CN106327485B - A kind of bituminous pavement rainy season light maintenance early warning system and its method - Google Patents
A kind of bituminous pavement rainy season light maintenance early warning system and its method Download PDFInfo
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Abstract
The invention discloses a kind of bituminous pavement rainy season light maintenance early warning system and its methods.Include the following steps: to carry out identification and statistics to Asphalt Pavement Damage, forms pavement disease database；It carries out big data collection and establishes rainy season pit repairing rate prediction model；Establish rainy season light maintenance (pit repairing) early warning system；Bituminous pavement rainy season light maintenance index is issued by mobile terminal before rainy season set, instructs the rainy season maintenance job of freeway management unit.The method that the present invention uses big data analysis application, data collection and analysis is carried out to the Crack cause of rainy season asphalt pavement pit, establish the rainy season pit repairing rate prediction model for meeting engineering experience, bituminous pavement rainy season light maintenance index is issued before rainy season set by rainy season light maintenance early warning system, it carries out prediction and early warning in advance for freeway management unit rainy season maintenance job, also provides platform foundation for " pre-event management ".
Description
Technical field
The present invention relates to the engineer application of Road Detection data more particularly to a kind of bituminous pavement rainy season light maintenance early warning systems
And its method.
Background technique
Quickly detection is current in national arterial highway maintenance management inspection (" state's inspection ") and each province for highway condition automation
It is widely applied in the maintenance management work of part highway administration person, detection data is mainly used for the road conditions of road network grade
It can evaluate, the formulation for conserving science decision for road management person provides Technical Reference.But it is on the whole, automatic in highway condition
Change quick detection data application aspect, there is also following disadvantages: data acquisition, analysis cost are larger, but also to the excavation of data
Not deep enough, outcome data form is mainly also more extensive evaluation of pavement condition data；Application is single, to the road of road network grade
Manager has certain help, but little to the effect of one line unit of road maintenance.Therefore, to overcome the above disadvantages, to road
Pavement disease data have carried out deep excavation and analysis, with the thinking and analysis method of big data, cheat to rainy season bituminous pavement
Slot formed influence factor analyzed, to its positively related influence factor, establish rainy season pit repairing rate prediction model
Equation and rainy season light maintenance (pit repairing) index, and further establish rainy season light maintenance early warning system, with to one line list of road maintenance
Position carries out push early warning, carries out personnel, technology, equipment, goods and materials and prediction scheme in advance convenient for one line unit of road maintenance and prepares, establishes
Refine maintenance management.
Summary of the invention
Based on this, it is necessary to provide a kind of bituminous pavement rainy season light maintenance early warning system and its method.
A kind of bituminous pavement rainy season light maintenance early warning system, including pavement disease identification module, pavement disease database, rainy season
Pit repairing rate prediction module, rainy season light maintenance warning module, mobile terminal；
The pavement disease identification module defect information in bituminous pavement picture for identification, and the information after identification is stored
In pavement disease database, rainy season pit repairing rate prediction module reads the data in pavement disease database, and according to going through
History precipitation in rain season, the history rainy season monthly volume of traffic establish rainy season pit repairing rate prediction model by regression analysis,
Establish rainy season light maintenance index and index classification；Rainy season light maintenance warning module according to will arrive precipitation in rain season, rainy season monthly friendship
The disease overall size of flux data and previous year section unit lane kilometer estimates rainy season current year pit repairing rate, and will
Rainy season current year pit repairing rate is compared with rainy season previous year pit repairing rate, when fiducial value is more than setting value, is passed through
Wireless network carries out early warning to mobile terminal.
The system further includes road synthetic detection vehicle, and road synthetic detection vehicle is for acquiring bituminous pavement picture.
