CN110334393B - Rail transit environment vibration prediction method, prediction system and vibration reduction measure evaluation method - Google Patents
Rail transit environment vibration prediction method, prediction system and vibration reduction measure evaluation method Download PDFInfo
- Publication number
- CN110334393B CN110334393B CN201910440909.1A CN201910440909A CN110334393B CN 110334393 B CN110334393 B CN 110334393B CN 201910440909 A CN201910440909 A CN 201910440909A CN 110334393 B CN110334393 B CN 110334393B
- Authority
- CN
- China
- Prior art keywords
- vibration
- signal
- model
- source
- finite element
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Geometry (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Automation & Control Theory (AREA)
- Architecture (AREA)
- Civil Engineering (AREA)
- Structural Engineering (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of rail transit environment vibration prediction, in particular to a rail transit environment vibration prediction method, a prediction system and a vibration reduction measure evaluation method. The prediction method comprises the following steps: establishing a dynamic finite element model of a project site and inputting a simulated vibration source signal to respectively obtain model reaction signals of at least two vibration positions; respectively processing each group of analog data through frequency spectrum analysis to respectively obtain a plurality of groups of vibration transfer functions; actually measuring an actual vibration signal of any vibration position and inputting the actual vibration signal into any group of vibration transfer functions to invert an equivalent source signal; and respectively inputting the equivalent source signals into the rest groups of vibration transfer functions to respectively obtain actual vibration signals. The prediction method omits a large number of source intensity acquisition and modeling processes, greatly reduces soil layer medium modeling, only needs to consider the output effect from actual source intensity to equivalent source, can restore the wave field of the structure to be analyzed only through a small number of surface actual measurements, and can greatly improve the calculation accuracy and efficiency.
Description
Technical Field
The invention relates to the technical field of rail transit vibration environment prediction, in particular to a rail transit environment vibration prediction method, a prediction system and a vibration reduction measure evaluation method.
Background
The rail transit not only greatly improves the urban trip efficiency and the urban comprehensive bearing capacity, but also can stimulate the urban economy and social vitality, so that the construction significance of an urban rail transit network is great. Under the background that the operation mileage of urban rail transit is exponentially increased and the scale of a wire network is continuously encrypted and expanded, the rail transit line inevitably overlaps with the range of vibration sensitive buildings such as existing or newly-built houses, hospitals, schools, scientific research units, concert halls and the like, and even the sensitive buildings are worn. In the current stage, the rail transit line mainly uses subways, vibration generated by the wheel track action of the subways is transmitted to a building foundation through a tunnel structure and a rock-soil medium, so that the indoor living comfort of a building is reduced, vibration-sensitive equipment fails and the like are caused, and the influence of secondary radiation noise caused by floor slab and wall vibration further aggravates the influence of vibration pollution.
In order to reduce the vibration influence caused by rail transit, the primary task is to determine the vibration characteristics and magnitude of the vibration, and then vibration reduction and noise reduction measures can be taken in a targeted manner. Although the vibration rules of the subway track-track bed-tunnel structure and surrounding rock and soil media and structures can be really known through field actual measurement, the more accurate vibration condition can be known only after the project is completely finished, so that the field actual measurement is often too late and cannot meet the prediction requirement.
At present, an empirical analogy prediction method is commonly adopted to judge the vibration characteristics and magnitude of a project site, and the method takes result data of a built project as empirical data to be input into a prediction model so as to judge the vibration environment of an evaluation object. However, because each line train form, track bed structure, tunnel and rock-soil characteristics have their own characteristics, in the simulation process, the empirical data is used as the source intensity input of the model or the simplified train load model is used for the source intensity input, which causes the input source intensity data to have great uncertainty, thereby greatly influencing the accuracy of the predicted structure.
