CN113283160B - Vibration prediction method for railway overhead line environment under influence of multiple random variables - Google Patents
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Abstract
The invention provides a vibration prediction method for a railway overhead line environment under the influence of multiple random variables. Comprising the following steps: analyzing the influence of the randomness of soil parameters on the vibration propagation and attenuation of the field soil; establishing a vehicle-line-bridge power interaction model based on the track irregularity random excitation; according to the influence of the randomness of soil parameters on the vibration propagation and attenuation of the field soil and a vehicle-line-bridge dynamic interaction model, a bridge-pile foundation-foundation soil dynamic interaction model is established, a vibration prediction system of a railway overhead line environment is established by means of a computer machine learning algorithm according to the model, and the vibration prediction system is utilized to predict the propagation and attenuation of the field vibration around the high-speed railway overhead bridge. The invention can provide a new thought for predicting, evaluating and preventing the environmental vibration of the overhead railway line, promote the development of the environmental vibration prediction to intellectualization and customization, and provide theoretical support and technical support for the planning of the overhead railway line and the vibration reduction and isolation design of surrounding structures of the high-speed railway.
Description
Technical Field
The invention relates to the technical field of vibration analysis of railway overhead line environments, in particular to a vibration prediction method of a railway overhead line environment under the influence of multiple random variables.
Background
Along with the continuous development of a large number of construction of overhead lines in high-speed railways and cities, the overhead lines are increasingly close to vibration sensitive points such as dense residential areas, cultural centers, ancient building groups, high-tech parks and the like in the cities, and the problem of surrounding environment vibration caused when trains run on bridges at higher speeds is increasingly remarkable. However, the existing prediction method of the surrounding environment vibration of the high-speed railway overhead line is used for performing fixed value prediction based on specific model parameters, and is difficult to reflect the influence of various random variables including the randomness of soil parameters on the environment vibration.
In the existing scheme, actually measured track irregularity data are used as input, wheel track dynamics response is used as output, a three-layer BP neural network model is used for mapping the wheel track relationship, and the three-layer BP neural network model is combined with a finite element Monte Carlo method for modeling, so that the influence of track irregularity on the reliability of ballastless track service is analyzed. However, the scheme is mainly focused on reliability analysis of the track structure, does not consider a large environmental vibration system of bridge structure-foundation-soil body, does not consider the influence of a plurality of random variables including random soil parameters, and does not intelligently predict the environmental vibration of the track traffic.
Although scholars at home and abroad develop a great deal of researches on a method for predicting the environmental vibration of the rail transit, the prior art has the following defects:
(1) The method can not explore the transmission and attenuation of the random nature of foundation soil parameters to the vibration of the field around the viaduct of the high-speed railway from the angles of wave theory and probability analysis;
(2) The influence of a plurality of random variables such as track irregularity random excitation, site soil parameter randomness and the like on environmental vibration cannot be comprehensively considered;
(3) The intelligent prediction of the environmental vibration cannot be performed through intelligent technologies such as big data analysis and the like, and a field response spectrum curve in the field of the environmental vibration cannot be generated.
(4) The calculation method in the prior art has low efficiency and low accuracy of calculation results.
Disclosure of Invention
The embodiment of the invention provides a vibration prediction method for a railway overhead line environment under the influence of multiple random variables, which aims to overcome the defects of the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
An intelligent prediction method for railway overhead line environmental vibration under the influence of multiple random variables comprises the following steps:
analyzing the influence of the randomness of soil parameters on the vibration propagation and attenuation of the field soil;
establishing a vehicle-line-bridge power interaction model based on the track irregularity random excitation;
establishing a bridge-pile group foundation-foundation soil dynamic interaction model according to the influence of the randomness of the soil parameters on the vibration propagation and attenuation of the field soil and the vehicle-line-bridge dynamic interaction model; and constructing a vibration prediction system of the railway overhead line environment by means of a computer machine learning algorithm according to the bridge-pile foundation-foundation soil dynamic interaction model, and predicting the propagation and attenuation of the vibration of the field around the high-speed railway overhead bridge by using the vibration prediction system.
