CN116336939A - Automatic detection-based pavement rut index instant analysis method - Google Patents
Automatic detection-based pavement rut index instant analysis method Download PDFInfo
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- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
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- G—PHYSICS
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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Abstract
The invention belongs to the field of automatic pavement detection and internet data transmission, in particular to an instant pavement rut index analysis method based on automatic detection, which comprises the following steps: 1) The laser road detection vehicle detects a road surface to acquire road surface index data; 2) Judging whether ruts exist on the road surface according to the acquired road surface index data; if the track exists on the section of road surface, determining a corresponding road mileage stake mark of the road surface with the track, and storing road surface index data containing the mileage stake mark to a vehicle-mounted server; 3) The vehicle-mounted server sends the road surface index data to the application server through the communication module; 4) The application server analyzes the road surface analysis data, and sends the road surface analysis data to the application server through the database. The invention is based on an automatic detection technology, utilizes a 5G communication module, solves the problem of poor timeliness of detection data processing, and combines automatic detection, data processing calculation and the Internet tightly.
Description
Technical Field
The invention belongs to the field of automatic pavement detection and internet data transmission, and particularly relates to an instant pavement rut index analysis method based on automatic detection.
Background
Along with the development of society, road traffic also changes over the sky and over the earth, and the construction of highways has direct influence on the development of various areas, so that the connection among various areas is enhanced, the development speed of regional economy is improved, and the communication between the interior and the outside is promoted to be tighter. The expressway is concerned by people because of large traffic flow and high speed, and the construction quality of the expressway is also good, in particular to the technical development in the field of highway traffic, and under the promotion of high and new technology, people seek advanced pavement detection technology more actively. The road surface automatic detection has remarkable effect in the aspect of participating in highway construction by utilizing science and technology.
The automatic detection mainly comprises pavement damage, road flatness, pavement deflection, pavement skid resistance, pavement ruts, front images and the like, and along with the rapid development of highway construction, the common highway such as the main line of most national provinces in China enters a major repair stage, and the pavement automatic detection technology is used for detecting the construction quality and the operation service life performance of the pavement, so that highway basic information can be accurately provided. The method helps the designer to propose the optimal prevention and treatment measures and repair schemes, and makes a specific design scheme for specific road projects. Automated detection is strongly pushing the scientization and modernization of engineering design.
The existing automatic detection technology has greatly advanced, but the detected data needs to be analyzed again after detection, so that the labor and time are consumed, and the timeliness is poor. And in the rut detection process, the traditional rut detection method comprises the following steps: the ruler measuring method, the surface altimeter measuring method, the laser section meter and the ultrasonic distance meter are more in interference factors, so that the measuring precision is lower, the laser section meter is replaced by the laser distance meter arranged in the traditional method, the consumption of the laser distance meter is increased, and huge cost is caused by detection on a large-scale ultralong road surface; meanwhile, the traditional detection method mostly uses a manual detection method, the method is to span a cross rod for detection on a detected road surface, then measure the distance from the cross rod to the bottom of a rut by using a ruler, the distance is the depth of the rut, the measurement method is greatly influenced by human factors, the measurement is inaccurate, the working efficiency is lower, and the workload of an operator is higher, so that the novel automatic detection method for detecting the rut index of the road surface is designed, and the method for analyzing the rut in real time is particularly important.
Disclosure of Invention
The invention aims to provide an automatic and instant analysis method for the road surface index based on automatic detection, which is capable of automatically analyzing the automatic detection data in real time, reducing the cost and improving the efficiency and the performance stability, so as to overcome the defects of the traditional road surface track index detection method.
The technical scheme adopted by the invention for achieving the purpose is as follows: an instant analysis method for pavement rut indexes based on automatic detection comprises the following steps:
1) The laser road detection vehicle detects a road surface to acquire road surface index data;
2) Judging whether ruts exist on the road surface through a template convolution method according to the acquired road surface index data; if the track exists on the section of road surface, determining a corresponding road mileage stake mark of the road surface with the track, and storing road surface index data containing the mileage stake mark to a vehicle-mounted server, otherwise, directly storing the road surface index data to the vehicle-mounted server;
3) The vehicle-mounted server sends the road surface index data to the application server through the communication module;
4) The application server performs data processing analysis on the road surface index data to obtain road surface analysis data, saves the road surface analysis data to the database, and sends the road surface analysis data to the application server, and the application server sends the road surface analysis data to each terminal through the Internet platform for real-time display.
