CN105628904A - Ground penetrating radar based water content detection method for railroad bed - Google Patents
Ground penetrating radar based water content detection method for railroad bed Download PDFInfo
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Abstract
The invention relates to a ground penetrating radar based water content detection method for a railroad bed, and belongs to the technical field of railroad bed detection. The method is implemented by the following steps: detecting data segments by a ground penetrating radar of the railroad bed; performing power spectrum analysis on detection data; performing forward simulation and performing power spectrum analysis on simulation data; performing regression analysis; calculating the water content corresponding to each frequency band range; comprehensively evaluating the water content; and drawing a water content distribution curve of a detection line and identifying a water content abnormal region. According to the method, quick, nondestructive and efficient quantitative detection of water content distribution of the railway bed is realized, bases are provided for state detection evaluation of the railway bed, and railway maintenance and repair departments are assisted in timely mastering roadbed states and carrying out maintenance and repair work.
Description
Technical field
The invention belongs to inspection of railway subgrade technical field, relate to a kind of railway bed moisture content detecting method based on GPR.
Background technology
Roadbed is the basis of railroad track, as rail track substructure, traffic safety plays vital effect. Subgrade defect is not only possible causes track uneven subsidence, causes rail level state seriously bad, increases line maintenance workload, affects the properly functioning of train, be also possible to jeopardize train operating safety time serious. How to grasp the health status of roadbed in time, take necessary maintenance measure to slow down disease, while continuing to increase axle weight, improving conevying efficiency, ensure traffic safety, become key subjects currently urgently to be resolved hurrily.
Moisture content is bigger than normal be cause rising soil, main external condition that the main Defect of Foundation Bed of Express such as subgrade subsidence is formed. GPR geophysical probing technique quick as one, lossless, efficient, the application in inspection of railway subgrade field is more and more extensive, it is possible to railway bed construction quality is monitored, detection roadbed thickness of ballast bed and surface layer of subgrade bed thickness etc. At present, from ground penetrating radar image, it is possible to rely on the moisture situation of artificial experience qualitative interpretation subgrade bed, but accurate moisture content data cannot be provided. For doubtful moisture section bigger than normal, detect further only by probing and centrifugal modeling, be costly and inefficient so that railway maintenance maintenance department cannot grasp roadbed state in time, carry out maintenance work.
Summary of the invention
The technical problem to be solved is cannot to realize the shortcoming of railway bed moisture content qualitative detection for GPR in prior art and provide a kind of railway bed moisture content detecting method based on GPR.
This invention address that its technical problem the technical scheme is that a kind of railway bed moisture content detecting method based on GPR, specifically include:
S1, railway bed GPR detection data sectional: railway bed GPR is detected data and is divided into isometric data cell, each data cell comprises road, k �� 10 radar data, k should be positioned at (1,10), between, each data cell takes mileage corresponding to its road, kth �� 5 radar data mileage as this data cell;
S2, to detection data carry out power spectrumanalysis: to described step S1 obtain data cell process one by one, detailed process is:
A) the radar wave power spectrum of whole data cell is calculated;
B) radar wave gross energy in time domain scale is calculated;
C) radar wave is calculated at 0MHz ~ 100MHz, 100MHz ~ 200MHz, 200MHz ~ 300MHz, 300MHz ~ 400MHz, the energy in 8 frequency band ranges between 400MHz ~ 500MHz, 500 ~ 600MHz and 100MHz ~ 300MHz, 300MHz ~ 500MHz accounts for the percentage ratio of gross energy;
S3, forward simulation, analog data is carried out power spectrumanalysis: utilize geological radar numerical simulation software to set up the railway bed model with different water cut, radar data is obtained by numerical simulation, the radar data of each model is regarded as a data cell, carries out power spectrumanalysis according to described step S2;
S4, regression analysis: the energy percentage of each frequency band range of radar wave of the model data that described step S3 obtains is carried out regression analysis respectively with the moisture content of model, obtaining the energy percentage of each frequency band range and the fit equation of moisture content, what select correlation coefficient is effective fit equation;
S5, calculate the moisture content that each frequency band range is corresponding: the described step S1 data cell obtained is calculated one by one, the energy percentage of each frequency band range of radar wave is substituted into respectively the described step S4 corresponding effectively fit equation obtained, calculates the moisture content that each frequency band range of this data cell is corresponding;
S6, moisture content overall merit: the described step S1 data cell obtained is calculated one by one, to the described step S5 moisture content obtained, be weighted according to the correlation coefficient of effective fit equation, obtain the moisture content comprehensive evaluation result x of this data cell;
S7, drafting detection circuit porous media curve, identify moisture content abnormal area: with mileage be abscissa, moisture content is for vertical coordinate, the moisture content comprehensive evaluation result x of described step S6 each data cell obtained is drawn curve, and it is moisture content abnormal area that moisture content exceedes the section of detection circuit design standard.