Preferably, said analyzing the effect of randomness of soil parameters on the propagation and attenuation of vibrations of the surface of the earth comprises:
inputting field soil dynamic parameter prior probability sample data, establishing a forward theory of surface waves based on a thin layer method with an ideal matching layer, performing multi-channel surface wave test of field soil, extracting a test dispersion curve of layered field soil to obtain a field soil test attenuation curve, inverting a posterior random probability distribution model of the field soil parameters according to the Bayesian theory and a Monte Carlo-Markov chain algorithm to obtain a posterior probability distribution model of the field soil parameters;
combining the posterior probability distribution model of the field soil parameters with the field soil theoretical analysis model, calculating to obtain a random vibration sample space of the field soil, further obtaining a probability density function of a random vibration transfer function of the field soil by using nuclear density estimation, obtaining a random vibration interval estimation of the field with certain confidence, and outputting posterior probability sample data of the field soil kinetic parameters, sample data of the field vibration transfer function and interval estimation.
Preferably, the building of the vehicle-line-bridge dynamic interaction model based on the random excitation of the track irregularity comprises the following steps:
establishing a train-track-bridge power interaction model by using vehicle parameters, track parameters, bridge parameters and track irregularity power spectrums, and performing random vibration analysis on an axle system under random excitation of track irregularity by using the train-track-bridge power interaction model to obtain the mean value, variance, power spectrums and sample data of wheel-track interaction forces between a train and a track;
and taking wheel-rail interaction force sample data calculated by the train-rail-bridge dynamic interaction model as input, establishing a girder-support-pier dynamic interaction model, and calculating the mean value, variance, power spectrum and sample data of the bridge support dynamic reaction force by adopting a frequency domain finite element method.
Preferably, said establishing a bridge-pile foundation-foundation soil dynamic interaction model based on said effect of randomness of soil parameters on field soil surface vibration propagation and attenuation and said vehicle-line-bridge dynamic interaction model comprises:
and establishing a bearing platform-pile group foundation-foundation soil dynamic interaction model according to posterior probability sample data of the site soil dynamic parameters, sample data and interval estimation of the site vibration transfer function and the mean value, variance, power spectrum and sample data of the bridge support dynamic reaction force, simulating foundation soil by adopting a thin layer method with an ideal matching layer, realizing dynamic interaction of the pile group and the foundation soil by adopting a volumetric method, respectively obtaining an impedance function of the pile group, a soil shear modulus, a soil shear wave velocity, and environmental vibration interval estimation under the influence of a single random variable and a double random variable of a material damping ratio, and analyzing an environmental vibration threshold value.
And optimizing and improving the bearing platform-pile group foundation-foundation soil dynamic interaction model through test data, and outputting vibration response sample data of the field around the overhead line and vibration level sample data of the environment around the overhead line.
Preferably, the method for predicting the propagation and attenuation of the vibration of the field around the high-speed railway viaduct by using the vibration prediction system is constructed by means of a computer machine learning algorithm according to the bridge-pile foundation-foundation soil dynamic interaction model, and comprises the following steps:
according to the bridge-pile foundation-foundation soil dynamic interaction model, an elevated high-speed railway surrounding environment vibration sample library is established through probability prediction model theoretical data and site vibration test data, posterior probability sample data of foundation soil dynamic parameters, site prominent period sample data, site vibration response sample data around an elevated line, vibration level sample data of site vibration surrounding environment and site test data are used as input data, displacement, speed, acceleration, vibration level and environment vibration site reaction spectrum of site vibration are used as output data, large data analysis is carried out by means of a computer machine learning algorithm, and test and training of the model are continuously carried out, so that a vibration prediction system is established;
performing interval estimation and threshold analysis on the environmental vibration of the field around the viaduct of the high-speed railway by using the vibration prediction system by means of a machine learning algorithm to obtain an intelligent environmental vibration prediction model based on the machine learning algorithm;
and taking the maximum value of each response of the environmental vibration as an output variable, and generating a vibration field response spectrum curve of the surrounding environment of the elevated high-speed railway through statistical analysis.
According to the technical scheme provided by the embodiment of the invention, the key problems can be completely solved, a new thought can be provided for predicting, evaluating and preventing the environmental vibration of the overhead railway line, the environmental vibration prediction is promoted to develop to intelligence and customization, and theoretical support and technical support can be provided for planning the overhead railway line and vibration reduction and isolation design of surrounding structures of the high-speed railway conveniently and rapidly.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an implementation of a vibration prediction method for a railway overhead line environment under the influence of multiple random variables according to an embodiment of the present invention;
FIG. 2 is a schematic diagram for discussing the influence of a plurality of random soil parameters on the vibration propagation and attenuation of the soil in a field according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of a vibration prediction system for constructing a railway overhead line environment by means of a computer machine learning algorithm according to an embodiment of the present invention;
fig. 4 is a schematic diagram of predicting propagation and attenuation of vibration in a field around a viaduct of a high-speed railway by using the vibration prediction system according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
The processing flow of the vibration prediction method of the railway overhead line environment under the influence of multiple random variables provided by the embodiment of the invention is shown in a figure 1, and the method comprises the following processing steps:
and S10, analyzing the influence of randomness of soil parameters on the vibration propagation and attenuation of the field soil.