The step 2) comprises the following steps:
(1) Defining a detection template;
(2) Roaming the vertical center line of the detection template along the advancing direction of the detection template to enable the vertical center line of the template to coincide with the center line of the road detection cross section;
(3) Extracting the relative elevation H of a measured pavement and a reference surface adopted by the detection of the laser road detection vehicle from pavement index data; setting a threshold D 0 For all detected relative elevations H i Making the following determination;
when H is i ＞D 0 When (1) is equal to 0 for all relative elevations, H is equal to i ＞D 0 Processing; n is a F (Xi) function;
when H is i ＞D 0 In the case of taking F (X) i )＝H j J is H i ≤D 0 And the closest point to point i, namely: h i ＞D 0 When the relative elevation of the point j is taken as the relative elevation H of the point j ；
The coefficient z of the template is corresponding to the relative elevation H of the template i Performing product operation, adding all obtained product values, and accumulating the relative elevation H of the template s The method comprises the following steps:
wherein, the coefficient z on the template is 1;
(4) According to accumulated relative elevation H of templates s Obtaining relative elevationAverage value H p The method comprises the following steps:
H p ＝H s ÷n
wherein n is the corresponding relative elevation H i Is the number of (3);
(5) Average value H of the relative elevation of the template and the relative elevation obtained in the step (4) p Difference is made to obtain the difference delta H between the relative elevation and the mean value of each point i The method comprises the following steps:
ΔH i ＝H i -H p
(6) Checking the difference delta H calculated in step (5) i Setting DeltaH i Determining whether there is ΔH exceeding the fluctuation section i If there are one or more delta H i Extracting ΔH beyond the fluctuation interval i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, judging that the track does not exist on the section of road surface;
(7) From extracting DeltaH exceeding the fluctuation interval i And determining the corresponding road mileage stake marks, wherein ruts exist in the range of the mileage stake marks.
The definition template specifically comprises the following steps: defining the length and width of the template: the detection width of the laser detection vehicle is the width of one lane; starting to set any position, detecting 1 cross section to be 5-20 cm;
when the width of the template in the scanning data of the laser road detection vehicle is defined, the length is the length of 5 detection cross sections in the advancing process by taking the scanning width when the data are acquired.
The step 4) comprises the following steps:
a) Carrying out format cleaning on the pavement index data received by the application server according to a template format library, and cleaning repeated data in the pavement index data;
b) Analyzing the road surface index data after the format cleaning, acquiring the section mileage according to the mileage stake mark, and acquiring an influence value in the section mileage according to the section mileage;
c) Comparing the influence values in the section mileage according to the road condition database, and evaluating according to the road condition database to obtain the road overall condition evaluation;
d) And the road overall condition evaluation is sent to a database for storage, and the application server waits for calling.
In the step b), the section mileage is obtained according to the mileage stake mark, which is specifically as follows:
according to two points delta H close to each other i And calculating the distance between the two points according to the coordinate difference of the two points to obtain the instantaneous speed of the detection vehicle, and accumulating according to the instantaneous speed of the detection vehicle to obtain the section mileage.
In the step b), the influence value in the section mileage is obtained according to the section mileage, specifically:
judging the section mileage types, which are respectively: a circle curve type and a relaxation curve type;
obtaining an influence value delta l or delta l in the section mileage by using straight line segments to replace the circular curve type and the buffer curve type s 。
The circular curve is of the type: the arc-shaped curves are arranged for connecting two adjacent straight-line sections when the trend of the road plane changes direction or the slope changes vertically;
the type of the relaxation curve is as follows: a transition curve with a radius of curvature gradually changing from infinity to a radius of a circular curve; the moderation curve type is arranged between a straight line and a circular curve type or a circular curve with the same turning direction as the radius of the circular curve differs by more than a threshold value.
The straight line segment replaces a circular curve type, namely:
Δl＝Rα－T
wherein Deltal is an influence value generated by replacing a circular curve with a straight line, R is the radius of the circular curve, T is the chord length, and alpha is the curve corner, namely the central angle of the circular curve.