A kind of bituminous pavement rainy season light maintenance method for early warning:
1) pavement disease identification module carries out the disease in bituminous pavement picture to draw frame identification one by one, to determine road surface disease
Harmful size, type and severity；The size of frame identification is drawn to calculate disease, disease size computing method are as follows: crack
Class disease calculates the length for drawing the cornerwise length of frame as crack, and repairing class disease, which calculates, draws frame length and width, obtains disease
Evil area；
2) the pavement disease data after calculating are stored in pavement disease database by pavement disease identification module；Road surface disease
Evil database establishes label to the pavement disease data that each is stored respectively, and the label includes pavement disease position, ruler
Very little, type, severity, to establish pavement disease database；
3) using precipitation in rain season, the rainy season monthly volume of traffic, section previous year disease overall size as rainy season asphalt road
The Crack cause of face pit slot；Using multinomial regression analysis, establishes rainy season pit repairing rate and precipitation in rain season, rainy season hand over
Regression equation between flux and the disease overall size of previous year, as rainy season pit repairing rate prediction model；
4) upcoming precipitation in rain season, rainy season monthly traffic data are collected, is searched from pavement disease database
The disease overall size data of previous year section unit lane kilometer；By rainy season pit repairing rate prediction model, rain is predicted
Season pit repairing rate；Using pavement disease statistics of database rainy season previous year pit repairing rate, by the growth of pit repairing rate
Rate is defined as rainy season light maintenance index, and is classified to index；
5) the light maintenance early warning of bituminous pavement rainy season is carried out according to obtained rainy season light maintenance index and rating information, before rainy season,
Rainy season light maintenance index and rating information are issued to mobile terminal by wireless network, early warning is carried out to rainy season light maintenance pressure.
Preferably, the step 1) method particularly includes: right according to " highway technology status assessment standard " JTGH20-2007
Pavement disease size, the range found in the picture of road surface is determined by way of drawing frame, to Damage Types, severity into
Line identifier calculates drawing frame area or length, calculation method are as follows: crack class disease, which calculates, draws the cornerwise length conduct of frame
The length in crack, repairing class disease, which calculates, draws frame length and width, obtains disease area；And to drawing frame trace, area or length
It is saved.
Preferably, the regression equation are as follows:
ynIndicate the rainy season pit repairing rate (lane m2/ kilometer) in the n-th year of the section；
a0、a1、a2、a3For parameter；
x1Represent the affiliated regional precipitation total amount (mm) in month in rainy season section；
x2Represent the monthly volume of traffic in month in rainy season section (ten thousand/number of track-lines)；
Indicate the disease capacity (m of the section (n-1)th annual unit lane kilometer2/ lane kilometer).
Preferably, index is classified method particularly includes:
When predicting rainy season current year light maintenance indices P RCI, PRCI less than 10%, pit slot amount is close with last year, or even reduces, small
It is little to repair pressure；More than or equal to 10%, less than 30% when, pit slot amount compared with last year slightly increase, maintenance unit need to pay close attention to；Greater than etc.
In 30%, less than 60% when, pit slot amount increases by a fairly big margin compared with last year, maintenance unit need to pay close attention in advance；When more than or equal to 60%, hole
Slot amount has extensive growth compared with last year, and maintenance unit needs serious concerns, is ready work.
Compared with the prior art, the invention has the beneficial effects that:
1, deep excavation has been carried out to using the resulting pavement disease data of high cost detection, has improved data application valence
Value.
2, with the thinking of big data and analysis method, find out and determined the positive correlation that rainy season asphalt pavement pit is formed
Influence factor.
3, pass through rainy season asphalt pavement pit repairing rate prediction model equation, rainy season light maintenance (pit repairing) index and index
The rainy season maintenance job of one line unit of road maintenance has been carried out quantitative analysis by the form of classification.
4, the rainy season light maintenance early warning system established can in advance carry out the rainy season maintenance job amount for conserving a line unit pre-
Alert, the technological service for improving detection unit is horizontal.
5, one line unit of road maintenance will be mentioned to pre-event management with subsequent management in thing, can be obviously improved by this system
The fining maintenance management of one line unit of road maintenance is horizontal.
Detailed description of the invention
Fig. 1 is Asphalt Pavement Damage identification and automated drafting system schematic diagram；
Fig. 2 is Asphalt Pavement Damage manual identified schematic diagram；
Fig. 3 is rainy season light maintenance index and index classification figure.