Traditional subway environment vibration evaluation, no matter theoretical analysis, numerical simulation or model experiment, all develop under the known condition of source strength, but be limited by the actual condition in the actual engineering, the source strength often is difficult to obtain or the acquisition cost is higher. In actual engineering implementation, the acquisition process of the vibration source intensity faces a lot of difficulties, many project actual conditions cause that accurate source intensity actual measurement data cannot be directly acquired, existing source intensity data of other similar circuits are usually selected to carry out approximate solution simulation, and similar source intensity data obtained by limiting a tested test piece is different from actual conditions necessarily, so that numerical value prediction precision is influenced.
In addition, the actual establishment of the model of the track-track bed-tunnel structure-surrounding rock and soil media-building structure consumes a large amount of manpower and material resources, and even sometimes the modeling task cannot be completed when the model is too large and the computer resources are limited. Particularly, when the tunnel burial depth is large, more rock and soil media need to be modeled, and the problem that the model is too large and the calculation efficiency is lower is caused.
Disclosure of Invention
Objects of the invention
The invention aims to provide a rail transit environment vibration prediction method, a prediction system and a vibration reduction measure evaluation method, which can omit the step of obtaining source intensity vibration, omit source intensity modeling during numerical simulation or theoretical analysis, and reduce soil layer medium modeling to the maximum extent, thereby solving the problems of low modeling calculation efficiency and difficult source intensity obtaining in the prior art.
(II) technical scheme
In order to solve the technical problem, in a first aspect, the present invention provides a rail transit environmental vibration prediction method, including:
establishing a dynamic finite element model of a project site, inputting a simulated vibration source signal into the dynamic finite element model, and respectively obtaining model reaction signals of at least two vibration positions in the project site through dynamic finite element calculation;
taking the simulated vibration source signal and any one of the model reaction signals as a group of simulated data, and respectively processing each group of the simulated data through frequency spectrum analysis to respectively obtain a plurality of groups of vibration transfer functions in the dynamic finite element model;
actually measuring an actual vibration signal of any vibration position, and inputting the actual vibration signal into any group of vibration transfer functions to invert a corresponding equivalent source signal;
and respectively inputting the equivalent source signals into the rest groups of vibration transfer functions to respectively obtain corresponding actual vibration signals.
In some embodiments, the creating a dynamic finite element model of the project site, inputting a simulated vibration source signal into the dynamic finite element model, and obtaining model reaction signals of at least two vibration positions in the project site through dynamic finite element calculation respectively further includes:
and inputting a simulated vibration source time-course signal into the dynamic finite element model to respectively obtain model reaction time-course signals of at least two vibration positions in the project site.
In some embodiments, the processing, with the simulated vibration source signal and any one of the model reaction signals as a set of simulated data, each set of the simulated data through spectrum analysis to obtain a plurality of sets of vibration transfer functions in the dynamic finite element model, further includes:
respectively carrying out Fourier transform on the simulated vibration source time-course signal and each model reaction time-course signal to respectively obtain a simulated vibration source frequency-domain signal and each corresponding model reaction frequency-domain signal;
and taking the simulated vibration source frequency domain signal and any one of the model reaction frequency domain signals as a group of simulated frequency domain data, and respectively obtaining a plurality of groups of vibration transfer functions according to the groups of simulated frequency domain data.
In some embodiments, the actually measuring an actual vibration signal of any one of the vibration positions and inputting the actual vibration signal into any one of the sets of vibration transfer functions to invert a corresponding equivalent source signal further includes:
actually measuring to obtain the actual vibration time-course signal of any vibration position;
performing Fourier transform on the actual vibration time-course signal according to frequency spectrum analysis to obtain a corresponding actual vibration frequency-domain signal;
and inputting the actual vibration frequency domain signal into the corresponding vibration transfer function to obtain a corresponding equivalent source frequency domain signal.