A schematic diagram for discussing the influence of a plurality of soil parameter random variables on the field soil vibration propagation and attenuation is provided in the embodiment of the invention as shown in fig. 2.
Firstly, inputting field soil movement parameter prior probability sample data, and establishing a forward theory of surface waves based on a thin layer method (TLM-PML) with an ideal matching layer; secondly, carrying out multi-channel surface wave testing on the field soil, extracting a test dispersion curve of layered field soil, and correcting a half-power bandwidth method according to test conditions to obtain an accurate field soil test attenuation curve; and then inverting the posterior random probability distribution model of the field soil parameters according to the Bayesian theory and the Monte Carlo-Markov chain Metropolis-Hastings algorithm to obtain the posterior probability distribution model of the field soil parameters.
And further combining the posterior probability distribution model of the site soil parameters with a site soil theoretical analysis model, and calculating to obtain a random vibration sample space of the site soil. And then, obtaining a probability density function of the random vibration transfer function of the field soil by using the nuclear density estimation to obtain the random vibration interval estimation of the field with certain confidence. And discussing the influence of a plurality of random variables of soil parameters on the vibration propagation and attenuation of the soil surface of the field, and outputting posterior probability sample data of the soil parameters of the field, sample data of the field vibration transfer function and interval estimation.
And step S20, building a vehicle-line-bridge power interaction model based on the track irregularity random excitation.
And establishing a train-track-bridge power interaction model by using the vehicle parameters, the track parameters, the bridge parameters and the track irregularity power spectrum, and carrying out random vibration analysis on an axle system under random excitation of the track irregularity by using the model to acquire statistics such as a mean value, a variance, a power spectrum and the like of wheel-track interaction forces between the train and the track.
And taking wheel-rail interaction force sample data calculated by the train-rail-bridge dynamic interaction model as input, establishing a girder-support-pier dynamic interaction model, and calculating statistics such as a mean value, variance, power spectrum, sample data and the like of the bridge support dynamic reaction force by adopting a frequency domain finite element method.
And step S30, establishing a bridge-pile group foundation-foundation soil dynamic interaction model according to the influence of the randomness of the soil parameters on the vibration propagation and attenuation of the soil surface of the field and the vehicle-line-bridge dynamic interaction model. And (3) establishing a bearing platform-pile group foundation-foundation soil dynamic interaction model according to the posterior probability sample data of the site soil dynamic parameters, the sample data and the interval of the site vibration transfer function obtained in the step (S10) and the mean value, variance, power spectrum and sample data of the bridge support dynamic counter force obtained in the step (S20), wherein foundation soil is simulated by adopting a thin layer method (PML-TLM) with an ideal matching layer, and the dynamic interaction between the pile group and the foundation soil is realized by adopting a volumetric method. The input parameters of the pile cap-pile group foundation-foundation soil dynamic interaction model are not deterministic soil parameters, but independent samples in the posterior probability model are used for respectively obtaining the impedance function of the pile group, the shear modulus of soil, the shear wave velocity of the soil, and the environmental vibration interval estimation under the influence of a single random variable and a double random variable of the material damping ratio, and analyzing the environmental vibration threshold.
And continuously optimizing and improving the pile cap-pile foundation-foundation soil dynamic interaction model through test data, outputting field vibration response sample data around the overhead line and vibration level sample data of vibration around the overhead line, so as to reasonably realize probability prediction of vibration around the overhead high-speed railway in theory.
And S40, constructing a vibration prediction system of the railway overhead line environment by means of a computer machine learning algorithm according to the bridge-pile foundation-foundation soil dynamic interaction model, and predicting the propagation and attenuation of the vibration of the field around the high-speed railway overhead bridge by using the vibration prediction system.
According to the bridge-pile foundation-foundation soil dynamic interaction model, an elevated high-speed railway surrounding environment vibration sample library is established through probability prediction model theoretical data and site vibration test data, posterior probability sample data of foundation soil dynamic parameters, site prominent period sample data, site vibration response sample data around an elevated line, vibration level sample data of site vibration around the elevated line and site test data are used as input data, displacement, speed, acceleration, vibration level, environment vibration site reaction spectrum and the like of site vibration are used as output data, large data analysis is carried out by means of a computer machine learning algorithm, model testing and training are carried out continuously, and a vibration prediction system with accuracy higher than that of traditional environment vibration prediction is constructed as shown in fig. 3.
