CN112712302A - Roadbed compaction parameter adjusting method, device and equipment and readable storage medium - Google Patents

Roadbed compaction parameter adjusting method, device and equipment and readable storage medium Download PDF

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CN112712302A
CN112712302A CN202110282047.1A CN202110282047A CN112712302A CN 112712302 A CN112712302 A CN 112712302A CN 202110282047 A CN202110282047 A CN 202110282047A CN 112712302 A CN112712302 A CN 112712302A
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杨长卫
岳茂
张良
童心豪
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Southwest Jiaotong University
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Abstract

The invention provides a roadbed compaction parameter adjusting method, a roadbed compaction parameter adjusting device, roadbed compaction parameter adjusting equipment and a readable storage medium, wherein the method comprises the following steps: acquiring a first actual vibration parameter of the vibratory roller and a first actual soil body parameter of the roadbed under the current compaction pass; obtaining a first actual compaction energy index of the roadbed based on first actual acceleration data of the vibratory roller; constructing a GRNN neural network model; obtaining vibration parameters of the vibratory roller for carrying out real-time prediction on the next compaction and predicted soil parameters of the roadbed, and obtaining a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model; and obtaining a first vibration parameter to be adjusted based on the first compaction energy index predicted value of the roadbed so as to guide the vibratory roller to adjust the vibration parameter in real time during the next compaction. The invention can obtain the CEV value by quickly processing the data, further judge how to compact the roadbed, and is beneficial to guiding the following construction.

Description

Roadbed compaction parameter adjusting method, device and equipment and readable storage medium
Technical Field
The invention relates to the technical field of railway engineering, in particular to a method, a device and equipment for adjusting roadbed compaction parameters and a readable storage medium.
Background
In the construction process of a high-speed railway, the operation safety of a high-speed train is influenced by the quality of the compaction quality of a roadbed, and the compaction and the quality control of roadbed filling engineering are the keys for ensuring the compaction quality of the roadbed. With the development of scientific technology, the detection of the Compaction quality of the roadbed gradually develops from the traditional detection method to Continuous Compaction Control (CCC). The continuous compaction control technology can reduce the defects caused by the traditional detection method to a certain extent, but the technology is not yet completely mature, so the technology is not widely applied to actual field work at present; on the other hand, in the field work of roadbed compaction, the technology does not realize complete intelligence, and after feedback of the system to the compaction real condition is obtained, the vibration parameters (exciting force, frequency, compaction pass and the like) of the road roller need to be selected through the human brain.
Disclosure of Invention
The invention aims to provide a roadbed compaction parameter adjusting method, a roadbed compaction parameter adjusting device, roadbed compaction parameter adjusting equipment and a readable storage medium, so that the problems are solved.
In order to achieve the above object, the embodiments of the present application provide the following technical solutions:
in one aspect, an embodiment of the present application provides a method for adjusting a subgrade compaction parameter, where the method includes:
step S1, acquiring a first actual vibration parameter of the vibratory roller and a first actual soil parameter of the roadbed under the current compaction pass, wherein the first actual vibration parameter of the vibratory roller comprises a first actual compaction pass of the vibratory roller, first actual acceleration data of the vibratory roller, a first actual frequency of the vibratory roller and a first actual excitation force of the vibratory roller, and the first actual soil parameter of the roadbed comprises a first actual settlement amount of the roadbed and a first actual curvature coefficient of roadbed filler grading;
step S2, obtaining a first actual compaction energy index of the roadbed based on the first actual acceleration data of the vibratory roller;
step S3, obtaining a first actual compaction energy index of the roadbed, a first actual vibration parameter of the vibratory roller and a first actual soil body parameter of the roadbed by using a GRNN neural network model to obtain a constructed GRNN neural network model;
step S4, obtaining vibration parameters of the vibratory roller for carrying out next-time compaction real-time prediction and predicted soil parameters of the roadbed, and obtaining a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model;
and step S5, obtaining a first vibration parameter to be adjusted based on the first compaction energy index predicted value of the roadbed so as to guide the vibratory roller to adjust the vibration parameter in real time for the next compaction.
Optionally, the step S2 includes:
performing empirical mode decomposition on first actual acceleration data of the vibratory roller to obtain a decomposition amount, and performing Hilbert-yellow transformation on the decomposition amount to obtain a Hilbert amplitude spectrum;
performing time integration on the Hilbert amplitude spectrum to obtain a marginal spectrum curve of the vibration signal;
and summing the marginal spectrum curves of the vibration signals to obtain the compaction energy index of the roadbed.
Optionally, the step S3 includes:
extracting the maximum value in the first actual acceleration data of the vibratory roller to obtain the acceleration peak value of the vibratory roller;
normalizing the first actual compaction energy index of the roadbed, the first actual compaction pass of the vibratory roller, the acceleration peak value of the vibratory roller, the first actual frequency of the vibratory roller, the first actual exciting force of the vibratory roller and the first actual soil body parameter of the roadbed to obtain processed data;
acquiring a parameter set value of a GRNN neural network model, wherein parameters of the GRNN neural network model comprise smoothing factors of the GRNN neural network model, and acquiring the set GRNN neural network model;
and acquiring the processed data by utilizing the set GRNN neural network model to obtain the constructed GRNN neural network model.
