CN117985076B - Method and system for evaluating service performance of ballastless track subgrade of high-speed railway - Google Patents
Method and system for evaluating service performance of ballastless track subgrade of high-speed railway Download PDFInfo
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Abstract
The invention relates to the technical field of railway monitoring, and discloses a method and a system for evaluating service performance of a ballastless track subgrade of a high-speed railway, wherein the method comprises the following steps: determining a patient interval through a ground monitoring system, determining a track bad interval through a vehicle monitoring system, dividing a risk road section and a patient road section according to the patient interval and the track bad interval, acquiring track dynamic response data of the risk road section and the patient road section through the track monitoring system, and acquiring roadbed static and dynamic response and object state data of the risk road section and the patient road section through the ground monitoring system, so that an evaluation index coefficient of the ballastless track roadbed service performance can be calculated based on the track dynamic response data, the roadbed static and dynamic response and the object state data, and further, a service performance evaluation result corresponding to the ballastless track roadbed can be obtained according to the evaluation index coefficient; the method solves the problems of low efficiency and low accuracy of the existing ballastless track subgrade service performance evaluation method.
Description
Technical Field
The invention relates to the technical field of railway monitoring, in particular to a method and a system for evaluating service performance of a ballastless track subgrade of a high-speed railway.
Background
Along with the rapid development of track traffic construction in China, the ballastless track structure is widely applied to track traffic construction, especially high-speed railways, due to the advantages of low maintenance quantity, high stability, strong durability and the like, and the service safety of the track structure is important due to the characteristics of high train running speed, long running mileage, large traffic quantity and the like of the high-speed railways. However, the geometric adjustment capability of the track structure is limited, the track structure is very sensitive to deformation of the lower roadbed structure, and because the distribution of the high-speed railway lines is linear, the track structure has the characteristics of wide span, uneven distribution of geological conditions, large difference of weather conditions and the like, and the high-speed railway lines have diseases such as partial surface area settlement, side slope sliding, uneven settlement of the roadbed, lateral extrusion, slurry pumping and the like under the actions of factors such as upper track load, running, rainfall, groundwater and the like after the roadbed is filled and compacted, so that a risk road section is caused for the safety of the upper track structure, and therefore, the track roadbed needs to be regularly monitored and evaluated for service performance to ensure that the roadbed is at a safety level. At present, the monitoring and evaluation process of the ballastless track roadbed needs to be manually carried out on site for data acquisition, the data acquisition process is time-consuming and labor-consuming, and in order to avoid the occurrence of monitoring blind areas, the performance data of each section of ballastless track roadbed needs to be correspondingly acquired, so that the efficiency is quite low, and moreover, the performance evaluation index is difficult to form a unified standard, and a reasonable, accurate and efficient evaluation system is lacked. Therefore, the existing ballastless track subgrade service performance evaluation method has the problems of low efficiency and low accuracy.
Disclosure of Invention
The invention provides a method and a system for evaluating the service performance of a ballastless track subgrade of a high-speed railway, which are used for solving the problems of low efficiency and low accuracy of the conventional method for evaluating the service performance of the ballastless track subgrade.
In order to achieve the above object, the present invention is realized by the following technical scheme:
in a first aspect, the invention provides a method for evaluating the service performance of a ballastless track subgrade of a high-speed railway, which is applied to a system for evaluating the service performance of the ballastless track subgrade of the high-speed railway, wherein the system comprises the following steps: an empty monitoring system, a rail-to-rail monitoring system, a ground monitoring system, and a car-to-car monitoring system, the method comprising:
acquiring basal plane data of ballastless track roadbed based on an empty monitoring system, determining patient data based on the basal plane data, and determining a patient interval based on the patient data;
acquiring steel rail data of a ballastless track based on a vehicle monitoring system, determining rail inspection failure data based on the steel rail data, and determining a track failure section based on the rail inspection failure data;
Dividing a risk section and a patient section according to the patient section and the poor track section, acquiring track dynamic response data of the risk section and the patient section through a track monitoring system, and acquiring roadbed static response and object state data of the risk section and the patient section through a ground monitoring system;
Constructing a sample data set based on the track dynamic response data and the roadbed static response and object state data, and carrying out weight calculation on the track dynamic response data and the roadbed static response and object state data to obtain weight coefficients corresponding to each index parameter;
And calculating an evaluation index coefficient of the service performance of the ballastless track subgrade based on the sample data set and the weight coefficient, and comparing the evaluation index coefficient with a preset evaluation threshold value to obtain a service performance evaluation result corresponding to the ballastless track subgrade.
Optionally, the acquiring the base surface data of the ballastless track subgrade based on the empty monitoring system, and determining the patient data based on the base surface data includes:
Acquiring surface data of the ballastless track subgrade through an empty monitoring system, and taking the surface data as basal plane data of the ballastless track subgrade;
and identifying a characteristic area with deformation on the ground surface in the basal plane data, and determining patient data in the basal plane data based on the characteristic area.