In some embodiments, the inputting the equivalent source signal into each of the remaining sets of the vibration transfer functions to obtain each corresponding actual vibration signal respectively further includes:
inputting the same equivalent source frequency domain signal into the rest groups of vibration transfer functions respectively to obtain corresponding actual vibration frequency domain signals respectively;
and performing Fourier inverse transformation on each actual vibration frequency domain signal to obtain each actual vibration time-course signal through inversion.
In some embodiments, the mathematical expression of the vibration transfer function is:
wherein:
tf is the vibration transfer function;
x (f) is the analog vibration source frequency domain signal;
y (f) is the model reaction frequency domain signal;
r (f) is the actual vibration frequency domain signal;
s (f) is the equivalent source frequency domain signal.
In some embodiments, the mathematical expression of a certain time-frequency characteristic pulse signal of the analog vibration source signal is as follows:
wherein:
x (t) is the time domain of the analog vibration source time-course signal;
x (omega) is the frequency domain amplitude of the analog vibration source frequency domain signal;
omega is angular frequency; i is an imaginary unit; e is an exponential symbol;
a 0 the maximum amplitude of the time domain of the analog vibration source time-course signal is obtained;
t 0 and gamma is the narrow-band pulse heald of the analog vibration source signalAnd combining the bandwidth adjustment coefficients.
In some embodiments, in the mathematical expression (2), the angular frequency ω includes an upper cut-off frequency ω 0 The upper limit cut-off frequency ranges from 100Hz to 200Hz, and the comprehensive bandwidth adjustment coefficient (t) of the narrow-band pulse of the analog vibration source signal 0 γ) ranges are:
in some embodiments, the prediction method further comprises:
respectively establishing at least two power finite element models in the project site, selecting any one power finite element model as a first model, taking the power finite element model in which at least one target position is located as a second model, and respectively inputting the same simulated vibration source signal into the first model and the second model to respectively obtain a first reaction signal of the first model and a second reaction signal of the second model;
simultaneously processing the analog vibration source signal, the first reaction signal and the second reaction signal through frequency spectrum analysis to respectively obtain a first vibration transfer function in the first model and a second vibration transfer function in the second model;
actually measuring a first vibration signal of any vibration position in the first model, and inputting the first vibration signal into the first vibration transfer function to invert the corresponding equivalent source signal;
and inputting the equivalent source signal into the second vibration transfer function to calculate a second vibration signal of the target position.
In a second aspect, the present invention provides a rail transit vibration environment prediction system, including:
the model establishing module is used for establishing a dynamic finite element model of the project site, inputting a simulated vibration source signal into the dynamic finite element model, and respectively obtaining model reaction signals of at least two vibration positions in the project site through dynamic finite element calculation;
the vibration analysis module is used for taking the simulated vibration source signal and any one model reaction signal as a group of simulated data, and respectively processing each group of the simulated data through frequency spectrum analysis so as to respectively obtain a plurality of groups of vibration transfer functions in the dynamic finite element model;
the vibration inversion module is used for actually measuring an actual vibration signal of any vibration position and inputting the actual vibration signal into any group of vibration transfer functions to invert a corresponding equivalent source signal;
and the vibration calculation module is used for respectively inputting the equivalent source signals into the rest groups of vibration transfer functions so as to respectively obtain the corresponding actual vibration signals.
In a third aspect, the present invention provides a method for evaluating a vibration damping measure, comprising:
actually measuring an actually measured vibration signal of a vibration position on any model of the project site;
taking the model reaction signal as a prediction vibration signal, and comparing the prediction vibration signal with the actually measured vibration signal to obtain a modified dynamic finite element model;
applying vibration reduction measures to the building structure according to the modified dynamic finite element model to obtain a dynamic finite element model applied with vibration reduction measures;
the second model after the vibration reduction measure is applied is subjected to re-prediction by using the rail transit environment vibration prediction method as claimed in any one of claims 1 to 9, so as to evaluate the vibration reduction effect of the vibration reduction measure.