Optionally, the step S4 includes:
acquiring the compaction pass of the vibratory roller for carrying out real-time prediction on the next time of compaction, the predicted acceleration data of the vibratory roller, the predicted frequency of the vibratory roller and the predicted exciting force of the vibratory roller;
extracting the maximum value in the predicted acceleration data of the vibratory roller to obtain the predicted acceleration peak value of the vibratory roller;
obtaining a fitting curve of the curvature coefficient of the roadbed filler relative to the compaction pass;
obtaining a predicted soil body parameter of the subgrade based on the predicted compaction pass of the vibratory roller and a fitting curve of the curvature coefficient of the subgrade filler relative to the compaction pass, wherein the predicted soil body parameter of the subgrade comprises a predicted subgrade settlement amount and a predicted grading curvature coefficient of the subgrade filler;
and obtaining a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model based on the predicted compaction pass number of the vibratory roller, the predicted acceleration peak value of the vibratory roller, the predicted frequency of the vibratory roller, the predicted exciting force of the vibratory roller and the predicted soil body parameters of the roadbed.
Optionally, the step S5 includes:
obtaining a compaction energy index standard value, and comparing a first compaction energy index predicted value of the roadbed with the compaction energy index standard value to obtain a first vibration parameter needing to be adjusted;
and sending the first vibration parameter to be adjusted to guide the vibratory roller to adjust the vibration parameter in real time for the next pressing.
Optionally, after step S5, the method further includes:
acquiring a second actual vibration parameter of the vibratory roller and a second actual soil parameter of the roadbed in real time for the next time of compaction, wherein the second actual vibration parameter of the vibratory roller comprises a second actual compaction pass of the vibratory roller, second actual acceleration data of the vibratory roller, a second actual frequency of the vibratory roller and a second actual excitation force of the vibratory roller, and the second actual soil parameter of the roadbed comprises a second actual settlement amount of the roadbed and a second actual curvature coefficient of roadbed filler grading;
obtaining a second actual compaction energy index of the roadbed based on second actual acceleration data of the vibratory roller;
updating the constructed GRNN neural network model based on a second actual compaction energy index of the roadbed, a second actual vibration parameter of the vibratory roller and a second actual soil parameter of the roadbed to obtain an updated GRNN neural network model;
obtaining vibration parameters of the vibratory roller for the next pressing and real-time prediction and soil parameters of the roadbed for the next pressing and real-time prediction, and obtaining a second compaction energy index prediction value of the roadbed by using the updated GRNN neural network model;
and obtaining a second vibration parameter to be adjusted based on the second compaction energy index predicted value of the roadbed so as to guide the vibratory roller to perform the adjustment of the vibration parameter in real time for the next compaction.
In a second aspect, the present application provides a roadbed compaction parameter adjusting device, which includes: the device comprises a first acquisition module, a first calculation module, a construction module, a second acquisition module and a second calculation module.
The first obtaining module is used for obtaining a first actual vibration parameter of the vibratory roller and a first actual soil parameter of the roadbed under the current compaction pass, the first actual vibration parameter of the vibratory roller comprises a first actual compaction pass of the vibratory roller, first actual acceleration data of the vibratory roller, a first actual frequency of the vibratory roller and a first actual excitation force of the vibratory roller, and the first actual soil parameter of the roadbed comprises a first actual settlement amount of the roadbed and a first actual curvature coefficient of roadbed filler grading;
the first calculation module is used for obtaining a first actual compaction energy index of the roadbed based on first actual acceleration data of the vibratory roller;
the building module is used for obtaining a first actual compaction energy index of the roadbed, a first actual vibration parameter of the vibratory roller and a first actual soil body parameter of the roadbed by using a GRNN neural network model to obtain a built GRNN neural network model;
the second acquisition module is used for acquiring vibration parameters of the vibratory roller for performing next-time compaction real-time prediction and predicted soil parameters of the roadbed, and acquiring a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model;
and the second calculation module is used for obtaining a first vibration parameter to be adjusted based on the first compaction energy index predicted value of the roadbed so as to guide the vibratory roller to adjust the vibration parameter in real time for the next compaction.
Optionally, the first computing module includes:
the decomposition unit is used for carrying out empirical mode decomposition on the first actual acceleration data of the vibratory roller to obtain a decomposition amount, and carrying out Hilbert-yellow transformation on the decomposition amount to obtain a Hilbert amplitude spectrum;
the integration unit is used for performing time integration on the Hilbert amplitude spectrum to obtain a marginal spectrum curve of the vibration signal;
and the summing unit is used for summing the marginal spectrum curve of the vibration signal to obtain the compaction energy index of the roadbed.