Further, the railway bed model in described step S3 comprises 3 layers, is railway roadbed, surface layer of subgrade bed and bottom layer of subgrade respectively, and each layer thickness is identical with detection section, and the soil property type of surface layer of subgrade bed is identical with detection section.
Further, the number of the railway bed model in described step S3 is no less than 5, and moisture content uniformly increases, and moisture content minima is less than 15%, and moisture content maximum is more than 30%.
Further, the radar wave parameter when radar wave parameter that the numerical simulation in described step S3 adopts is with detection roadbed is identical.
Further, the regression analysis in described step S4 adopts linear equation to be fitted.
Further, the method for the weighted calculation in described step S6 is:, in formula: n is effective fit equation number, for the moisture content that each frequency band range is corresponding, for the correlation coefficient of effective fit equation.
The invention has the beneficial effects as follows: the present invention obtains the energy percentage of each frequency band range of radar wave and the fit equation of moisture content by forward simulation and regression analysis, and the moisture content that separate equation calculated is weighted the comprehensive evaluation result obtaining detection circuit moisture content, identify moisture content abnormal area, achieve quick, lossless, the detection by quantitative to railway bed moisture content, the generation of the subgrade defects such as accurate early warning is risen soil, subgrade subsidence, ensures railway operation safety.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of the present invention.
Fig. 2 is the power spectrum chart of a data cell of one embodiment of the present of invention.
Fig. 3 is the model data regression analysis curve of one embodiment of the present of invention.
Fig. 4 is the detection circuit moisture content comprehensive evaluation result curve of one embodiment of the present of invention.
Detailed description of the invention
Embodiment 1: detection section is south China rail track, detects mileage 10km. Ragstone roadbed location thickness of ballast bed is 35cm; Subgrade bed thickness 3m, its mesexine 0.7m, adopt graded broken stone filling technology; Bottom 2.3m, adopts conditioned soil to fill.
Using Italy's IDS GPR, select 400MHz antenna, sampling channel spacing is 0.1m, collects 100,000 road radar datas altogether. It is as follows that each step is embodied as details:
S1, railway bed GPR detection data sectional: railway bed GPR is detected data and is divided into 2000 isometric data cells, each data cell comprises 50 road radar datas, take mileage corresponding to its 25th road radar data mileage as this data cell, namely the mileage of first data cell is 2.5m, and the mileage of second data cell is 7.5m;
S2, to detection data carry out power spectrumanalysis: to described step S1 obtain 2000 data cells process one by one, detailed process is:
A) adopting welch method to seek the radar wave power spectrum of whole data cell, Fig. 2 is the power spectrum of a data cell;
B) radar wave gross energy in time domain scale is calculated;
C) radar wave is calculated at 0MHz ~ 100MHz, 100MHz ~ 200MHz, 200MHz ~ 300MHz, 300MHz ~ 400MHz, 400MHz ~ 500MHz, the energy in 8 frequency band ranges between 500 ~ 600MHz and 100MHz ~ 300MHz, 300MHz ~ 500MHz accounts for the percentage ratio of gross energy, and the result of calculation of 1 ~ 4 number unit is as shown in table 1:
TableThe each frequency band range energy of 1-4 number unit accounts for gross energy percentage ratio
S3, forward simulation, analog data is carried out power spectrumanalysis: utilize geological radar numerical simulation software GPRMAX to set up 9 railway bed models with different water cut, moisture content difference 10.5%, 13.5%, 16.5%, 19.5%, 22.5%, 25.5%, 28.5%, 31.5% and 34.5%, radar data is obtained by numerical simulation, the radar data of each model is regarded as a data cell, carrying out power spectrumanalysis according to described step S2, result of calculation is as shown in table 2:
The each frequency band range energy of table 2 forward model accounts for gross energy percentage ratio
S4, regression analysis: the energy percentage of each frequency band range of radar wave of the model data that described step S3 obtains is carried out regression analysis respectively with the moisture content of model, regression analysis curve and fit equation, correlation coefficient are as shown in Figure 3, what select correlation coefficient is effective fit equation, i.e. 0MHz ~ 100MHz, 200MHz ~ 300MHz, the fit equation of 300MHz ~ 400MHz, 400MHz ~ 500MHz and 100MHz ~ 300MHz, 300MHz ~ 500MHz totally 6 frequency band ranges;
S5, calculate the moisture content that each frequency band range is corresponding: the described step S1 data cell obtained is calculated one by one, the energy percentage of each frequency band range of radar wave is substituted into respectively the described step S4 corresponding effectively fit equation obtained, calculate this data cell at 0MHz ~ 100MHz, 200MHz ~ 300MHz, 300MHz ~ 400MHz, the moisture content that the fit equation of 400MHz ~ 500MHz and 100MHz ~ 300MHz, 300MHz ~ 500MHz totally 6 frequency band ranges is corresponding;
The result of calculation of 1 ~ 4 number unit is as shown in table 3:
The moisture content that table 31-4 number unit each frequency band range energy is corresponding
S6, moisture content overall merit: the described step S1 data cell obtained is calculated one by one, to the described step S5 moisture content (as shown in table 3) obtained, correlation coefficient (as shown in Figure 3) according to fit equation is weighted according to formula, obtain the moisture content comprehensive evaluation result of this data cell, the result of calculation of 1 ~ 4 number unit respectively 26.99%, 28.04%, 27.76% and 27.44%;
S7, draw detection circuit porous media curve, identify moisture content abnormal area: with mileage be abscissa, moisture content for vertical coordinate, by described step S6 obtain each data cell moisture content comprehensive evaluation result draw curve, as shown in Figure 4. This detection circuit design standard is that moisture content must not be higher than 15%, and therefore, the 0th ~ 350 meter of this circuit is the abnormal higher region of moisture content.