The substitution of the relaxation curve type by straight line segments, namely:
wherein Deltal s For the influence value produced by a straight line instead of a circular curve, l s To alleviate the curve length l 0 For the theoretical relaxation curve length, R is the radius of the circular curve connected with the relaxation curve, p is the inner displacement, q is the tangential growth, beta 0 The center angle corresponding to the theoretical relaxation curve length is the relaxation curve angle.
The invention has the following beneficial effects and advantages:
1. the invention opens up an automatic detection tool and a data processing server, and immediately detects the road surface to obtain a detection result.
2. The invention is based on an automatic detection technology, utilizes a 5G communication module, solves the problem of poor timeliness of detection data processing, and combines automatic detection, data processing calculation and the Internet tightly.
3. After the detection data is calculated and archived in real time, the road surface details can be checked at any time and any place through the Internet platform, and a long-time waiting data processing process is not needed.
4. Compared with the traditional detection method, the pavement rut detection method greatly reduces the influence of human factors, solves the problems of inaccurate measurement and lower working efficiency, and reduces the workload of operators.
5. Compared with the traditional automatic detection method, the pavement rut detection method reduces the calculation amount of automatic detection, and basic data is transmitted to an application server for analysis and evaluation.
6. The template convolution method is used in track detection, the section mileage of a certain track can be roughly analyzed by judging the fluctuation interval, and compared with the existing automatic detection method, the template convolution method has higher efficiency, does not need to analyze each section of path, and reduces the analysis amount of automatic detection.
Drawings
FIG. 1 is a frame diagram of an instant analysis method for automatically detecting pavement rut indexes;
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
As shown in fig. 1, the frame diagram of the automatic detecting method for analyzing the pavement rut index in real time is characterized in that the instant data is characterized in that the detection results are obtained, the images are obtained after the detection, after a detector drives a detection vehicle to walk, even people out of thousands can see the automatic processed pavement detection results in real time, so that corresponding schemes can be formulated in time:
1) The principle of the laser road detection vehicle for detecting the ruts on the road surface is that the relative elevation H of the road surface and a reference surface is measured to judge whether the ruts exist on the road surface; the purpose of the laser scanning data processing is to find out the deformation points generated on the road surface so as to realize the rut detection on the road surface.
The method comprises the steps that an inspector drives a detection vehicle to automatically detect, the automatic detection is carried out on the basic detection number, and firstly, the laser road detection vehicle on a mobile server of the existing vehicle detects a road surface to obtain road surface index data;
2) Judging whether ruts exist on the road surface through a template convolution method according to the acquired road surface index data; if the track exists on the section of road surface, determining a corresponding road mileage stake mark of the road surface with the track, and storing road surface index data containing the mileage stake mark to a vehicle-mounted server, otherwise, directly storing the road surface index data to the vehicle-mounted server;
template convolution is convolution with a template, the basic idea being to assign a value to a pixel as a function of its own gray value and the gray values of its neighboring pixels. Therefore, firstly, a proper width and length are defined for the template, the detection width of the laser road detection vehicle is 3.75m (the width of one lane), and the cross section detection interval is 5-20 cm (any setting). Therefore, when the width of the template in the scanning data of the laser road detection vehicle is defined, the length is the length of 5 detection sections in the advancing process by taking the scanning width when the data is acquired. After the template is defined, the main steps of template convolution implementation are as follows:
2-1) roaming the vertical center line of the detection template along the advancing direction of the detection vehicle, so that the vertical center line of the template coincides with the center line of the road detection cross section;
2-2) extracting the relative elevation H of the measured pavement and the reference surface adopted by the detection of the laser road detection vehicle from the pavement index data; setting a threshold D 0 For all detected relative elevations H i Making the following determination;
when H is i ＞D 0 When (1) is equal to 0 for all relative elevations, H is equal to i ＞D 0 Processing; n is a F (Xi) function;
when H is i ＞D 0 In the case of taking F (X) i )＝H j J is H i ≤D 0 And the closest point to point i, namely: h i ＞D 0 When the relative elevation of the point j is taken as the relative elevation H of the point j ；
The coefficient z of the template is corresponding to the relative elevation H of the template i Performing product operation, adding all obtained product values, and accumulating the relative elevation H of the template s The method comprises the following steps:
wherein, the coefficient z on the template is 1;
2-3) accumulated relative elevation H according to templates s Obtaining the average value H of the relative elevation p The method comprises the following steps:
H p ＝H s ÷n
wherein n is the corresponding relative elevation H i Is the number of (3);
2-4) averaging the relative elevation of the template with the relative elevation obtained in step (4) p Difference is made to obtain the difference delta H between the relative elevation and the mean value of each point i The method comprises the following steps:
ΔH i ＝H i -H p
2-5) checking the difference ΔH calculated in step 2-4) i Setting DeltaH i Determining whether there is ΔH exceeding the fluctuation section i If there are one or more delta H i Extracting ΔH beyond the fluctuation interval i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, judging that the track does not exist on the section of road surface;
2-6) extracting ΔH beyond the fluctuation interval i And determining the corresponding road mileage stake marks, wherein ruts exist in the range of the mileage stake marks.