Optionally, the acquiring the rail data of the ballastless track based on the opposite vehicle monitoring system, determining the rail defect data based on the rail data, includes:
The method comprises the steps of obtaining steel rail data of a ballastless track through a vehicle monitoring system, and determining steel rail evaluation indexes through steel rail evaluation index calculation, wherein the steel rail data comprise: left track direction data, right track direction data, left high-low data, right high-low data, horizontal data, track gauge data and triangle pit data, wherein the calculation formula is shown as follows:
；
Wherein T 1 is a steel rail evaluation index, x ij is a random measurement value of 7 continuous sampling points of indexes in steel rail data, n is the sampling number of unit sections, and x i is the i-th index amplitude;
and comparing the steel rail evaluation index with a preset steel rail quality evaluation threshold, and determining the steel rail evaluation index as defective rail detection data when the steel rail evaluation index is smaller than or equal to the preset steel rail quality evaluation threshold.
Optionally, the dividing the risk section and the patient section according to the patient section and the poor track section includes:
dividing a union interval of the patient interval and the track defect interval into a risk section, and dividing an intersection of the patient interval and the track defect interval into a patient section.
Optionally, the acquiring, by the on-track monitoring system, the dynamic response data of the track of the risk section and the patient section, and the acquiring, by the on-track monitoring system, the static response and the physical state data of the roadbed of the risk section and the patient section, includes:
Acquiring track plate acceleration data, track plate dynamic strain data, interlayer gap data, support layer acceleration data, support layer dynamic strain data and plate bottom void data of the risk road section and the patient road section through a track monitoring system, and taking the data as track dynamic response data;
And obtaining roadbed acceleration data, roadbed dynamic strain data, roadbed differential settlement data, water content and compactness data of the risk road section and the patient road section through a ground monitoring system, and taking the data as roadbed static response and object state data.
Optionally, the calculating an evaluation index coefficient of the ballastless track subgrade service performance based on the sample data set and the weight coefficient includes:
performing comprehensive evaluation calculation on the data in the sample data set and the weight coefficient corresponding to the data to obtain an evaluation index coefficient corresponding to the roadbed service performance, wherein the calculation formula is shown as follows:
；
Wherein Q represents an evaluation index coefficient, w i represents a weight coefficient, and R i represents sample dataset data;
the weight coefficient acquisition steps are as follows:
building a BP neural network model, building an input layer neuron node of the neural network model based on the data quantity in the sample data set, and building an output layer of the BP neural network model;
And taking the data in the sample data set as input data of the BP neural network model, and taking output data of the BP neural network as a weight coefficient.
Optionally, comparing the evaluation index coefficient with a preset evaluation threshold to obtain a service performance evaluation result corresponding to the ballastless track subgrade, including:
Setting an evaluation threshold Q, wherein the evaluation threshold Q comprises a safety threshold, a good threshold, a poor threshold and a poor threshold from large to small;
Comparing the evaluation index coefficient with a preset evaluation threshold, when the evaluation index coefficient is smaller than or equal to a difference threshold, evaluating the service performance of the current roadbed as poor service performance, when the evaluation index coefficient is larger than the difference threshold and smaller than or equal to a poor threshold, evaluating the service performance of the current roadbed as poor service performance, when the evaluation index coefficient is larger than the poor threshold and smaller than or equal to a good threshold, evaluating the service performance of the current roadbed as good service performance, and when the evaluation index coefficient is larger than the good threshold and smaller than or equal to a safety threshold, evaluating the service performance of the current roadbed as safe service performance;
And taking the service performance with poor service performance, good service performance and safe service performance as a service performance evaluation result corresponding to the ballastless track roadbed.
Optionally, the method further comprises:
Acquiring track inspection data, track data and roadbed data of ballastless track roadbed in a risk road section and a patient road section, preprocessing, calling, analyzing data, analyzing numerical values and setting evaluation indexes to judge the health conditions of the risk road section and the patient road section;
The pretreatment comprises the following steps: performing noise reduction processing on the track inspection data, the track data and the roadbed data, removing error data and abnormal data, and obtaining reliable data;
the calling comprises the following steps: extracting the preprocessed track inspection data, track data and roadbed data;
The data analysis includes: taking the called data as sample data, constructing a sample data set, and calculating a weight coefficient to obtain evaluation index coefficients of the subgrade service performance of the ballastless track risk section and the patient section;
the numerical analysis includes: constructing a high-speed railway ballastless track roadbed numerical model, inputting actual working conditions as parameters into the numerical model for numerical analysis, performing multi-working condition simulation by taking local climate conditions and geological conditions as variables to obtain a numerical simulation data set, and performing weight coefficient calculation to obtain evaluation index coefficients for predicting the roadbed service performance of a ballastless track risk section and a diseased section;
and comparing the evaluation index coefficient obtained by the data analysis and the numerical analysis with a preset health condition threshold value to obtain a health condition evaluation result corresponding to the ballastless track subgrade.