Specific embodiment
A kind of bituminous pavement rainy season light maintenance early warning system, including pavement disease identification module, pavement disease database, rainy season
Pit repairing rate prediction module, rainy season light maintenance warning module, mobile terminal；
The pavement disease identification module defect information in bituminous pavement picture for identification, and the information after identification is stored
In pavement disease database, rainy season pit repairing rate prediction module reads the data in pavement disease database, and according to going through
History precipitation in rain season, the history rainy season monthly volume of traffic establish rainy season pit repairing rate prediction model by regression analysis,
Establish rainy season light maintenance index and index classification；Rainy season light maintenance warning module according to will arrive precipitation in rain season, rainy season monthly friendship
The disease overall size of flux data and previous year section unit lane kilometer estimates rainy season current year pit repairing rate, and will
Rainy season current year pit repairing rate is compared with rainy season previous year pit repairing rate, when fiducial value is more than setting value, is passed through
Wireless network carries out early warning to mobile terminal.
The system further includes road synthetic detection vehicle, and road synthetic detection vehicle is for acquiring bituminous pavement picture.
As shown in Figure 1, a kind of bituminous pavement rainy season light maintenance method for early warning of the invention includes the following steps:
1) by automating the true picture in road surface that quick detection device acquisition includes defect information, pavement disease identifies mould
Block carries out the disease in the true picture of bituminous pavement to draw frame identification one by one, to determine the size of pavement disease, type and serious
Degree；The size of frame identification is drawn to calculate disease, disease size computing method are as follows: crack class disease, which calculates, draws frame diagonal line
Length of the length as crack, repairing class disease, which calculates, draws frame length and width, obtains disease area；Disease recognition foundation
" highway technology status assessment standard " JTGH20-2007, the pavement disease size found in road pavement picture, range are by drawing frame
Mode be determined, Damage Types, severity are identified, to drawing frame area or length to calculate, calculation method
Are as follows: crack class disease calculates the length for drawing the cornerwise length of frame as crack, and repairing class disease, which calculates, draws frame length and width,
Obtain disease area；And drawing frame trace, area or length are saved；Damage Types are divided into crack class in the embodiment of the present invention
Disease and repairing class disease, wherein class disease in crack has transverse crack, longitudinal crack, cracking, blocky crack；Repairing class disease has
Crack repairing and blocky repairing.
Fig. 2 shows traces and recognition result that Asphalt Pavement Damage manually draws frame to identify, the pavement disease type in figure
For transverse crack；
2) the pavement disease data after calculating are stored in pavement disease database by pavement disease identification module；Road surface disease
Evil database establishes label to the pavement disease data that each is stored respectively, and the label includes pavement disease position, ruler
Very little, type, severity, to establish pavement disease database；
3) qualitative from single factor test by theoretical research analysis and bound bitumen pavement maintenance management reality of work empirical discovery
From the point of view of analysis, precipitation influences pavement structural strength, rigidity and stability, therefore is positively correlated with rainy season pit repairing rate；It hands over
The structural failure of flux, especially heavy traffic flow road pavement is very big, makes that pavement life is obviously shortened and pit slot is damaged further
The reason of extension, rainy season pit repairing rate are also positively correlated with it；Section previous year disease overall size includes that road surface is whole
Situation is road bed information, is also positively correlated with rainy season pit repairing rate.That is precipitation in rain season, the rainy season volume of traffic and upper one
The disease overall size of annual section unit lane kilometer is positively correlated with rainy season asphalt pavement pit repairing rate, establishes rainy season
Asphalt pavement pit repairing rate is feasible with its prediction model for being positively correlated factor.