Optionally, the building module includes:
the first extraction unit is used for extracting the maximum value in the first actual acceleration data of the vibratory roller to obtain the acceleration peak value of the vibratory roller;
the processing unit is used for carrying out normalization processing on a first actual compaction energy index of the roadbed, a first actual compaction pass of the vibratory roller, an acceleration peak value of the vibratory roller, a first actual frequency of the vibratory roller, a first actual exciting force of the vibratory roller and a first actual soil body parameter of the roadbed to obtain processed data;
the device comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring a parameter set value of a GRNN neural network model, and parameters of the GRNN neural network model comprise smoothing factors of the GRNN neural network model to obtain the set GRNN neural network model;
and the first computing unit is used for acquiring the processed data by utilizing the set GRNN neural network model to obtain the constructed GRNN neural network model.
Optionally, the second obtaining module includes:
the second acquisition unit is used for acquiring the compaction pass number of the vibratory roller for carrying out next-time compaction real-time prediction, the predicted acceleration data of the vibratory roller, the predicted frequency of the vibratory roller and the predicted exciting force of the vibratory roller;
the second extraction unit is used for extracting the maximum value in the predicted acceleration data of the vibratory roller to obtain the predicted acceleration peak value of the vibratory roller;
the third acquisition unit is used for acquiring a fitting curve of the curvature coefficient of the roadbed filler relative to the compaction pass;
the second calculation unit is used for obtaining predicted soil body parameters of the roadbed based on the predicted compaction pass of the vibratory roller and a fitting curve of the curvature coefficient of the roadbed filler relative to the compaction pass, wherein the predicted soil body parameters of the roadbed comprise predicted roadbed settlement and predicted grading curvature coefficient of the roadbed filler;
and the third calculation unit is used for obtaining a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model based on the predicted compaction pass of the vibratory roller, the predicted acceleration peak value of the vibratory roller, the predicted frequency of the vibratory roller, the predicted exciting force of the vibratory roller and the predicted soil body parameters of the roadbed.
Optionally, the second computing module includes:
the fourth obtaining unit is used for obtaining a compaction energy index standard value, comparing the first compaction energy index predicted value of the roadbed with the compaction energy index standard value, and obtaining a first vibration parameter needing to be adjusted;
and the sending unit is used for sending the first vibration parameter to be adjusted so as to guide the vibratory roller to adjust the vibration parameter in real time for the next pressing.
Optionally, the apparatus further comprises:
a third obtaining module, configured to obtain a second actual vibration parameter of the vibratory roller and a second actual soil parameter of the roadbed in real time, where the second actual vibration parameter of the vibratory roller includes a second actual compaction pass of the vibratory roller, second actual acceleration data of the vibratory roller, a second actual frequency of the vibratory roller, and a second actual excitation force of the vibratory roller, and the second actual soil parameter of the roadbed includes a second actual settlement amount of the roadbed and a second actual curvature coefficient of roadbed filler grading;
the third calculation module is used for obtaining a second actual compaction energy index of the roadbed based on second actual acceleration data of the vibratory roller;
the updating module is used for updating the constructed GRNN neural network model based on a second actual compaction energy index of the roadbed, a second actual vibration parameter of the vibratory roller and a second actual soil body parameter of the roadbed to obtain an updated GRNN neural network model;
the fourth obtaining module is used for obtaining vibration parameters of the vibratory roller for the next pressing real-time prediction and soil body parameters of the roadbed for the next pressing real-time prediction, and obtaining a second compaction energy index predicted value of the roadbed by using the updated GRNN neural network model;
and the fourth calculation module is used for obtaining a second vibration parameter to be adjusted based on the second compaction energy index predicted value of the roadbed so as to guide the vibratory roller to perform the adjustment of the vibration parameter in real time for the next compaction.
In a third aspect, embodiments of the present application provide a subgrade compaction parameter adjustment apparatus that includes a memory and a processor. The memory is used for storing a computer program; the processor is used for realizing the steps of the roadbed compaction parameter adjusting method when executing the computer program.
In a fourth aspect, the present disclosure provides a readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the roadbed compaction parameter adjusting method.
The invention has the beneficial effects that:
1. through the GRNN-CEV index prediction system constructed by the method, data obtained after field compaction are put into the prediction system for training, and then the vibration parameters of the vibratory roller, such as compaction pass, exciting force and the like, are changed, so that a simulated numerical value can be obtained. The compaction condition of each area can be known by comparing the predicted value with the specified value, so that the selection of the vibration parameters is more clear, the following construction can be guided, the site construction time is saved, and the corresponding manufacturing cost is reduced.
2. Compared with the prior continuous compaction technology, the method is more intelligent after the GRNN neural network model is added; through the construction of a GRNN-CEV index prediction system, the CEV numerical value can be obtained through the rapid processing of data, and then the compaction quality of the roadbed can be judged.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a method for adjusting subgrade compaction parameters according to an embodiment of the invention;
fig. 2 is a schematic structural view of a roadbed compaction parameter adjusting device in the embodiment of the invention;
fig. 3 is a schematic structural diagram of a roadbed compaction parameter adjusting device in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers or letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1
As shown in fig. 1, the present embodiment provides a roadbed compaction quality assessment method, which includes step S1, step S2, step S3, step S4 and step S5.