(III) advantageous effects
The technical scheme of the invention has the following beneficial effects: the rail transit environment vibration prediction method comprises the following steps: establishing a dynamic finite element model of the project site, inputting a simulated vibration source signal into the dynamic finite element model, and respectively obtaining model reaction signals of at least two vibration positions in the project site through dynamic finite element calculation; taking the simulated vibration source signal and any model reaction signal as a group of simulated data, and respectively processing each group of simulated data through frequency spectrum analysis to respectively obtain a plurality of groups of vibration transfer functions in the dynamic finite element model; actually measuring an actual vibration signal of any vibration position, and inputting the actual vibration signal into any group of vibration transfer functions to invert a corresponding equivalent source signal; and respectively inputting the equivalent source signals into the rest groups of vibration transfer functions to respectively obtain corresponding actual vibration signals. The prediction method provided by the invention omits the step of acquiring actual source intensity data, omits source intensity modeling during numerical simulation or theoretical analysis, reduces the soil layer medium modeling to the maximum extent, only needs to consider the output effect of the source intensity, can restore the wave field of the structure to be analyzed only through a small amount of actual measurement, and can concentrate a large amount of modeling energy and computing resources on the fine simulation of the building structure of the vibration receptor so as to improve the computing precision.
On one hand, the prediction method omits the actual source intensity acquisition step, and does not need to enter the interior of the subway tunnel or the interior of a line operation area to carry out vibration source intensity test work, so that the error problem which is easy to generate when the source intensity is acquired is avoided, the source intensity acquisition difficulty brought by the limitation of field test conditions is greatly reduced, and the operability is very strong;
on the other hand, the prediction method does not need to consider the tunnel-track bed-track-train structure during numerical modeling, and can simplify soil layer modeling according to needs, thereby greatly reducing the calculation amount, reducing the input parameter requirements and effectively improving the calculation precision.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of a rail transit environment vibration prediction method according to an embodiment of the present invention;
FIG. 2 is a time-course waveform of a time-domain bell pulse in accordance with an embodiment of the present invention;
FIG. 3 is a Fourier amplitude spectrum of a time domain bell pulse of an embodiment of the present invention;
fig. 4 is an application example 1 of the rail transit environment vibration prediction method according to the embodiment of the invention;
fig. 5 is an application embodiment 2 of the rail transit environment vibration prediction method according to the embodiment of the invention;
fig. 6 is a schematic structural diagram of a rail transit vibration environment prediction system according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in further detail with reference to the drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The embodiment of the invention provides a rail transit environment vibration prediction method, which can predict and reduce the vibration environment of a target position without acquiring actual vibration source data and performing source intensity modeling during numerical simulation or theoretical analysis when the vibration source data are unknown, thereby improving the prediction efficiency and the precision of prediction calculation.
As shown in fig. 1, the prediction method includes the following steps:
s1, establishing a power finite element model of a project site, inputting a simulated vibration source signal into the power finite element model, and respectively obtaining model reaction signals of at least two vibration positions in the project site through power finite element calculation;
s2, taking the simulated vibration source signal and any model reaction signal as a group of simulated data, and respectively processing each group of simulated data through frequency spectrum analysis to respectively obtain a plurality of groups of vibration transfer functions in the dynamic finite element model;
s3, actually measuring an actual vibration signal of any vibration position, and inputting the actual vibration signal into any group of vibration transfer functions to invert a corresponding equivalent source signal;
and S4, respectively inputting the equivalent source signals into the rest groups of vibration transfer functions to respectively obtain corresponding actual vibration signals.
According to research, the vibration frequency of the foundation soil in a 25m range is mostly concentrated above 30Hz when a train passes through, and the dynamic strain range of the soil body caused by the vibration frequency is mostly 10 -5 ～10 -4 The method belongs to the typical research category of high-frequency low-amplitude elastic fluctuation. Therefore, the dynamic reaction analysis (including the dynamic finite element calculation) does not need to consider the dynamic nonlinear effect and the large displacement deformation of the rock-soil body.