By theory literature determine rainy season asphalt pavement pit formation influence factor, incorporation engineering it is empirically determined with
Rainy season asphalt pavement pit forms the influence factor being positively correlated, i.e. precipitation in rain season, the rainy season volume of traffic and previous year section
The disease overall size of unit lane kilometer；It is total with precipitation in rain season, the rainy season monthly volume of traffic, section previous year disease scale
Measure the Crack cause as rainy season asphalt pavement pit；Using multinomial regression analysis, establish rainy season pit repairing rate with
Regression equation between the disease overall size of precipitation in rain season, the rainy season volume of traffic and previous year, as rainy season pit repairing rate
Prediction model；
4) upcoming rainy season precipitation is collected from units such as the road persons of local meteorological department, traffic department and road network grade
Amount, rainy season monthly traffic data search the disease rule of previous year section unit lane kilometer from pavement disease database
Mould aggregate data；By rainy season pit repairing rate prediction model, rainy season pit repairing rate is predicted；It is united using pavement disease database
Count rainy season previous year pit repairing rate, the growth rate of pit repairing rate be defined as rainy season light maintenance index, and to index number into
Row classification；
5) the light maintenance early warning of bituminous pavement rainy season is carried out according to obtained rainy season pit repairing rate, before rainy season, by wireless
Network issues rainy season light maintenance index to mobile terminal, carries out early warning to rainy season light maintenance pressure.
Further, by rainy season light maintenance early warning system, instruct freeway management unit carry out in advance personnel, technology,
Equipment, goods and materials and prediction scheme prepare.
Using the method for multinomial regression analysis, rainy season pit repairing rate and precipitation in rain season, rainy season traffic are established
Regression equation between amount and the disease overall size of previous year predicts rainy season current year pit repairing rate, forms rainy season light maintenance and refers to
The classification of several and index.
In the present embodiment, by regression analysis, following regression equation is determined:
ynIndicate the rainy season pit repairing rate (lane m2/ kilometer) in the n-th year of the section；
a0、a1、a2、a3For parameter；
x1The affiliated regional precipitation total amount (mm) in month in rainy season 4-6 section is represented, data source is in province's hydrology board web rainwater
Monthly magazine；
x2The monthly volume of traffic in month in rainy season 4-6 section (ten thousand/number of track-lines) is represented, if affiliated section exists simultaneously two kinds
The above carriageway type (such as the road sections part two-way six-lane, part two-way four-lane), then according to different carriageway type mileages
To convert the section number of track-lines；
Indicate the disease capacity (lane m2/ kilometer) of the section (n-1)th annual unit lane kilometer, wherein transverse fissure, lobe
Width reference area is influenced according to 0.2m.
As shown in figure 3, index is classified method particularly includes:
When predicting rainy season current year light maintenance indices P RCI, PRCI less than 10%, pit slot amount is close with last year, or even reduces, small
It is little to repair pressure；More than or equal to 10%, less than 30% when, pit slot amount compared with last year slightly increase, maintenance unit need to pay close attention to；Greater than etc.
In 30%, less than 60% when, pit slot amount increases by a fairly big margin compared with last year, maintenance unit need to pay close attention in advance；When more than or equal to 60%, hole
Slot amount has extensive growth compared with last year, and maintenance unit needs serious concerns, is ready work.
Regression equation and the engineering verification situation of index classification are as follows:
By taking the section of 3 groups of pavement structure similitudes as an example, the lookup pavement disease data in pavement disease database, and from
Precipitation in rain season data, rainy season monthly traffic data are collected by local meteorological department, traffic department, press above-mentioned regression equation respectively
It is verified.
1) certain Soft Roadbed:
2013 to 2015 4 variable datas of Liang Ge administrative office are collected as shown in following table -2, XX administrative office 2013-2015
Year, totally 5 groups of data are fitted XX administrative office 2013-2015, as a result as shown in Table-1.
- 1 XX1 of table I administrative office of high speed and each variable data of II administrative office
- 2 XX1 of table I administrative office of high speed and II administrative office's fitting result
| Parameter | Numerical value |
| a0 | 1.2107 |
| a1 | -0.0040826 |
| a2 | 0.0074321 |
| a3 | -0.058457 |
| Square (R^2) of related coefficient | 0.9991 |
Then I administrative office, 2015 annual data is checked with -2 result of table and regression equation, rainy season pit slot is small within 2015
Repair rate predicted value are as follows:
Y=a0+(a1*x1+a2*x2*x3+a3*x3)2
=1.2107+ (- 4.083e-3*391.9+7.4321e-3*15.14*29.31
-5.8457e-2*29.31)2
=1.2109
0.044 is differed with actual result, fitting result is preferable.