Optionally, the method further comprises: repairing or compensating rigidity of the risk road section and the patient road section based on the health condition evaluation result;
the repairing or stiffness compensating step includes: according to the health condition evaluation result, the damaged part, the repair cost and the repair time are considered, and a structural repair or rigidity compensation scheme is formulated;
for the track plate and supporting layer structure, when the health condition is bad, repairing by adopting measures such as filling materials, repairing adhesives, bar planting and the like, or compensating rigidity by adopting measures such as adding rubber pads, spring pads and the like to the supporting layer, and when the health condition is bad, carrying out integral replacement, dismantling the original damaged structure and paving again;
for roadbed structures, when the health condition is poor or bad, a compensation layer, lateral grouting or reinforcement pile method is adopted between the roadbed and the track structure;
And periodically performing empty monitoring and vehicle monitoring on the non-patient road sections or the non-risk road sections.
In a second aspect, the embodiment of the application provides a high-speed railway ballastless track subgrade service performance evaluation system, which further comprises a processor and a memory;
A memory for storing a computer program;
a processor for implementing the method steps of any one of the first aspects when executing a program stored on a memory.
The beneficial effects are that:
according to the method for evaluating the service performance of the ballastless track subgrade of the high-speed railway, the empty monitoring system is used for determining the patient interval, the vehicle monitoring system is used for determining the track bad interval, the risk road section and the patient road section are divided according to the patient interval and the track bad interval, the track dynamic response data of the risk road section and the patient road section are obtained through the rail monitoring system, the static response and the object state data of the roadbed of the risk road section and the patient road section are obtained through the ground monitoring system, and the evaluation index of the service performance of the ballastless track subgrade can be calculated based on the track dynamic response data and the static response and the object state data of the roadbed, so that the service performance evaluation corresponding to the ballastless track subgrade can be obtained according to the evaluation index; compared with the traditional evaluation method, the ballastless track subgrade is divided into the risk road section and the patient road section, so that the service performance evaluation range is reduced from the whole ballastless track subgrade to the ballastless track subgrade in the risk road section and the patient road section, and the area with good service performance can be ignored when the service performance evaluation is carried out, so that sample data to be considered is reduced, the evaluation efficiency is improved, and the performance evaluation is more targeted; meanwhile, when performance evaluation is carried out on the risk road section and the patient road section, the track dynamic response data, the roadbed static response and the object state data are obtained, and the evaluation index of the service performance is calculated based on the track dynamic response data, the roadbed static response and the object state data, so that the accuracy of the service performance evaluation is improved.
Drawings
FIG. 1 is one of the flow charts of the high-speed railway ballastless track subgrade service performance evaluation method according to the preferred embodiment of the invention;
FIG. 2 is a second flowchart of a method for evaluating the service performance of a ballast-less rail bed of a high-speed railway according to a preferred embodiment of the present invention;
fig. 3 is a schematic structural diagram of a high-speed railway ballastless track subgrade service performance evaluation system provided by the preferred embodiment of the invention.
In the figure, 1, an empty monitoring system; 2. a rail alignment monitoring system; 2.1, a track plate acceleration monitoring element; 2.2, a supporting layer acceleration monitoring element; 2.3, a track plate dynamic strain monitoring element; 2.4, supporting the dynamic strain monitoring element of the layer; 2.5, a track plate and a supporting layer relative displacement monitoring element; 2.6, a monitoring element for the relative displacement between the supporting layer and the roadbed; 3. a ground monitoring system; 3.1, roadbed acceleration monitoring elements; 3.2, a roadbed dynamic strain monitoring element; 3.3, roadbed settlement monitoring elements; 3.4, a ground penetrating radar; 4. a vehicle monitoring system; 5. an acquisition module; 6. a visual interface; 7. a numerical simulation system.
Detailed Description
The following description of the present invention will be made clearly and fully, and it is apparent that the embodiments described are only some, but not all, of the embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Likewise, the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate a relative positional relationship, which changes accordingly when the absolute position of the object to be described changes.