Step S1, acquiring a first actual vibration parameter of the vibratory roller and a first actual soil parameter of the roadbed under the current compaction pass, wherein the first actual vibration parameter of the vibratory roller comprises a first actual compaction pass of the vibratory roller, first actual acceleration data of the vibratory roller, a first actual frequency of the vibratory roller and a first actual excitation force of the vibratory roller, and the first actual soil parameter of the roadbed comprises a first actual settlement amount of the roadbed and a first actual curvature coefficient of roadbed filler grading;
step S2, obtaining a first actual compaction energy index of the roadbed based on the first actual acceleration data of the vibratory roller;
step S3, obtaining a first actual compaction energy index of the roadbed, a first actual vibration parameter of the vibratory roller and a first actual soil body parameter of the roadbed by using a GRNN neural network model to obtain a constructed GRNN neural network model;
step S4, obtaining vibration parameters of the vibratory roller for carrying out next-time compaction real-time prediction and predicted soil parameters of the roadbed, and obtaining a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model;
and step S5, obtaining a first vibration parameter to be adjusted based on the first compaction energy index predicted value of the roadbed so as to guide the vibratory roller to adjust the vibration parameter in real time for the next compaction.
In a construction site, the aim of enabling the roadbed compaction quality to reach the compaction index is mainly achieved by changing the vibration parameters of the vibratory roller. Too much or too little change in the vibration parameters directly affects the compaction quality of the subgrade. It is time consuming to measure the compaction index after each compaction and the current feedback of data from each compaction pass is followed by the human brain to select the vibration parameters for the next compaction. In order to intelligently select the high-speed railway foundation compaction vibration parameters, the data obtained after the field is compacted for several times is put into a prediction system for training through the embodiment, and then the vibration parameters of the vibratory roller, such as the compaction times, the exciting force and the like, are changed, so that the simulated numerical value can be obtained. The compaction condition of each area can be known by comparing the predicted value with the standard value, and the selection of the vibration parameters is clearer, so that the following construction is guided.
In a specific embodiment of the present disclosure, the step S2 may further include a step S21, a step S22 and a step S23.
Step S21, performing empirical mode decomposition on first actual acceleration data of the vibratory roller to obtain a decomposition amount, and performing Hilbert-yellow transformation on the decomposition amount to obtain a Hilbert amplitude spectrum;
step S22, performing time integration on the Hilbert amplitude spectrum to obtain a marginal spectrum curve of the vibration signal;
and step S23, summing the marginal spectrum curves of the vibration signals to obtain the compaction energy index of the roadbed.
In a specific embodiment of the present disclosure, the step S3 may further include a step S31, a step S32, a step S33, and a step S34.
Step S31, extracting the maximum value in the first actual acceleration data of the vibratory roller to obtain the acceleration peak value of the vibratory roller;
step S32, carrying out normalization processing on the first actual compaction energy index of the roadbed, the first actual compaction pass of the vibratory roller, the acceleration peak value of the vibratory roller, the first actual frequency of the vibratory roller, the first actual exciting force of the vibratory roller and the first actual soil body parameter of the roadbed to obtain processed data;
step S33, obtaining a parameter set value of a GRNN neural network model, wherein the parameters of the GRNN neural network model comprise a smoothing factor of the GRNN neural network model, and obtaining the set GRNN neural network model;
and step S34, acquiring the processed data by using the set GRNN neural network model to obtain the constructed GRNN neural network model.
In this embodiment, the smoothing factor of the GRNN neural network model is set to 0.1, and meanwhile, the processed data is put into the GRNN neural network model in this embodiment, so that the model is constructed.
In a specific embodiment of the present disclosure, the step S4 may further include a step S41, a step S42, a step S43, a step S44, and a step S45.
Step S41, acquiring the compaction pass number of the vibratory roller for carrying out next-time compaction real-time prediction, the predicted acceleration data of the vibratory roller, the predicted frequency of the vibratory roller and the predicted exciting force of the vibratory roller;
step S42, extracting the maximum value in the predicted acceleration data of the vibratory roller to obtain the predicted acceleration peak value of the vibratory roller;
s43, obtaining a fitting curve of the curvature coefficient of the roadbed filling material relative to the compaction pass;
step S44, obtaining predicted soil parameters of the subgrade based on the predicted compaction pass of the vibratory roller and a fitting curve of the curvature coefficient of the subgrade filler relative to the compaction pass, wherein the predicted soil parameters of the subgrade comprise predicted subgrade settlement and predicted grading curvature coefficient of the subgrade filler;
and step S45, obtaining a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model based on the predicted compaction pass of the vibratory roller, the predicted acceleration peak value of the vibratory roller, the predicted frequency of the vibratory roller, the predicted exciting force of the vibratory roller and the predicted soil body parameters of the roadbed.
Compared with the prior continuous compaction technology, the method is more intelligent after the GRNN neural network model is added; through the construction of a GRNN-CEV index prediction system, the CEV numerical value can be obtained through the rapid processing of data, and then the compaction quality of the roadbed can be judged.
In a specific embodiment of the present disclosure, the step S5 may further include a step S51 and a step S52.