Road surface rainy season light maintenance rate index:Road surface rainy season light maintenance refers to
Number is 1 grade.
2) XX2 III administrative office of high speed and IV administrative office:
2013 to 2015 4 variable datas of Liang Ge administrative office are collected as shown in following table -3, XX administrative office
Totally 5 groups of data are fitted 2013-2015, XX administrative office 2013-2015, as a result as shown in table -5:
- 3 XX2 of table III administrative office of high speed and each variable data of IV administrative office
- 4 XX2 of table III administrative office of high speed and IV administrative office's fitting result
| Parameter | Numerical value |
| a0 | 0.2808 |
| a1 | -0.00046365 |
| a2 | 0.0010868 |
| a3 | -0.011963 |
| Square (R^2) of related coefficient | 0.9754 |
Then IV administrative office, 2015 annual data is checked with -4 result of table and regression equation, rain in 2015
Season pit slot light maintenance rate predicted value are as follows:
Y=a0+(a1*x1+a2*x2*x3+a3*x3)2
=0.2808+ (- 4.6365e-4*692.6+1.0868e-3*37.84*17.1842
-1.1963e-2*17.1842)2
=0.3132
0.0099 is differed with actual result, fitting result is preferable.
Road surface rainy season light maintenance index:Road surface rainy season light maintenance index
It is 2 grades.
3) V administrative office of XX3 high speed, VI administrative office and VII administrative office:
Three administrative offices, 2014 to 2015 4 variable datas are collected as shown in following table -5, V administrative office 2014-2015
Year, totally 5 groups of data are fitted for VI administrative office 2014-2015 and VII administrative office 2014, as a result as shown in table -5:
V administrative office of table -5 XX3 high speed, VI administrative office and the fitting of VII administrative office's data
V administrative office of table -6 XX3 high speed, VI administrative office and VII administrative office's fitting result
| Parameter | Numerical value |
| a0 | 0.0222 |
| a1 | 0.0039328 |
| a2 | 0.0096558 |
| a3 | -0.4423 |
| Square (R^2) of related coefficient | 0.9644 |
Then VII administrative office, 2015 annual data is checked with -6 result of table and regression equation, rainy season pit slot is small within 2015
Repair rate predicted value are as follows:
Y=a0+(a1*x1+a2*x2*x3+a3*x3)2
=0.0222+ (3.9328e-3*1020+9.6558e-3*17.58*11.56
-0.4423*11.56)2
=0.7551
0.078 is differed with actual result, fitting result is preferable.
Road surface rainy season light maintenance index:Road surface rainy season light maintenance index
It is 1 grade.
Verify conclusion:
1) from the point of view of three cases, regression equation has preferable correlation (phase to the fitting result of different highways
Relationship number square both greater than 0.96), illustrate the regression analysis of the proper rainy season pit slot light maintenance rate of fitting of a polynomial form；
2) it from the point of view of three cases, is predicted with rainy season pit slot light maintenance rate of the formula being fitted to the coming year, predicted value
Relatively with actual value；
3) since during actual prediction, precipitation in rain season and the volume of traffic are all discreet values, it is therefore necessary to rainy season
Pit slot light maintenance rate carries out indexation classification, proposes road surface rainy season light maintenance index, and carrying out indexation classification later can be clear
To freeway management unit, the light maintenance preparation before rainy season carries out early warning on ground.
4) engineering experience shows that the indexation grade scale and engineering management are practical relatively.