Specifically, in the discrete form of the dynamic equation used for dynamic response analysis, the mass matrix, the damping matrix and the stiffness matrix are all constants, so that when the dynamic model established on the project site is linear, the vibration system of the model remains unchanged. Therefore, for any input vibration signal, the vibration transfer function is a fixed function.
In the prediction method provided by the embodiment of the invention, a broadband signal time interval (such as a limited bandwidth pulse time interval) is adopted as a simulated vibration source signal and is input into a power finite element model of a project site, a series of power reaction analysis of the project site is carried out, so that a vibration transfer function in the power finite element model is obtained, and then the linear property of a linear invariant system (namely the power finite element model) is utilized to carry out reaction calculation under complex input.
Because the time-domain analysis operation of the signal is to solve a differential equation and an integral equation in the time domain, relatively speaking, the time-domain operation of the signal is complex, and in order to facilitate the analysis operation and improve the calculation accuracy, in this embodiment, each signal is correspondingly processed by a frequency-domain analysis method (also called a transfer function method), and the specific processing steps are as described in the following steps S21 and S22.
Therefore, in the prediction method provided by the embodiment of the invention, only the vibration reaction of any point on the surface of the soil layer of the free ground field between the source intensity and the building structure needs to be tested, the building structure and a small amount of soil layers on the field need to be modeled, and the wave field of the linear field is restored by utilizing the vibration transfer function method of the linear model through actually measuring the ground surface reaction, so that the vibration prediction of the rail transit environment is completed, and the actual vibration reaction of each floor and the surrounding ground of the building structure is obtained.
According to the prediction method provided by the embodiment of the invention, direct source strong excitation of the track, the track bed or the tunnel vibration caused by the interaction of the wheel and the track is not required to be actually measured, only a foundation soil vibration reaction signal (namely an actual vibration signal) caused by the direct source strong excitation is required to be actually measured, and then an equivalent source at a certain position below a project site is inverted by utilizing a predetermined vibration transfer function to serve as an input signal (namely an equivalent source signal) of the whole model, so that the vibration influence of the building structure caused by the track traffic can be accurately predicted finally.
As shown in fig. 1, the prediction method described in this embodiment does not need to establish a source intensity model, so that the following two disadvantages can be effectively avoided:
(1) According to the prediction method, accurate source intensity data are obtained through actual measurement without entering the field, and source intensity errors are introduced by means of similar source intensity test data, so that the calculation efficiency and the calculation precision are effectively improved.
(2) The prediction method does not need to establish a track-track bed-tunnel structure model, can reduce the establishment of surrounding rock and soil medium models as much as possible, and can concentrate a great deal of modeling energy and computing resources on the fine simulation of the building structure of the vibration receptor, thereby greatly improving the computing efficiency and the computing precision.
Specifically, in the prediction method of this embodiment, the project site in step S1 refers to a site where the prediction object is located, and the project site includes a site soil layer and a surface building structure. The simulated vibration source signal is signal data of a known vibration source preset at the lower edge position of the dynamic finite element model of the project site.
Further, the step S1 specifically includes:
s11, inputting a simulated vibration source time course signal x (t) into the dynamic finite element model to respectively obtain model reaction time course signals y (t) of at least two vibration positions in the project field.
In other words, step S11 represents that at least two model reaction time-course signals y (t) in the same model are obtained from the simulated vibration source time-course signal x (t) by the time domain solution of the dynamic finite element method by inputting a wide-band simple time-course into the dynamic finite element model as the simulated vibration source time-course signal x (t).
Further, the step S2 specifically includes:
s21, respectively carrying out Fourier transform on the simulated vibration source time-course signal x (t) and each model reaction time-course signal y (t) through a frequency domain analysis method to respectively obtain a simulated vibration source frequency-domain signal x (f) and each corresponding model reaction frequency-domain signal y (f);
and S22, taking the simulated vibration source frequency domain signal x (f) and any model reaction frequency domain signal y (f) as a group of simulated frequency domain data, and respectively obtaining a plurality of groups of vibration transfer functions tf according to the groups of simulated frequency domain data.