3) The vehicle-mounted server sends the road surface index data to the application server through the communication module; in the embodiment, the communication module adopts a 5G communication module;
4) The application server processes and analyzes the road surface index data;
4-1) carrying out format cleaning on the pavement index data received by the application server according to a template format library, in particular to cleaning of repeated data (the data format is set in the data format library in advance according to different automatic detection equipment);
4-2) after format cleaning when the detected length reaches the calculated satisfied road segment units (e.g.: every hundred meters, kilometer are a calculation unit), carry out rut, roughness, jump car, structure degree of depth equal dividing index and analyze, because roughness, jump car, structure degree of depth etc. all adopt current inspection method to detect, through further analysis to improved rut detection method in this embodiment, include:
acquiring the section mileage according to the mileage stake marks and obtaining the section mileage according to two similar points delta H i And calculating the distance between the two points according to the coordinate difference of the two points to obtain the instantaneous speed of the detection vehicle, and accumulating according to the instantaneous speed of the detection vehicle to obtain the section mileage.
Obtaining an influence value in the section mileage according to the section mileage;
judging the section mileage types, which are respectively: a circle curve type and a relaxation curve type;
obtaining an influence value delta l or delta l in the section mileage by using straight line segments to replace the circular curve type and the buffer curve type s ；
The types of the circular curves are as follows: the arc-shaped curves are arranged for connecting two adjacent straight-line sections when the trend of the road plane changes direction or the slope changes vertically;
the relaxation curve types are: a transition curve with a radius of curvature gradually changing from infinity to a radius of a circular curve; the moderation curve type is set between a straight line and a circular curve type or a circular curve identical to a turn whose radius differs by more than a threshold value.
The straight line segment replaces the circular curve type, namely:
Δl＝Rα－T
wherein Deltal is an influence value generated by replacing a circular curve with a straight line, R is the radius of the circular curve, T is the chord length, and alpha is the curve corner, namely the central angle of the circular curve.
The relaxation curve type is replaced by a straight line segment, namely:
wherein Deltal s For the influence value produced by a straight line instead of a circular curve, l s To alleviate the curve length l 0 For the theoretical relaxation curve length, R is the radius of the circular curve connected with the relaxation curve, p is the inner displacement, q is the tangential growth, beta 0 The center angle corresponding to the theoretical relaxation curve length is the relaxation curve angle.
4-3) comparing the influence values in the section mileage according to the road condition database, and evaluating according to the road condition database to obtain the road overall condition evaluation; this embodiment is discussed in terms of flatness analysis and rutting analysis:
4-3-1) rut analysis:
(1) And setting the corresponding format of data cleaning of each different detection device. For example, the automatic detection tool is in a format corresponding to the equipment A, and the format corresponding to the equipment B is in a tool B.
(2) The automatic detection is carried out by a inspector, the equipment A is adopted, and the detected data is local to the detection equipment. And automatically and real-timely transmitting the detected basic data to a data processing server through a 5G communication module.
(3) And recognizing that the data is the data transmitted by the equipment A, and cleaning and converting according to the corresponding format A in the format library.
Assuming that the road design adopts a general minimum radius, the length of the moderation curve adopts a minimum length value, and the running speeds of the detection vehicle are respectively 60km/h, 80km/h, 100km/h and 120km/h, the straight line segment replaces the influence value generated by the curve in one sampling interval to calculate the result shown in the following table 1.
Table 1 influence value calculation result table
As can be seen from the data of the table, the mileage influence value of replacing the curve segment with the straight line segment is in the centimeter level, and the larger the radius of the curve is, the smaller the influence value is; the longer the relief curve, the less impact (the actual design will typically be greater than the average minimum radius and minimum relief curve length, so its impact will be less).