Claims (4)
1. a kind of method for early warning based on bituminous pavement rainy season light maintenance early warning system, it is characterised in that the bituminous pavement rainy season is small
Repairing early warning system includes pavement disease identification module, pavement disease database, rainy season pit repairing rate prediction module, rainy season light maintenance
Warning module, mobile terminal；
The pavement disease identification module defect information in bituminous pavement picture for identification, and the information after identification is stored in road
In the disease database of face；Rainy season pit repairing rate prediction module reads the data in pavement disease database, and according to history rain
Seasonal rainfall amount, the history rainy season monthly volume of traffic and section previous year disease overall size establish rain by regression analysis
Season pit repairing rate prediction model；Rainy season light maintenance warning module is according to precipitation in rain season, the rainy season monthly volume of traffic number that will be arrived
According to the disease overall size with previous year section unit lane kilometer, rainy season current year pit repairing rate is estimated, and obtain rainy season
Light maintenance index and index classification carry out publication and early warning to mobile terminal by wireless network；
Method is as follows:
Step 1): pavement disease identification module carries out the disease in bituminous pavement picture to draw frame identification one by one, to determine road surface
Size, type and the severity of disease；The size of frame identification is drawn to calculate disease, disease size computing method are as follows: split
It stitches class disease and calculates the length for drawing the cornerwise length of frame as crack, repairing class disease, which calculates, draws frame length and width, obtains
Disease area；
Step 2): the pavement disease data after calculating are stored in pavement disease database by pavement disease identification module；Road surface
Disease database establishes label to the pavement disease data that each is stored respectively, the label include pavement disease position,
Size, type, severity, to establish pavement disease database；
Step 3): using precipitation in rain season, the rainy season monthly volume of traffic, section previous year disease overall size as rainy season asphalt road
The Crack cause of face pit slot；Using multinomial regression analysis, establishes rainy season pit repairing rate and precipitation in rain season, rainy season hand over
Regression equation between flux and the disease overall size of previous year, as rainy season pit repairing rate prediction model；
Step 4): upcoming precipitation in rain season, rainy season monthly traffic data are collected, is searched from pavement disease database
The disease overall size data of previous year section unit lane kilometer；By rainy season pit repairing rate prediction model, rain is predicted
Season pit repairing rate；Using pavement disease statistics of database rainy season previous year pit repairing rate, by the growth of pit repairing rate
Rate is defined as rainy season light maintenance index, and is classified to index；
Step 5): carrying out the light maintenance early warning of bituminous pavement rainy season according to obtained rainy season light maintenance index and rating information, before rainy season,
Rainy season light maintenance index and rating information are issued to mobile terminal by wireless network, early warning is carried out to rainy season light maintenance pressure.
2. the method as described in claim 1, it is characterised in that the step 1) method particularly includes: according to " highway technology situation
Evaluation criteria " JTG H20-2007, pavement disease size, the range found in road pavement picture carry out really by way of drawing frame
It is fixed, Damage Types, severity are identified, drawing frame area or length are calculated, calculation method are as follows: crack class disease
The length for drawing the cornerwise length of frame as crack is calculated, repairing class disease, which calculates, draws frame length and width, obtains disease area；
And drawing frame trace, area or length are saved.
3. the method as described in claim 1, it is characterised in that the regression equation are as follows:
ynIndicate the rainy season pit repairing rate (lane m2/ kilometer) in the n-th year of the section；
a0、a1、a2、a3For parameter；
x1Represent the affiliated regional precipitation total amount (mm) in month in rainy season section；
x2Represent the monthly volume of traffic in month in rainy season section (ten thousand/number of track-lines)；
Indicate the disease capacity (m of the section (n-1)th annual unit lane kilometer2/ lane kilometer).
4. the method as described in claim 1, it is characterised in that index classification method particularly includes:
When predicting rainy season current year light maintenance indices P RCI, PRCI less than 10%, pit slot amount is close with last year, or even reduces, light maintenance pressure
Power is little；More than or equal to 10%, less than 30% when, pit slot amount compared with last year slightly increase, maintenance unit need to pay close attention to；It is more than or equal to
30%, when less than 60%, pit slot amount increases by a fairly big margin compared with last year, and maintenance unit need to be paid close attention in advance；When more than or equal to 60%, pit slot
Amount has extensive growth compared with last year, and maintenance unit needs serious concerns, is ready work.
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| CN110390125A (en) * | 2019-05-15 | 2019-10-29 | 南京金蓝智慧城市规划设计有限公司 | Bituminous pavement failure analysis method based on correlation model |
| CN111369051B (en) * | 2020-03-03 | 2023-04-07 | 交通运输部公路科学研究所 | Method for predicting rut and pit diseases of asphalt pavement |
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