Step S51, obtaining a compaction energy index standard value, and comparing the first compaction energy index predicted value of the roadbed with the compaction energy index standard value to obtain a first vibration parameter needing to be adjusted;
and step S52, sending the first vibration parameter to be adjusted to guide the vibratory roller to adjust the vibration parameter in real time for the next pressing.
In the embodiment, the compaction condition of each area can be known by comparing the predicted value with the specified value, so that the selection of the vibration parameters is clearer, the following construction can be guided, the site construction time is saved, and the corresponding manufacturing cost is reduced.
In a specific embodiment of the present disclosure, after the step S5, the method may further include a step S6, a step S7, a step S8, a step S9, and a step S10.
Step S6, obtaining a second actual vibration parameter of the vibratory roller for the next time of compaction and a second actual soil parameter of the roadbed, wherein the second actual vibration parameter of the vibratory roller comprises a second actual compaction pass of the vibratory roller, second actual acceleration data of the vibratory roller, a second actual frequency of the vibratory roller and a second actual excitation force of the vibratory roller, and the second actual soil parameter of the roadbed comprises a second actual settlement amount of the roadbed and a second actual curvature coefficient of roadbed filler grading;
step S7, obtaining a second actual compaction energy index of the roadbed based on second actual acceleration data of the vibratory roller;
step S8, updating the constructed GRNN neural network model based on a second actual compaction energy index of the roadbed, a second actual vibration parameter of the vibratory roller and a second actual soil body parameter of the roadbed to obtain an updated GRNN neural network model;
step S9, obtaining vibration parameters of the vibratory roller for the next pressing real-time prediction and soil parameters of the roadbed for the next pressing real-time prediction, and obtaining a second compaction energy index prediction value of the roadbed by using the updated GRNN neural network model;
and step S10, obtaining a second vibration parameter to be adjusted based on the second compaction energy index predicted value of the roadbed so as to guide the vibratory roller to adjust the vibration parameter in real time for the next compaction.
In this embodiment, the relevant data after the next compaction is obtained, and then the GRNN neural network model is updated, so that the accuracy of model prediction can be improved.
Under the specific construction condition, according to the construction sequence of a site, firstly, data obtained after first compaction is put into the model, the data for second compaction is predicted after the model is built, the predicted data is compared with a standard CEV value after being obtained, how the compaction condition of each area of the roadbed can be known through comparison, and then parameters are changed through the predicted compaction condition; and after the second compaction, putting the data after the second compaction into the model to update the model, and then predicting until the construction is finished.
Example 2
As shown in fig. 2, the present embodiment provides a roadbed compaction parameter adjusting device, which comprises: a first obtaining module 701, a first calculating module 702, a constructing module 703, a second obtaining module 704 and a second calculating module 705.
The first obtaining module 701 is configured to obtain a first actual vibration parameter of the vibratory roller and a first actual soil parameter of the roadbed at the current compaction pass, where the first actual vibration parameter of the vibratory roller includes a first actual compaction pass of the vibratory roller, first actual acceleration data of the vibratory roller, a first actual frequency of the vibratory roller, and a first actual excitation force of the vibratory roller, and the first actual soil parameter of the roadbed includes a first actual settlement amount of the roadbed and a first actual curvature coefficient of roadbed filler grading;
the first calculating module 702 is configured to obtain a first actual compaction energy index of the roadbed based on the first actual acceleration data of the vibratory roller;
the building module 703 is configured to obtain a first actual compaction energy index of the roadbed, a first actual vibration parameter of the vibratory roller, and a first actual soil parameter of the roadbed by using a GRNN neural network model, so as to obtain a built GRNN neural network model;
the second obtaining module 704 is configured to obtain vibration parameters of the vibratory roller for performing next compaction real-time prediction and predicted soil parameters of the roadbed, and obtain a first compaction energy index predicted value of the roadbed by using the constructed GRNN neural network model;
the second calculating module 705 is configured to obtain a first vibration parameter to be adjusted based on the first compaction energy index predicted value of the roadbed, so as to guide the vibratory roller to perform adjustment on the vibration parameter in real time for the next compaction.
At present, the roadbed continuous compaction control technology does not realize complete intellectualization, and the following vibration parameters are determined through thinking of human brain after related parameters are fed back through compaction; the currently used compaction detection indexes are many, different evaluation ranges exist for different compaction indexes facing the same working condition, and the method is also a problem for the compaction index detection method; meanwhile, if the compaction condition of the roadbed needs to be judged after compaction, a lot of time is needed for measurement, and the construction progress is delayed.
In the embodiment, a CEV index prediction system is established through a GRNN neural network model, and the intelligent adjustment of the high-speed railway base compression parameters can be realized. Establishing a relation between a vibration parameter, a soil body parameter and a CEV index in a GRNN neural network model through data of a field test; after each compaction, the data obtained after the compaction is input, the network model is continuously updated, and then the compaction condition under the following working condition is simulated by changing the vibration parameters and the soil body parameters.
In a specific embodiment of the present disclosure, the first calculation module 702 includes a decomposition unit 7021, an integration unit 7022, and a summation unit 7023.