The relationship among the simulated vibration source frequency domain signal x (f), the model reaction frequency domain signal y (f) and the vibration transfer function tf in step S22 is described in the following mathematical expression (1) of the vibration transfer function.
Further, the step S3 specifically includes:
s31, actually measuring to obtain an actual vibration time-course signal r (t) of any vibration position;
s32, performing Fourier transform on the actual vibration time-course signal r (t) according to frequency spectrum analysis to obtain a corresponding actual vibration frequency-domain signal r (f);
and S33, inputting the actual vibration frequency domain signal r (f) into the corresponding vibration transfer function tf to obtain a corresponding equivalent source frequency domain signal S (f).
The relationship among the actual vibration frequency domain signal, the equivalent source frequency domain signal and the vibration transfer function in step S33 is described in the following mathematical expression (1) of the vibration transfer function.
It can be understood that, in the same dynamic finite element model, a vibration transfer function exists between the vibration source position and each vibration position, and the vibration transfer function reflects the vibration relationship between the vibration source position and the current vibration position, so that the vibration transfer functions are different when the vibration positions are different. It can be seen that the vibration transfer function in step S33 is the vibration transfer function tf corresponding to the actual vibration frequency domain signal r (f) obtained by actual measurement.
Further, the step S4 specifically includes:
s41, respectively inputting the same equivalent source frequency domain signal S (f) into the rest groups of vibration transfer functions tf to respectively obtain corresponding actual vibration frequency domain signals r (f);
and S42, carrying out inverse Fourier transform on each actual vibration frequency domain signal r (f) to obtain each actual vibration time-course signal r (t) through inversion.
In step S41 and step S42, the actual vibration signals corresponding to the plurality of specified vibration transfer functions in the same model can be estimated using the same equivalent source signal, and the estimation efficiency and the estimation accuracy are high.
It can be understood that, according to all the decomposition steps from the step S1 to the step S4, in order to estimate actual vibration signals of more vibration positions, a plurality of vibration positions should be preset, and model reaction signals of all the vibration positions should be predetermined by using dynamic finite element calculation, so as to determine all the corresponding vibration transfer functions.
Fig. 1 shows a schematic diagram of a prediction method according to an embodiment of the present invention. Tf shown in fig. 1 is a vibration transfer function, x (t) is an analog vibration source time-course signal, x (f) is an analog vibration source frequency-domain signal, y (t) is a model reaction time-course signal, y (f) is a model reaction frequency-domain signal, r (t) is an actual vibration time-course signal, r (f) is an actual vibration frequency-domain signal, s (t) is an equivalent source time-course signal, s (f) is an equivalent source frequency-domain signal, FFT represents fourier transform, and IFFT represents inverse fourier transform.
Specifically, the mathematical expression of the vibration transfer function according to the embodiments of the present invention is:
based on the relationship of the transfer functions, the prediction method of the embodiment establishes a passive strong theoretical analysis model and a finite element analysis model, inverses the input wave field of the passive strong rail transit environment vibration prediction by utilizing the ground surface vibration actually measured between the source strength and the building structure, then carries out the environmental vibration prediction analysis and evaluation of the building structure containing the vibration receptor caused by the rail transit operation, and provides a basis for the design of the vibration reduction measures of the building structure.
In the embodiment of the present invention, as shown in fig. 2 and fig. 3, a single time domain clock pulse with a wide bandwidth in a frequency domain is selected as an input analog vibration source time interval signal x (t).
