20 | 2021-06-25 | 中铁四局集团第一工程有限公司 | Road surface flatness detection method and system |
| CN112665535A (en) * | 2020-12-04 | 2021-04-16 | 中冶天工集团有限公司 | Method for measuring wall surface flatness |
| CN113689565B (en) * | 2021-10-21 | 2022-03-18 | 北京中科慧眼科技有限公司 | Road flatness grade detection method and system based on binocular stereo vision and intelligent terminal |
| CN113957774B (en) * | 2021-10-27 | 2023-04-07 | 安徽理工大学 | Road surface roughness detection device based on singlechip information feedback formula |
| CN114908649B (en) * | 2022-06-16 | 2024-01-26 | 西安长安大学工程设计研究院有限公司 | Roadbed and pavement flatness measuring device |
| CN115538253A (en) * | 2022-09-01 | 2022-12-30 | 中国路桥工程有限责任公司 | Intelligent paving system based on laser guide |
| CN116289443B (en) * | 2023-02-01 | 2023-11-10 | 兰陵县昌通公路工程有限公司 | Road and bridge construction flatness measuring device |
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| CN110084116B (en) * | 2019-03-22 | 2022-02-01 | 深圳市速腾聚创科技有限公司 | Road surface detection method, road surface detection device, computer equipment and storage medium |
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| DA3 | Amendments made section 104 |
Free format text: THE NATURE OF THE AMENDMENT IS: APPLICATION IS TO PROCEED UNDER THE NUMBER 2020104486 |