The decomposition unit 7021 is configured to perform empirical mode decomposition on the first actual acceleration data of the vibratory roller to obtain a decomposition amount, and perform Hilbert-yellow transform on the decomposition amount to obtain a Hilbert amplitude spectrum;
the integrating unit 7022 is configured to perform time integration on the Hilbert amplitude spectrum to obtain a marginal spectrum curve of the vibration signal;
and the summing unit 7023 is configured to sum the marginal spectrum curve of the vibration signal to obtain a compaction energy index of the roadbed.
In a specific embodiment of the present disclosure, the building module 703 includes a first extracting unit 7031, a processing unit 7032, a first obtaining unit 7033, and a first calculating unit 7034.
The first extracting unit 7031 is configured to extract a maximum value in the first actual acceleration data of the vibratory roller, so as to obtain an acceleration peak value of the vibratory roller;
the processing unit 7032 is configured to perform normalization processing on the first actual compaction energy index of the roadbed, the first actual compaction pass of the vibratory roller, the acceleration peak of the vibratory roller, the first actual frequency of the vibratory roller, the first actual exciting force of the vibratory roller, and the first actual soil body parameter of the roadbed to obtain processed data;
the first obtaining unit 7033 is configured to obtain parameter setting values of a GRNN neural network model, where the parameters of the GRNN neural network model include smoothing factors of the GRNN neural network model, so as to obtain a set GRNN neural network model;
the first computing unit 7034 is configured to obtain the processed data by using the set GRNN neural network model, so as to obtain a constructed GRNN neural network model.
In a specific embodiment of the present disclosure, the second obtaining module 704 includes a second obtaining unit 7041, a second extracting unit 7042, a third obtaining unit 7043, a second calculating unit 7044, and a third calculating unit 7045.
The second obtaining unit 7041 is configured to obtain the compaction pass number of the vibratory roller, the predicted acceleration data of the vibratory roller, the predicted frequency of the vibratory roller, and the predicted excitation force of the vibratory roller, which are used for performing the next compaction real-time prediction;
the second extracting unit 7042 is configured to extract a maximum value in the predicted acceleration data of the vibratory roller to obtain a predicted acceleration peak value of the vibratory roller;
the third obtaining unit 7043 is configured to obtain a fitted curve of the curvature coefficient of the roadbed filler with respect to the compaction pass;
the second calculating unit 7044 is configured to obtain predicted soil parameters of the roadbed based on the predicted compaction pass of the vibratory roller and a fitting curve of the curvature coefficient of the roadbed filler with respect to the compaction pass, where the predicted soil parameters of the roadbed include a predicted roadbed settlement amount and a predicted grading curvature coefficient of the roadbed filler;
and the third calculating unit 7045 is configured to obtain a first compaction energy index predicted value of the roadbed by using the constructed GRNN neural network model based on the predicted compaction pass of the vibratory roller, the predicted acceleration peak value of the vibratory roller, the predicted frequency of the vibratory roller, the predicted exciting force of the vibratory roller, and the predicted soil body parameter of the roadbed.
In a specific embodiment of the present disclosure, the second calculating module 705 includes a fourth obtaining unit 7051 and a sending unit 7052.
The fourth obtaining unit 7051 is configured to obtain a standard compaction energy index value, and compare the first predicted compaction energy index value of the roadbed with the standard compaction energy index value to obtain a first vibration parameter to be adjusted;
the sending unit 7052 is configured to send the first vibration parameter to be adjusted, so as to instruct the vibratory roller to adjust the vibration parameter in real time for the next pressing.
In a specific embodiment of the present disclosure, the apparatus further includes a third obtaining module 706, a third calculating module 707, an updating module 708, a fourth obtaining module 709, and a fourth calculating module 710.
The third obtaining module 706 is configured to obtain a second actual vibration parameter of the vibratory roller for the next time of compaction and a second actual soil parameter of the roadbed, where the second actual vibration parameter of the vibratory roller includes a second actual compaction pass of the vibratory roller, second actual acceleration data of the vibratory roller, a second actual frequency of the vibratory roller, and a second actual excitation force of the vibratory roller, and the second actual soil parameter of the roadbed includes a second actual settlement amount of the roadbed and a second actual curvature coefficient of roadbed filler grading;
the third calculating module 707 is configured to obtain a second actual compaction energy index of the roadbed based on second actual acceleration data of the vibratory roller;
the updating module 708 is configured to update the constructed GRNN neural network model based on a second actual compaction energy index of the roadbed, a second actual vibration parameter of the vibratory roller, and a second actual soil parameter of the roadbed to obtain an updated GRNN neural network model;
the fourth obtaining module 709 is configured to obtain vibration parameters of the vibratory roller for the next pressing real-time prediction and soil parameters of the roadbed for the next pressing real-time prediction, and obtain a second compaction energy index prediction value of the roadbed by using the updated GRNN neural network model;
the fourth calculating module 710 is configured to obtain a second vibration parameter that needs to be adjusted based on the second compaction energy index predicted value of the roadbed, so as to guide the vibratory roller to perform adjustment of the vibration parameter in real time for the next compaction.