Referring to fig. 1-3, an embodiment of the application provides a method for evaluating service performance of a ballastless track subgrade of a high-speed railway, which is applied to a system for evaluating service performance of a ballastless track subgrade of a high-speed railway, and the system comprises: an empty monitoring system 1, a rail-to-rail monitoring system 2, a ground monitoring system 3, and a car-to-car monitoring system 4, the method comprising:
Acquiring basal plane data of ballastless track roadbed based on an empty monitoring system 1, determining patient data based on the basal plane data, and determining a patient interval based on the patient data;
Acquiring steel rail data of a ballastless track based on a vehicle monitoring system 4, determining rail inspection failure data based on the steel rail data, and determining a rail failure section based on the rail inspection failure data;
Dividing a risk section and a patient section according to the patient section and the poor track section, acquiring track dynamic response data of the risk section and the patient section through a track monitoring system 2, and acquiring roadbed static response and object state data of the risk section and the patient section through a ground monitoring system 3;
Constructing a sample data set based on the track dynamic response data and the roadbed static response and object state data, and carrying out weight calculation on the track dynamic response data and the roadbed static response and object state data to obtain weight coefficients corresponding to each index parameter;
And calculating an evaluation index coefficient of the service performance of the ballastless track subgrade based on the sample data set and the weight coefficient, and comparing the evaluation index coefficient with a preset evaluation threshold value to obtain a service performance evaluation result corresponding to the ballastless track subgrade.
In the above embodiment, the air monitoring system 1 determines the patient section, the vehicle monitoring system 4 determines the track bad section, the risk section and the patient section are divided according to the patient section and the track bad section, the track dynamic response data of the risk section and the patient section are obtained by the vehicle monitoring system 2, the roadbed static response and the object state data of the risk section and the patient section are obtained by the ground monitoring system 3, and the evaluation index coefficient of the ballastless track roadbed service performance can be calculated based on the track dynamic response data and the roadbed static response and the object state data, so that the service performance evaluation result corresponding to the ballastless track roadbed is obtained according to the evaluation index coefficient; the method establishes the intelligent monitoring system for the ballastless track subgrade of the high-speed railway, has wide monitoring range, does not need personnel on-site management, can perform remote management and visual operation of monitoring data, and realizes intelligent evaluation and early warning of the service state of the subgrade.
Optionally, the acquiring the base surface data of the ballastless track subgrade based on the empty monitoring system 1 and determining the patient data based on the base surface data includes:
Acquiring surface data of the ballastless track subgrade through the empty monitoring system 1, and taking the surface data as basal plane data of the ballastless track subgrade;
and identifying a characteristic area with deformation on the ground surface in the basal plane data, and determining patient data in the basal plane data based on the characteristic area.
In the above embodiment, by laying the empty monitoring system 1, the empty monitoring system 1 utilizes the cooperative coordination of synthetic aperture radar interferometry (InSAR) and the Beidou satellite to realize the monitoring of the roadbed section 'surface' of the ballastless track of the high-speed railway, firstly, SBAS-InSAR monitoring is carried out based on the data of the medium-resolution SAR satellite, and the earth surface regional census is realized by utilizing the high-coherence target time sequence InSAR method, so that the characteristic region with earth surface deformation is primarily identified; then, paving Beidou/GNSS monitoring stations on the characteristic areas, constructing an empty roadbed monitoring network, and realizing high-precision satellite monitoring; and finally, acquiring the surface elevation, subsidence, side slope sliding, staggering and other patient data of the characteristic region based on a high-precision resolution SAR image combined with a high-coherence target time sequence InSAR method and a GNSS high-precision monitoring technology.
Optionally, the acquiring rail data of the ballastless track based on the paired vehicle monitoring system 4, determining rail inspection failure data based on the rail data, includes:
the steel rail data of the ballastless track is obtained through the train monitoring system 4, and the steel rail evaluation index is determined through the steel rail evaluation index calculation, wherein the steel rail data comprises: the calculation of the left rail direction data, the right rail direction data, the left high and low data, the right high and low data, the horizontal data, the track gauge data and the triangle pit data meets the following relation:
；
Wherein T 1 is a steel rail evaluation index, x ij is a random measurement value of 7 continuous sampling points of indexes in steel rail data, n is the sampling number of unit sections, and x i is the i-th index amplitude;
and comparing the steel rail evaluation index with a preset steel rail quality evaluation threshold, and determining the steel rail evaluation index as defective rail detection data when the steel rail evaluation index is smaller than or equal to the preset steel rail quality evaluation threshold.
In the above embodiment, by paving the opposite vehicle monitoring system 4, the opposite vehicle monitoring system 4 is a detection device for installing track smoothness on a track inspection vehicle or an operation truck, so as to realize "line" monitoring of the ballastless track of the high-speed railway, so as to obtain track inspection data of the quality of the rail of the ballastless track of the high-speed railway, and the method comprises the following steps: 7 track quality detection data including left track direction, right track direction, left height, right height, horizontal, track gauge and triangle pit, and finishing steel rail evaluation index calculation by the formula (1):
（1）
wherein: t1 is a steel rail evaluation index, x ij is a random measurement value of 7 index continuous sampling points in steel rail data, n is the sampling number of unit sections, and x i is an ith index amplitude;
Setting a steel rail quality evaluation threshold value [ T ] and judging the quality of the rail inspection data based on T 1 to [ T ].