Fig. 4 is a schematic diagram of predicting propagation and attenuation of vibration in a field around a viaduct of a high-speed railway by using the vibration prediction system according to an embodiment of the present invention. And carrying out interval estimation and threshold analysis on the environmental vibration by means of a machine learning algorithm and the vibration prediction system to obtain an intelligent environmental vibration prediction model based on the machine learning algorithm.
And then, taking the maximum value of each response of the environmental vibration as an output variable, and generating a field response spectrum curve of the environmental vibration around the elevated high-speed railway through statistical analysis.
In conclusion, the embodiment of the invention not only can provide a new thought for predicting, evaluating and preventing the environmental vibration of the overhead railway line and promote the development of the environmental vibration prediction to intellectualization and customization, but also can provide theoretical support and technical support for planning the overhead railway line and reducing vibration isolation design of surrounding structures of the high-speed railway.
According to the invention, the propagation and attenuation of the random nature of foundation soil parameters to the vibration of the field around the viaduct of the high-speed railway can be explored from the angles of fluctuation theory and probability analysis; the influence of a plurality of random variables including track irregularity random excitation, site soil parameter randomness and the like on environmental vibration can be comprehensively considered.
According to the invention, intelligent prediction is carried out on the environmental vibration through intelligent technologies such as big data analysis and the like, and a field response spectrum curve in the field of the environmental vibration can be further generated to guide the planning of the high-speed railway overhead line and the vibration reduction and isolation design and planning of surrounding buildings. The invention utilizes the computer data mining technology and the intelligent algorithm, has high calculation efficiency and accurate calculation result.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
From the above description of embodiments, it will be apparent to those skilled in the art that the present invention may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (4)
1. An intelligent prediction method for railway overhead line environmental vibration under the influence of multiple random variables is characterized by comprising the following steps:
analyzing the influence of the randomness of soil parameters on the vibration propagation and attenuation of the field soil;
establishing a vehicle-line-bridge power interaction model based on the track irregularity random excitation;
establishing a bridge-pile group foundation-foundation soil dynamic interaction model according to the influence of the randomness of the soil parameters on the vibration propagation and attenuation of the field soil and the vehicle-line-bridge dynamic interaction model; constructing a vibration prediction system of a railway overhead line environment by means of a computer machine learning algorithm according to the bridge-pile foundation-foundation soil dynamic interaction model, and predicting propagation and attenuation of field vibration around the high-speed railway overhead bridge by using the vibration prediction system;
analyzing the influence of the randomness of soil parameters on the vibration propagation and attenuation of the field soil, wherein the method comprises the following steps:
inputting field soil dynamic parameter prior probability sample data, establishing a forward theory of surface waves based on a thin layer method with an ideal matching layer, performing multi-channel surface wave test of field soil, extracting a test dispersion curve of layered field soil to obtain a field soil test attenuation curve, inverting a posterior random probability distribution model of the field soil parameters according to the Bayesian theory and a Monte Carlo-Markov chain algorithm to obtain a posterior probability distribution model of the field soil parameters;
combining the posterior probability distribution model of the field soil parameters with the field soil theoretical analysis model, calculating to obtain a random vibration sample space of the field soil, further obtaining a probability density function of a random vibration transfer function of the field soil by using nuclear density estimation, obtaining a random vibration interval estimation of the field with certain confidence, and outputting posterior probability sample data of the field soil kinetic parameters, sample data of the field vibration transfer function and interval estimation.
2. The method of claim 1, wherein the creating a vehicle-line-bridge dynamic interaction model based on the random excitation of track irregularities comprises:
establishing a train-track-bridge power interaction model by using vehicle parameters, track parameters, bridge parameters and track irregularity power spectrums, and performing random vibration analysis on an axle system under random excitation of track irregularity by using the train-track-bridge power interaction model to obtain the mean value, variance, power spectrums and sample data of wheel-track interaction forces between a train and a track;
and taking wheel-rail interaction force sample data calculated by the train-rail-bridge dynamic interaction model as input, establishing a girder-support-pier dynamic interaction model, and calculating the mean value, variance, power spectrum and sample data of the bridge support dynamic reaction force by adopting a frequency domain finite element method.