It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3
Corresponding to the above method embodiment, the embodiment of the present disclosure further provides a roadbed compaction parameter adjusting device, and a roadbed compaction parameter adjusting device described below and a roadbed compaction parameter adjusting method described above may be referred to in correspondence with each other.
Fig. 3 is a block diagram illustrating a subgrade compaction parameter adjustment apparatus 800 in accordance with an exemplary embodiment. As shown in fig. 3, the subgrade compaction parameter adjustment apparatus 800 may include: a processor 801, a memory 802. The subgrade compaction parameter adjustment apparatus 800 may also include one or more of a multimedia component 803, an input/output (I/O) interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the subgrade compaction parameter adjustment apparatus 800 to perform all or part of the steps of the subgrade compaction parameter adjustment method. The memory 802 is used to store various types of data to support the operation of the subgrade compaction parameter adjustment apparatus 800, which may include, for example, instructions for any application or method operating on the subgrade compaction parameter adjustment apparatus 800, as well as application-related data such as contact data, transceived messages, pictures, audio, video, and so forth. The Memory 802 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 803 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 802 or transmitted through the communication component 805. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the subgrade compaction parameter adjustment device 800 and other devices. Wireless communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding communication component 805 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the subgrade compaction parameter adjustment Device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the subgrade compaction parameter adjustment methods described above.
In another exemplary embodiment, a computer readable storage medium comprising program instructions that, when executed by a processor, implement the steps of the subgrade compaction parameter adjustment method described above is also provided. For example, the computer readable storage medium may be the memory 802 described above including program instructions that may be executed by the processor 801 of the subgrade compaction parameter adjustment apparatus 800 to implement the subgrade compaction parameter adjustment method described above.
Example 4
Corresponding to the above method embodiment, the disclosed embodiment also provides a readable storage medium, and a readable storage medium described below and a roadbed compaction parameter adjusting method described above can be correspondingly referred to.
A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the subgrade compaction parameter adjustment method according to the above-mentioned method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A roadbed compaction parameter adjusting method is characterized by comprising the following steps:
step S1, acquiring a first actual vibration parameter of the vibratory roller and a first actual soil parameter of the roadbed under the current compaction pass, wherein the first actual vibration parameter of the vibratory roller comprises a first actual compaction pass of the vibratory roller, first actual acceleration data of the vibratory roller, a first actual frequency of the vibratory roller and a first actual excitation force of the vibratory roller, and the first actual soil parameter of the roadbed comprises a first actual settlement amount of the roadbed and a first actual curvature coefficient of roadbed filler grading;
step S2, obtaining a first actual compaction energy index of the roadbed based on the first actual acceleration data of the vibratory roller;
step S3, obtaining a first actual compaction energy index of the roadbed, a first actual vibration parameter of the vibratory roller and a first actual soil body parameter of the roadbed by using a GRNN neural network model to obtain a constructed GRNN neural network model;
step S4, obtaining vibration parameters of the vibratory roller for carrying out next-time compaction real-time prediction and predicted soil parameters of the roadbed, and obtaining a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model;
and step S5, obtaining a first vibration parameter to be adjusted based on the first compaction energy index predicted value of the roadbed so as to guide the vibratory roller to adjust the vibration parameter in real time for the next compaction.
2. The subgrade compaction parameter adjustment method according to claim 1, wherein the step S2 comprises:
performing empirical mode decomposition on first actual acceleration data of the vibratory roller to obtain a decomposition amount, and performing Hilbert-yellow transformation on the decomposition amount to obtain a Hilbert amplitude spectrum;
performing time integration on the Hilbert amplitude spectrum to obtain a marginal spectrum curve of the vibration signal;
and summing the marginal spectrum curves of the vibration signals to obtain the compaction energy index of the roadbed.
3. The subgrade compaction parameter adjustment method according to claim 1, wherein the step S3 comprises:
extracting the maximum value in the first actual acceleration data of the vibratory roller to obtain the acceleration peak value of the vibratory roller;
normalizing the first actual compaction energy index of the roadbed, the first actual compaction pass of the vibratory roller, the acceleration peak value of the vibratory roller, the first actual frequency of the vibratory roller, the first actual exciting force of the vibratory roller and the first actual soil body parameter of the roadbed to obtain processed data;
acquiring a parameter set value of a GRNN neural network model, wherein parameters of the GRNN neural network model comprise smoothing factors of the GRNN neural network model, and acquiring the set GRNN neural network model;
and acquiring the processed data by utilizing the set GRNN neural network model to obtain the constructed GRNN neural network model.