The expert and the lead can see the road surface condition of the car after the car is passed, and make corresponding schemes and plans in real time.
4-3-2) flatness analysis:
(1) And setting the corresponding format of data cleaning of each different detection device. For example, the automatic detection tool is in a format corresponding to the equipment A, and the format corresponding to the equipment B is in a tool B.
(2) The calculation formula of each index and the related weight parameters, such as the road jump index (PBI), are set, and the formula is set as follows:
wherein PBI is road surface jump vehicle with i-th degree, a i The unit of the road surface jump of the i-th degree is deducted, the value is taken according to the stipulation of the following table, the type of the road surface jump is i 0 Taking 3 of the total number of road surface jump types;
table 2 road surface jumping vehicle score standard
(3) The automatic detection is carried out by a inspector, the equipment A is adopted, and the detected data is local to the detection equipment. And automatically and real-timely transmitting the detected basic data to a data processing server through a 5G communication module.
(4) And recognizing that the data is the data transmitted by the equipment A, and cleaning and converting according to the corresponding format A in the format library. As can be seen from table 2, for example, the calculation unit is 1km,1km has 1 heavy jump, and 0 jump is performed in medium jump and light jump:
calculated skip pbi=100- (0 x 0+0 x 25+1 x 50) =50 (units: minutes).
Other indexes are correspondingly calculated, and then the condition of the road surface is obtained. The expert and the lead can see the road surface condition of the car after the car is passed, and make corresponding schemes and plans in real time.
4-4) the road overall condition evaluation is sent to a database to be stored, and the application server waits for calling. And obtaining pavement analysis data, storing the pavement analysis data into a database, transmitting the pavement analysis data to an application server, and transmitting the pavement analysis data to each terminal through an Internet platform by the application server for real-time display. Through internet platform, carry out real-time show to archival data, the show can be in multi-terminal (office desktop, cell-phone, show hall's show large screen etc.), according to real-time testing result, can audio-visual look over to make corresponding scheme.
The foregoing is merely an embodiment of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, expansion, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (9)
1. An instant analysis method for pavement rut indexes based on automatic detection is characterized by comprising the following steps:
1) The laser road detection vehicle detects a road surface to acquire road surface index data;
2) Judging whether ruts exist on the road surface through a template convolution method according to the acquired road surface index data; if the track exists on the section of road surface, determining a corresponding road mileage stake mark of the road surface with the track, and storing road surface index data containing the mileage stake mark to a vehicle-mounted server, otherwise, directly storing the road surface index data to the vehicle-mounted server;
3) The vehicle-mounted server sends the road surface index data to the application server through the communication module;
4) The application server performs data processing analysis on the road surface index data to obtain road surface analysis data, saves the road surface analysis data to the database, and sends the road surface analysis data to the application server, and the application server sends the road surface analysis data to each terminal through the Internet platform for real-time display.
2. The method for the instantaneous analysis of the rutting index on the basis of the automatic detection according to claim 1, wherein said step 2) comprises the following steps:
(1) Defining a detection template;
(2) Roaming the vertical center line of the detection template along the advancing direction of the detection template to enable the vertical center line of the template to coincide with the center line of the road detection cross section;
(3) Extracting the relative elevation H of a measured pavement and a reference surface adopted by the detection of the laser road detection vehicle from pavement index data; setting a threshold D 0 For all detected relative elevations H i Making the following determination;
when H is i ＞D 0 When (1) is equal to 0 for all relative elevations, H is equal to i ＞D 0 Processing; n is a F (Xi) function;
when H is i ＞D 0 In the case of taking F (X) i )＝H j J is H i ≤D 0 And the closest point to point i, namely: h i ＞D 0 When the relative elevation of the point j is taken as the relative elevation H of the point j ；
The coefficient z of the template is corresponding to the relative elevation H of the template i Performing product operation, adding all obtained product values, and accumulating the relative elevation H of the template s The method comprises the following steps:
wherein, the coefficient z on the template is 1;
(4) According to accumulated relative elevation H of templates s Obtaining the average value H of the relative elevation p The method comprises the following steps:
H p ＝H s ÷n
wherein n is the corresponding relative elevation H i Is the number of (3);
(5) Average value H of the relative elevation of the template and the relative elevation obtained in the step (4) p Difference is made to obtain the difference delta H between the relative elevation and the mean value of each point i The method comprises the following steps:
ΔH i ＝H i -H p
(6) Checking the difference delta H calculated in step (5) i Setting DeltaH i Determining whether there is ΔH exceeding the fluctuation section i If there are one or more delta H i Extracting ΔH beyond the fluctuation interval i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, judging that the track does not exist on the section of road surface;
(7) From extracting DeltaH exceeding the fluctuation interval i And determining the corresponding road mileage stake marks, wherein ruts exist in the range of the mileage stake marks.