3. The method of claim 2, wherein said establishing a bridge-pile foundation-foundation soil dynamic interaction model based on said effect of randomness of said soil parameters on field soil surface vibration propagation and attenuation and said vehicle-line-bridge dynamic interaction model comprises:
establishing a bearing platform-pile group foundation-foundation soil dynamic interaction model according to posterior probability sample data of the site soil dynamic parameters, sample data and interval estimation of a site vibration transfer function and mean value, variance, power spectrum and sample data of the bridge support dynamic reaction force, simulating foundation soil by adopting a thin layer method with an ideal matching layer, realizing dynamic interaction of the pile group and the foundation soil by adopting a volumetric method, respectively obtaining an impedance function of the pile group, a soil shear modulus, a soil shear wave velocity, and environmental vibration interval estimation under the influence of a single random variable and a double random variable of a material damping ratio, and analyzing an environmental vibration threshold;
and optimizing and improving the bearing platform-pile group foundation-foundation soil dynamic interaction model through test data, and outputting vibration response sample data of the field around the overhead line and vibration level sample data of the environment around the overhead line.
4. A method according to claim 3, wherein said constructing a vibration prediction system of a railway overhead line environment from said bridge-pile foundation-foundation soil dynamic interaction model by means of a computer machine learning algorithm, and predicting propagation and attenuation of site vibrations around a high-speed railway overhead bridge by using said vibration prediction system comprises:
according to the bridge-pile foundation-foundation soil dynamic interaction model, an elevated high-speed railway surrounding environment vibration sample library is established through probability prediction model theoretical data and site vibration test data, posterior probability sample data of foundation soil dynamic parameters, site prominent period sample data, site vibration response sample data around an elevated line, vibration level sample data of site vibration surrounding environment and site test data are used as input data, displacement, speed, acceleration, vibration level and environment vibration site reaction spectrum of site vibration are used as output data, large data analysis is carried out by means of a computer machine learning algorithm, and test and training of the model are continuously carried out, so that a vibration prediction system is established;
performing interval estimation and threshold analysis on the environmental vibration of the field around the viaduct of the high-speed railway by using the vibration prediction system by means of a machine learning algorithm to obtain an intelligent environmental vibration prediction model based on the machine learning algorithm;
and taking the maximum value of each response of the environmental vibration as an output variable, and generating a vibration field response spectrum curve of the surrounding environment of the elevated high-speed railway through statistical analysis.
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| CN103150458A (en) * | 2013-04-01 | 2013-06-12 | 中南大学 | Car-track-bridge-foundation coupling system and dynamic analysis method thereof |
| CN108052958A (en) * | 2017-11-09 | 2018-05-18 | 同济大学 | Consider based on known excitation and simultaneously Bayes's modal identification method of environmental excitation influence |
| CN109214123A (en) * | 2018-10-18 | 2019-01-15 | 大连海事大学 | It is a kind of that a Longitudinal vibration analysis method is held based on saturation the floating of loosened soil stake model |
| CN111553115A (en) * | 2020-04-10 | 2020-08-18 | 东南大学 | Large-span bridge vibration response prediction method under typhoon action based on data driving |
| JP2020166819A (en) * | 2019-12-24 | 2020-10-08 | ポート・アンド・アンカー株式会社 | Method and system for creating model to discriminate state of structure |
| WO2021056977A1 (en) * | 2019-09-24 | 2021-04-01 | 重庆美的通用制冷设备有限公司 | Pipeline vibration control method, computer device, storage medium and pipeline system |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103150458A (en) * | 2013-04-01 | 2013-06-12 | 中南大学 | Car-track-bridge-foundation coupling system and dynamic analysis method thereof |
| CN108052958A (en) * | 2017-11-09 | 2018-05-18 | 同济大学 | Consider based on known excitation and simultaneously Bayes's modal identification method of environmental excitation influence |
| CN109214123A (en) * | 2018-10-18 | 2019-01-15 | 大连海事大学 | It is a kind of that a Longitudinal vibration analysis method is held based on saturation the floating of loosened soil stake model |
| WO2021056977A1 (en) * | 2019-09-24 | 2021-04-01 | 重庆美的通用制冷设备有限公司 | Pipeline vibration control method, computer device, storage medium and pipeline system |
| JP2020166819A (en) * | 2019-12-24 | 2020-10-08 | ポート・アンド・アンカー株式会社 | Method and system for creating model to discriminate state of structure |
| CN111553115A (en) * | 2020-04-10 | 2020-08-18 | 东南大学 | Large-span bridge vibration response prediction method under typhoon action based on data driving |
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