4. The subgrade compaction parameter adjustment method according to claim 1, wherein the step S4 comprises:
acquiring the compaction pass of the vibratory roller for carrying out real-time prediction on the next time of compaction, the predicted acceleration data of the vibratory roller, the predicted frequency of the vibratory roller and the predicted exciting force of the vibratory roller;
extracting the maximum value in the predicted acceleration data of the vibratory roller to obtain the predicted acceleration peak value of the vibratory roller;
obtaining a fitting curve of the curvature coefficient of the roadbed filler relative to the compaction pass;
obtaining a predicted soil body parameter of the subgrade based on the predicted compaction pass of the vibratory roller and a fitting curve of the curvature coefficient of the subgrade filler relative to the compaction pass, wherein the predicted soil body parameter of the subgrade comprises a predicted subgrade settlement amount and a predicted grading curvature coefficient of the subgrade filler;
and obtaining a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model based on the predicted compaction pass number of the vibratory roller, the predicted acceleration peak value of the vibratory roller, the predicted frequency of the vibratory roller, the predicted exciting force of the vibratory roller and the predicted soil body parameters of the roadbed.
5. A subgrade compaction parameter adjustment device, comprising:
the first obtaining module is used for obtaining a first actual vibration parameter of the vibratory roller and a first actual soil parameter of a roadbed under the current compaction pass, the first actual vibration parameter of the vibratory roller comprises a first actual compaction pass of the vibratory roller, first actual acceleration data of the vibratory roller, a first actual frequency of the vibratory roller and a first actual excitation force of the vibratory roller, and the first actual soil parameter of the roadbed comprises a first actual settlement amount of the roadbed and a first actual curvature coefficient of roadbed filler grading;
the first calculation module is used for obtaining a first actual compaction energy index of the roadbed based on first actual acceleration data of the vibratory roller;
the construction module is used for acquiring a first actual compaction energy index of the roadbed, a first actual vibration parameter of the vibratory roller and a first actual soil body parameter of the roadbed by utilizing the GRNN neural network model to obtain a constructed GRNN neural network model;
the second acquisition module is used for acquiring vibration parameters of the vibratory roller for carrying out real-time prediction on next compaction and predicted soil parameters of the roadbed, and acquiring a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model;
and the second calculation module is used for obtaining a first vibration parameter to be adjusted based on the first compaction energy index predicted value of the roadbed so as to guide the vibratory roller to adjust the vibration parameter in real time during the next compaction.
6. The subgrade compaction parameter adjustment device according to claim 5, wherein the first calculation module comprises:
the decomposition unit is used for carrying out empirical mode decomposition on the first actual acceleration data of the vibratory roller to obtain a decomposition amount, and carrying out Hilbert-yellow transformation on the decomposition amount to obtain a Hilbert amplitude spectrum;
the integration unit is used for performing time integration on the Hilbert amplitude spectrum to obtain a marginal spectrum curve of the vibration signal;
and the summing unit is used for summing the marginal spectrum curve of the vibration signal to obtain the compaction energy index of the roadbed.
7. The subgrade compaction parameter adjustment device according to claim 5, wherein the building modules comprise:
the first extraction unit is used for extracting the maximum value in the first actual acceleration data of the vibratory roller to obtain the acceleration peak value of the vibratory roller;
the processing unit is used for carrying out normalization processing on a first actual compaction energy index of the roadbed, a first actual compaction pass of the vibratory roller, an acceleration peak value of the vibratory roller, a first actual frequency of the vibratory roller, a first actual exciting force of the vibratory roller and a first actual soil body parameter of the roadbed to obtain processed data;
the device comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring a parameter set value of a GRNN neural network model, and parameters of the GRNN neural network model comprise smoothing factors of the GRNN neural network model to obtain the set GRNN neural network model;
and the first computing unit is used for acquiring the processed data by utilizing the set GRNN neural network model to obtain the constructed GRNN neural network model.
8. The subgrade compaction parameter adjustment device according to claim 5, wherein the second acquisition module comprises:
the second acquisition unit is used for acquiring the compaction pass number of the vibratory roller for carrying out next-time compaction real-time prediction, the predicted acceleration data of the vibratory roller, the predicted frequency of the vibratory roller and the predicted exciting force of the vibratory roller;
the second extraction unit is used for extracting the maximum value in the predicted acceleration data of the vibratory roller to obtain the predicted acceleration peak value of the vibratory roller;
the third acquisition unit is used for acquiring a fitting curve of the curvature coefficient of the roadbed filler relative to the compaction pass;
the second calculation unit is used for obtaining predicted soil body parameters of the roadbed based on the predicted compaction pass of the vibratory roller and a fitting curve of the curvature coefficient of the roadbed filler relative to the compaction pass, wherein the predicted soil body parameters of the roadbed comprise predicted roadbed settlement and predicted grading curvature coefficient of the roadbed filler;
and the third calculation unit is used for obtaining a first compaction energy index predicted value of the roadbed by utilizing the constructed GRNN neural network model based on the predicted compaction pass of the vibratory roller, the predicted acceleration peak value of the vibratory roller, the predicted frequency of the vibratory roller, the predicted exciting force of the vibratory roller and the predicted soil body parameters of the roadbed.
9. A subgrade compaction parameter adjustment apparatus, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the subgrade compaction parameter adjustment method according to any one of claims 1 to 4 when executing said computer program.
10. A readable storage medium, characterized by: the readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the subgrade compaction parameter adjustment method according to any one of claims 1 to 4.
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