3. The automatic detection-based pavement rut index instant analysis method according to claim 2, wherein the defining templates are specifically:
defining the length and width of the template: the detection width of the laser road detection vehicle is the width of one lane; starting to set any position, detecting 1 cross section to be 5-20 cm;
when the width of the template in the scanning data of the laser road detection vehicle is defined, the length is the length of 5 detection cross sections in the advancing process by taking the scanning width when the data are acquired.
4. The method for the instantaneous analysis of a rutting index on a pavement based on automatic detection according to claim 1, wherein said step 4) comprises the following steps:
a) Carrying out format cleaning on the pavement index data received by the application server according to a template format library, and cleaning repeated data in the pavement index data;
b) Analyzing the road surface index data after the format cleaning, acquiring the section mileage according to the mileage stake mark, and acquiring an influence value in the section mileage according to the section mileage;
c) Comparing the influence values in the section mileage according to the road condition database, and evaluating according to the road condition database to obtain the road overall condition evaluation;
d) And the road overall condition evaluation is sent to a database for storage, and the application server waits for calling.
5. The method for real-time analysis of pavement rut index based on automatic detection according to claim 4, wherein in step b), the obtaining the section mileage according to the mileage stake mark is specifically:
according to two points delta H close to each other i And calculating the distance between the two points according to the coordinate difference of the two points to obtain the instantaneous speed of the detection vehicle, and accumulating according to the instantaneous speed of the detection vehicle to obtain the section mileage.
6. The method for real-time analysis of pavement rut index based on automatic detection according to claim 4, wherein in step b), the influence value in the section mileage is obtained according to the section mileage, specifically:
judging the section mileage types, which are respectively: a circle curve type and a relaxation curve type;
by straight line segments instead of circular curvesModel and buffer curve type, and obtaining influence value delta l or delta l in section mileage s 。
7. The automated detection-based pavement rut index instant analysis method according to claim 6, wherein said circular curve type is: the arc-shaped curves are arranged for connecting two adjacent straight-line sections when the trend of the road plane changes direction or the slope changes vertically;
the type of the relaxation curve is as follows: a transition curve with a radius of curvature gradually changing from infinity to a radius of a circular curve; the moderation curve type is arranged between a straight line and a circular curve type or a circular curve with the same turning direction as the radius of the circular curve differs by more than a threshold value.
8. The method for real-time analysis of pavement rut index based on automatic detection according to claim 6, wherein said passing straight line segment replaces a circular curve type, namely:
Δl＝Rα－T
wherein Deltal is an influence value generated by replacing a circular curve with a straight line, R is the radius of the circular curve, T is the chord length, and alpha is the curve corner, namely the central angle of the circular curve.
9. The method for real-time analysis of pavement rut index based on automatic detection according to claim 6, wherein said passing straight line segment replaces the moderation curve type, namely:
wherein Deltal s For the influence value produced by a straight line instead of a circular curve, l s To alleviate the curve length l 0 For the theoretical relaxation curve length, R is the radius of the circular curve connected with the relaxation curve, p is the inner displacement, q is the tangential growth, beta 0 The center angle corresponding to the theoretical relaxation curve length is the relaxation curve angle.
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| CN117351338A (en) * | 2023-12-04 | 2024-01-05 | 北京理工大学前沿技术研究院 | Automatic pile correction method, system and equipment |
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| CN117351338A (en) * | 2023-12-04 | 2024-01-05 | 北京理工大学前沿技术研究院 | Automatic pile correction method, system and equipment |
| CN117351338B (en) * | 2023-12-04 | 2024-02-13 | 北京理工大学前沿技术研究院 | Automatic pile correction method, system and equipment |
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