Optionally, the dividing the risk section and the patient section according to the patient section and the poor track section includes:
dividing a union interval of the patient interval and the track defect interval into a risk section, and dividing an intersection of the patient interval and the track defect interval into a patient section.
In the above embodiment, the risk section is divided based on the intersection section of the patient data and the rail inspection defective data, and the intersection section is divided into the patient section. Then, a track acceleration monitoring element 2.1, a track plate surface mounting supporting layer acceleration monitoring element 2.2, a track plate and supporting layer surface mounting track plate dynamic strain monitoring element 2.3, a supporting layer surface mounting supporting layer dynamic strain monitoring element 2.4, a supporting layer surface mounting track plate and supporting layer relative displacement monitoring element 2.5, a supporting layer and roadbed relative displacement monitoring element 2.6, a roadbed acceleration monitoring element 3.1, a roadbed dynamic strain monitoring element 3.2, a roadbed top mounting roadbed settlement monitoring element 3.3 and a roadbed top mounting ground penetrating radar 3.4 are mounted on the track plate surface and the track plate surface of a patient road section, and the track monitoring system 2 and the ground penetrating monitoring system 3 are mounted; the patient road segments are laid out with a set of monitoring elements that are more closely spaced than the risk road segments.
Optionally, the acquiring the track dynamic response data of the risk section and the patient section by the track monitoring system 2 and the roadbed static response and the object state data of the risk section and the patient section by the track monitoring system 3 include:
Acquiring track plate acceleration data, track plate dynamic strain data, interlayer gap data, support layer acceleration data, support layer dynamic strain data and plate bottom void data of the risk road section and the patient road section by a track monitoring system 2, and taking the data as track dynamic response data;
The road bed acceleration data, road bed dynamic strain data, road bed differential settlement data, water content and compactness data of the risk road sections and the patient road sections are obtained through the ground monitoring system 3, and the data are used as road bed static response and object state data.
In the above embodiment, based on the on-track monitoring system 2, acquiring the dynamic response data values of the track structures of the risk road section and the patient road section includes: track plate acceleration data A 1, track plate dynamic strain data A 2, interlayer gap data A 3, support layer acceleration data B 1, support layer dynamic strain data B 2 and plate bottom void data B 3. Based on the ground monitoring system 3, obtain risk highway section, the dynamic response of disease highway section road bed structure and subsidence data value, include: roadbed acceleration data S 1, roadbed dynamic strain data S 2, roadbed differential settlement data S 3, water content S 4 and compactness data S 5.
The method is characterized in that the original data directly obtained by the monitoring element is collected based on the acquisition instrument, the router is used for carrying out wireless transmission on the original data of the monitoring element, and the data is collected and stored in the database.
At the same time, preprocessing the original data; The called data are used as sample data, a sample data set is constructed, parameter characteristics of each index are extracted to perform dimensionless processing, and equivalent values of each monitoring index are obtained: r 1 is the equivalent of T 1, R 2 is the equivalent of A 1, R 3 is an equivalent of A 2, R 4 is an equivalent of A 3, R 5 is an equivalent of B 1, R 6 is the equivalent of B 2, R 7 is the equivalent of B 3, R 8 is the equivalent of S 1, R 9 is the equivalent of S 2, R 10 is the equivalent of S 3, R 11 is the equivalent of S 4, r 12 is the S 5 equivalent value.
Constructing a sample dataset Ri={R1,R2,R3,R4,R5,R6,R7,R8,R9,R10,R11,R12}.
Optionally, the calculating an evaluation index coefficient of the ballastless track subgrade service performance based on the sample data set and the weight coefficient includes:
Performing comprehensive evaluation calculation on the data in the sample data set and the weight coefficient corresponding to the data to obtain an evaluation index coefficient corresponding to the roadbed service performance, wherein the calculation of the evaluation index coefficient meets the following relation:
；
Wherein Q represents an evaluation index coefficient, w i represents a weight coefficient, and R i represents sample dataset data;
the weight coefficient acquisition steps are as follows:
building a BP neural network model, building an input layer neuron node of the neural network model based on the data quantity in the sample data set, and building an output layer of the BP neural network model;
And taking the data in the sample data set as input data of the BP neural network model, and taking output data of the BP neural network as a weight coefficient.
In the above embodiment, weight calculation is performed on each index parameter to obtain a weight coefficient w i; and fusing the sample data sets according to the formula (2) to form a comprehensive evaluation index Q of the subgrade service performance,
（2）
Where Q represents an evaluation index coefficient, w i represents a weight coefficient, and R i represents sample dataset data.