Embodiment described above is intended for the use of the present invention, but not limitation of the present invention, person skilled in the relevant technique, without departing from the spirit and scope of the present invention, various conversion or modification can also be made, therefore all equivalent technical schemes also should belong to scope of the invention, should be limited by each claim.
Claims (6)
1. the railway bed moisture content detecting method based on GPR, it is characterised in that described method comprises the steps of:
S1, railway bed GPR detection data sectional: railway bed GPR is detected data and is divided into isometric data cell, each data cell comprises road, k �� 10 radar data, k should be positioned at (1,10), between, each data cell takes mileage corresponding to its road, kth �� 5 radar data mileage as this data cell;
S2, to detection data carry out power spectrumanalysis: to described step S1 obtain data cell process one by one, detailed process is:
A) the radar wave power spectrum of whole data cell is calculated;
B) radar wave gross energy in time domain scale is calculated;
C) radar wave is calculated at 0MHz ~ 100MHz, 100MHz ~ 200MHz, 200MHz ~ 300MHz, 300MHz ~ 400MHz, the energy in 8 frequency band ranges between 400MHz ~ 500MHz, 500 ~ 600MHz and 100MHz ~ 300MHz, 300MHz ~ 500MHz accounts for the percentage ratio of gross energy;
S3, forward simulation, analog data is carried out power spectrumanalysis: utilize geological radar numerical simulation software to set up the railway bed model with different water cut, radar data is obtained by numerical simulation, the radar data of each model is regarded as a data cell, carries out power spectrumanalysis according to described step S2;
S4, regression analysis: the energy percentage of each frequency band range of radar wave of the model data that described step S3 obtains is carried out regression analysis respectively with the moisture content of model, obtaining the energy percentage of each frequency band range and the fit equation of moisture content, what select correlation coefficient is effective fit equation;
S5, calculate the moisture content that each frequency band range is corresponding: the described step S1 data cell obtained is calculated one by one, the energy percentage of each frequency band range of radar wave is substituted into respectively the described step S4 corresponding effectively fit equation obtained, calculates the moisture content that each frequency band range of this data cell is corresponding;
S6, moisture content overall merit: the described step S1 data cell obtained is calculated one by one, to the described step S5 moisture content obtained, the correlation coefficient according to effective fit equation, be weighted, obtain the moisture content comprehensive evaluation result x of this data cell;
S7, drafting detection circuit porous media curve, identify moisture content abnormal area: with mileage be abscissa, moisture content is for vertical coordinate, the moisture content comprehensive evaluation result x of described step S6 each data cell obtained is drawn curve, and it is moisture content abnormal area that moisture content exceedes the section of detection circuit design standard.
2. the method for claim 1, it is characterised in that the railway bed model in described step S3 comprises 3 layers, is railway roadbed, surface layer of subgrade bed and bottom layer of subgrade respectively, and each layer thickness is identical with detection section, and the soil property type of surface layer of subgrade bed is identical with detection section.
3. the method for claim 1, it is characterised in that the number of the railway bed model in described step S3 is no less than 10, and moisture content uniformly increases, minimum is 10%, is 50% to the maximum.
4. the method for claim 1, it is characterised in that the radar wave parameter when radar wave parameter that the numerical simulation in described step S3 adopts is with detection roadbed is identical.
5. the method for claim 1, it is characterised in that the regression analysis in described step S4 adopts linear equation to be fitted.
6. the method for claim 1, it is characterised in that the method for the weighted calculation in described step S6 is:, in formula: n is effective fit equation number, for the moisture content that each frequency band range is corresponding, for the correlation coefficient of effective fit equation.
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Cited By (2)
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| CN106018754A (en) * | 2016-07-27 | 2016-10-12 | 华北水利水电大学 | Construction method of fine compaction model for restructured soil |
| CN109324325A (en) * | 2017-07-31 | 2019-02-12 | 中南大学 | A kind of calculation method using the dirty rate of trailer-mounted radar signal extraction railway ballast |
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| CN109324325A (en) * | 2017-07-31 | 2019-02-12 | 中南大学 | A kind of calculation method using the dirty rate of trailer-mounted radar signal extraction railway ballast |
| CN109324325B (en) * | 2017-07-31 | 2021-01-29 | 中南大学 | Calculation method for extracting railway ballast contamination rate by utilizing vehicle-mounted radar signal |
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