In an embodiment, a weight coefficient corresponding to data in a sample data set is calculated by using a BP deep traffic network, including:
(1) Constructing an input layer, and determining the neuron nodes of the input layer according to the number of sample data sets, wherein the number is 12;
(2) Constructing a hidden layer, namely constructing a hidden layer, wherein the number of hidden layer nodes is set to be 4;
(3) Constructing an output layer, wherein the output layer is set as a node and is used for outputting a calculation weight result;
(4) Constructing a loss function, wherein the loss function adopts a mean square error method, and adopts a random gradient descent method to carry out algorithm optimization;
(5) Iterative training, namely calculating an output value, and then back-propagating update weights to perform iterative calculation continuously;
(6) And outputting a final result.
Optionally, comparing the evaluation index coefficient with a preset evaluation threshold to obtain a service performance evaluation result corresponding to the ballastless track subgrade, including:
Setting an evaluation threshold Q, wherein the evaluation threshold Q comprises a safety threshold, a good threshold, a poor threshold and a poor threshold from large to small;
Comparing the evaluation index coefficient with a preset evaluation threshold, when the evaluation index coefficient is smaller than or equal to a difference threshold, evaluating the service performance of the current roadbed as poor service performance, when the evaluation index coefficient is larger than the difference threshold and smaller than or equal to a poor threshold, evaluating the service performance of the current roadbed as poor service performance, when the evaluation index coefficient is larger than the poor threshold and smaller than or equal to a good threshold, evaluating the service performance of the current roadbed as good service performance, and when the evaluation index coefficient is larger than the good threshold and smaller than or equal to a safety threshold, evaluating the service performance of the current roadbed as safe service performance;
And taking the service performance with poor service performance, good service performance and safe service performance as a service performance evaluation result corresponding to the ballastless track roadbed.
Optionally, the method further comprises:
Acquiring track inspection data, track data and roadbed data of ballastless track roadbed in a risk road section and a patient road section, preprocessing, calling, analyzing data, analyzing numerical values and setting evaluation indexes to judge the health conditions of the risk road section and the patient road section;
The pretreatment comprises the following steps: performing noise reduction processing on the track inspection data, the track data and the roadbed data, removing error data and abnormal data, and obtaining reliable data;
the calling comprises the following steps: extracting the preprocessed track inspection data, track data and roadbed data;
The data analysis includes: taking the called data as sample data, constructing a sample data set, and calculating a weight coefficient to obtain evaluation index coefficients of the subgrade service performance of the ballastless track risk section and the patient section;
the numerical analysis includes: constructing a high-speed railway ballastless track roadbed numerical model, inputting actual working conditions as parameters into the numerical model for numerical analysis, performing multi-working condition simulation by taking local climate conditions and geological conditions as variables to obtain a numerical simulation data set, and performing weight coefficient calculation to obtain evaluation index coefficients for predicting the roadbed service performance of a ballastless track risk section and a diseased section;
and comparing the evaluation index coefficient obtained by the data analysis and the numerical analysis with a preset health condition threshold value to obtain a health condition evaluation result corresponding to the ballastless track subgrade.
In the above embodiment, by evaluating the health condition of the ballastless track roadbed in the risk road section and the patient road section, the health condition of the risk road section and the patient road section can be accurately obtained, so that corresponding remedial measures can be made according to the health condition, and the track inspection data, the track data and the roadbed data can be collected through the collection module 5.
The evaluation of the health condition of the risk road section and the patient road section can be mainly performed by the rail inspection data, the rail data and the roadbed data of the ballastless track roadbed in the risk road section and the patient road section, the evaluation index coefficients of the service performance of the road bed of the risk road section and the patient road section in a real-time state can be obtained through data analysis after the rail inspection data, the rail data and the roadbed data are obtained, and the evaluation index coefficients of the service performance of the ballastless track risk road section and the roadbed of the patient road section, which are predicted under different climatic conditions and geological conditions, can be obtained through numerical analysis, and can be used for evaluating the health condition of the risk road section and the patient road section.
Both data analysis and numerical analysis may be accomplished by the numerical simulation system 7, with the visual interface 6 providing an operable platform for the numerical simulation system 7.
When the health condition evaluation is carried out on the risk road section and the patient road section, the health condition evaluation threshold M is set, the evaluation index coefficient obtained by data analysis and numerical analysis is compared with the preset health condition evaluation threshold from large to small in the evaluation threshold M, when the evaluation index coefficient is smaller than or equal to the difference threshold, the current road bed health condition is evaluated as poor in health condition, when the evaluation index coefficient is larger than or equal to the difference threshold and smaller than or equal to the poor threshold, the current road bed health condition is evaluated as poor in health condition, when the evaluation index coefficient is larger than or equal to the poor threshold and smaller than or equal to the good threshold, the current road bed health condition is evaluated as good in health condition, and when the evaluation index coefficient is larger than or equal to the good threshold and smaller than or equal to the health threshold.
Since there are problems in both the risk section and the patient section, only the risk section and the patient section are poor in health condition or poor in health condition when the health condition evaluation is performed.
Optionally, the method further comprises:
repairing or compensating rigidity of the risk road section and the patient road section based on the health condition evaluation result;
the repairing or stiffness compensating step includes: according to the health condition evaluation result, the damaged part, the repair cost and the repair time are considered, and a structural repair or rigidity compensation scheme is formulated;
When the health condition is bad, the original damaged structure is removed and paved again;
When the damaged part is of a roadbed structure and the health condition is poor or bad, a compensation layer, lateral grouting or reinforcement pile method is adopted between the roadbed and the track structure;
And periodically performing empty monitoring and vehicle monitoring on the non-patient road sections or the non-risk road sections.
The embodiment of the application also provides a system for evaluating the service performance of the ballastless track subgrade of the high-speed railway, which further comprises a processor and a memory;
A memory for storing a computer program;
a processor for implementing the method steps of any one of the first aspects when executing a program stored on a memory.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (9)
1. The method for evaluating the service performance of the ballastless track subgrade of the high-speed railway is applied to a system for evaluating the service performance of the ballastless track subgrade of the high-speed railway, and is characterized by comprising the following steps of: an empty monitoring system (1), a rail-to-rail monitoring system (2), a ground monitoring system (3) and a car-to-car monitoring system (4), the method comprising:
Acquiring basal plane data of a ballastless track roadbed based on an empty monitoring system (1), determining patient data based on the basal plane data, and determining a patient interval based on the patient data;
acquiring steel rail data of a ballastless track based on a vehicle monitoring system (4), determining rail inspection failure data based on the steel rail data, and determining a rail failure section based on the rail inspection failure data;
Dividing a risk section and a patient section according to the patient section and the poor track section, acquiring track dynamic response data of the risk section and the patient section through a track monitoring system (2), and acquiring roadbed static response and object state data of the risk section and the patient section through a ground monitoring system (3);
Constructing a sample data set based on the track dynamic response data and the roadbed static response and object state data, and carrying out weight calculation on the track dynamic response data and the roadbed static response and object state data to obtain weight coefficients corresponding to each index parameter;
Calculating an evaluation index coefficient of the service performance of the ballastless track subgrade based on the sample data set and the weight coefficient, and comparing the evaluation index coefficient with a preset evaluation threshold value to obtain a service performance evaluation result corresponding to the ballastless track subgrade;
The track dynamic response data of the risk road section and the patient road section are obtained through the track monitoring system (2), the roadbed static response and the object state data of the risk road section and the patient road section are obtained through the ground monitoring system (3), and the track dynamic response data comprises:
Acquiring track plate acceleration data, track plate dynamic strain data, interlayer gap data, support layer acceleration data, support layer dynamic strain data and plate bottom void data of the risk road section and the patient road section through a track monitoring system (2), and taking the data as track dynamic response data;
And obtaining roadbed acceleration data, roadbed dynamic strain data, roadbed differential settlement data, water content and compactness data of the risk road sections and the patient road sections through a ground monitoring system (3), and taking the data as roadbed static response and object state data.
2. The method for evaluating the service performance of the ballastless track subgrade of the high-speed railway according to claim 1, characterized in that said acquiring the base surface data of the ballastless track subgrade based on the empty monitoring system (1) and determining the patient data based on the base surface data comprises:
acquiring surface data of the ballastless track subgrade through an empty monitoring system (1), and taking the surface data as basal plane data of the ballastless track subgrade;
and identifying a characteristic area with deformation on the ground surface in the basal plane data, and determining patient data in the basal plane data based on the characteristic area.
3. The method for evaluating the service performance of the ballastless track subgrade of the high-speed railway according to claim 1, wherein said obtaining the rail data of the ballastless track based on the opposite car monitoring system (4) and determining the rail defect data based on the rail data comprises:
the method comprises the steps of obtaining steel rail data of a ballastless track through a vehicle monitoring system (4), and determining steel rail evaluation indexes through steel rail evaluation index calculation, wherein the steel rail data comprise: the calculation of the left rail direction data, the right rail direction data, the left high and low data, the right high and low data, the horizontal data, the track gauge data and the triangle pit data meets the following relation:
；
Wherein T 1 is a steel rail evaluation index, x ij is a random measurement value of 7 continuous sampling points of indexes in steel rail data, n is the sampling number of unit sections, and x i is the i-th index amplitude;
and comparing the steel rail evaluation index with a preset steel rail quality evaluation threshold, and determining the steel rail evaluation index as defective rail detection data when the steel rail evaluation index is smaller than or equal to the preset steel rail quality evaluation threshold.
4. The method for evaluating the service performance of the ballastless track subgrade of the high-speed railway according to claim 1, wherein said dividing the risk section and the disease section according to the disease section and the track defect section comprises:
dividing a union interval of the patient interval and the track defect interval into a risk section, and dividing an intersection of the patient interval and the track defect interval into a patient section.
5. The method for evaluating the service performance of the ballastless track subgrade of the high-speed railway according to claim 1, wherein said calculating the evaluation index coefficient of the service performance of the ballastless track subgrade based on the sample data set and the weight coefficient comprises:
Performing comprehensive evaluation calculation on the data in the sample data set and the weight coefficient corresponding to the data to obtain an evaluation index coefficient corresponding to the roadbed service performance, wherein the calculation of the evaluation index coefficient meets the following relation:
；
Wherein Q represents an evaluation index coefficient, w i represents a weight coefficient, and R i represents sample dataset data;
the weight coefficient acquisition steps are as follows:
building a BP neural network model, building an input layer neuron node of the neural network model based on the data quantity in the sample data set, and building an output layer of the BP neural network model;
And taking the data in the sample data set as input data of the BP neural network model, and taking output data of the BP neural network as a weight coefficient.
6. The method for evaluating the service performance of the ballastless track subgrade of the high-speed railway according to claim 1, wherein the step of comparing the evaluation index coefficient with a preset evaluation threshold value to obtain the service performance evaluation result corresponding to the ballastless track subgrade comprises the following steps:
Setting an evaluation threshold Q, wherein the evaluation threshold Q comprises a safety threshold, a good threshold, a poor threshold and a poor threshold from large to small;
Comparing the evaluation index coefficient with a preset evaluation threshold, when the evaluation index coefficient is smaller than or equal to a difference threshold, evaluating the service performance of the current roadbed as poor service performance, when the evaluation index coefficient is larger than the difference threshold and smaller than or equal to a poor threshold, evaluating the service performance of the current roadbed as poor service performance, when the evaluation index coefficient is larger than the poor threshold and smaller than or equal to a good threshold, evaluating the service performance of the current roadbed as good service performance, and when the evaluation index coefficient is larger than the good threshold and smaller than or equal to a safety threshold, evaluating the service performance of the current roadbed as safe service performance;
And taking the service performance with poor service performance, good service performance and safe service performance as a service performance evaluation result corresponding to the ballastless track roadbed.
7. The method for evaluating the service performance of the ballastless track subgrade of the high-speed railway according to claim 1, further comprising:
Acquiring track inspection data, track data and roadbed data of ballastless track roadbed in a risk road section and a patient road section, preprocessing, calling, analyzing data, analyzing numerical values and setting evaluation indexes to judge the health conditions of the risk road section and the patient road section;
The pretreatment comprises the following steps: performing noise reduction processing on the track inspection data, the track data and the roadbed data, removing error data and abnormal data, and obtaining reliable data;
the calling comprises the following steps: extracting the preprocessed track inspection data, track data and roadbed data;
The data analysis includes: taking the called data as sample data, constructing a sample data set, and calculating a weight coefficient to obtain evaluation index coefficients of the subgrade service performance of the ballastless track risk section and the patient section;
the numerical analysis includes: constructing a high-speed railway ballastless track roadbed numerical model, inputting actual working conditions as parameters into the numerical model for numerical analysis, performing multi-working condition simulation by taking local climate conditions and geological conditions as variables to obtain a numerical simulation data set, and performing weight coefficient calculation to obtain evaluation index coefficients for predicting the roadbed service performance of a ballastless track risk section and a diseased section;
and comparing the evaluation index coefficient obtained by the data analysis and the numerical analysis with a preset health condition threshold value to obtain a health condition evaluation result corresponding to the ballastless track subgrade.
8. The method for evaluating the service performance of the ballastless track subgrade of the high-speed railway according to claim 7, further comprising: repairing or compensating rigidity of the risk road section and the patient road section based on the health condition evaluation result;
the repairing or stiffness compensating step includes: according to the health condition evaluation result, the damaged part, the repair cost and the repair time are considered, and a structural repair or rigidity compensation scheme is formulated;
when the health condition is bad, the original damaged structure is removed and paved again;
When the damaged part is of a roadbed structure and the health condition is poor or bad, a compensation layer, lateral grouting or reinforcement pile method is adopted between the roadbed and the track structure;
And periodically performing empty monitoring and vehicle monitoring on the non-patient road sections or the non-risk road sections.
9. The high-speed railway ballastless track subgrade service performance evaluation system is characterized by further comprising a processor and a memory;
A memory for storing a computer program;
a processor for implementing the method steps of any one of claims 1-8 when executing a program stored on a memory.
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| CN110949452B (en) * | 2020-02-24 | 2020-09-08 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Dynamic verification system and detection method for high-speed railway roadbed |
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| CN107389790A (en) * | 2017-06-13 | 2017-11-24 | 同济大学 | The visual test device of roadbed dither under a kind of plane strain condition |
| CN110685194A (en) * | 2019-12-09 | 2020-01-14 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Dynamic evaluation method for high-speed railway subgrade |
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