Specifically, fig. 2 shows a time domain waveform diagram of the analog vibration source time interval signal x (t) as the analog vibration source signal. The abscissa in FIG. 2 is time T/s; the ordinate is the displacement amplitude U. Fig. 3 shows a fourier amplitude spectrum (i.e., frequency domain amplitude spectrum X (ω)) of the analog vibration source frequency domain signal as the analog vibration source signal, in which the abscissa of fig. 3 represents frequency and the ordinate represents amplitude of the fourier spectrum.
The mathematical expression of a certain time-frequency characteristic pulse signal as an analog vibration source signal is as follows:
wherein:
x (t) is the time domain of the analog vibration source time-course signal;
x (omega) is the frequency domain amplitude of the frequency domain signal of the analog vibration source;
omega is angular frequency; i is an imaginary unit; e is an exponential symbol;
a 0 the maximum amplitude of the time domain of the time-course signal of the analog vibration source is obtained;
t 0 and gamma is a narrow-band pulse comprehensive bandwidth adjustment coefficient of the analog vibration source signal.
In the mathematical expression (2), the angular frequency ω includes an upper limit cutoff frequency ω 0 In other words, the angular frequency ω is a range value, and the range corresponding to the angular frequency ω includes a smaller range, which is the range of the cut-off frequency, and the range of the cut-off frequency has a maximum end value, which is the upper cut-off frequency ω 0 . Upper cut-off frequency omega 0 And a moldNarrow-band pulse comprehensive bandwidth adjustment coefficient t of quasi-vibration source signal 0 And γ have the following relationship: omega 0 And t 0 Is inversely proportional to each other, and ω 0 And is proportional to gamma.
According to the existing vibration environment data, the dynamic analysis in the seismic engineering is only required to be 30Hz, but the environmental vibration induced by the rail vehicle in operation at least reaches 100Hz, so the angular frequency in the mathematical expression (2) needs to be further processed.
Since the adjustment coefficient of the comprehensive bandwidth of the narrow-band pulse is an adjustable parameter of the pulse signal, the upper-limit cutoff frequency ω required by the vibration environment in the embodiment can be obtained by adjusting the parameter t0 and the parameter γ 0 . According to the dynamic analysis, the upper cut-off frequency (also called high-frequency cut-off frequency) ω applicable to the rail transit environment vibration described in this embodiment 0 The value range of (a) is 100 Hz-200 Hz, and the maximum amplitude a of the time domain of the analog vibration source time course signal 0 Taking 1, simulating the comprehensive bandwidth adjustment coefficient (t) of the narrow-band pulse of the vibration source signal 0 γ) ranges are:
as can be seen from the kinetic analysis, the larger the parameter γ and the larger the parameter t 0 The smaller the upper limit cut-off frequency omega 0 The higher. The method specifically comprises the following steps: when the parameter t 0 When 0.1 is taken and the parameter gamma is 2000, the upper cut-off frequency omega is set 0 Is 15Hz; when the parameter t 0 When 0.05 is taken and the parameter gamma is 10000, the upper limit cut-off frequency omega is taken 0 Is 30Hz; when the parameter t 0 Taking 0.01 and the parameter gamma as 20000, the upper cut-off frequency omega is set 0 Is 100Hz; when the parameter t 0 When 0.005 is taken and the parameter gamma is 40000, the upper cut-off frequency omega is set 0 Is 200Hz.
In this embodiment, the simulated vibration source signal is input into the model from the bottom boundary position of the dynamic finite element model. After the simulated vibration source signal is used as a simulated known source intensity and is input into a dynamic finite element model, the vibration transfer function of the model can be determined by the prediction method, so that an equivalent source signal corresponding to an actually measured vibration position is deduced, further, the simulation of a linear field wave field containing a building structure without testing the source intensity can be realized, and the vibration influence analysis of the rail transit environment can be accurately carried out.
Further, the embodiment of the present invention provides another prediction method based on the above prediction method. The method can simultaneously establish a plurality of dynamic finite element models, and obtains respective vibration transfer functions of the two models based on the basic model and the model of the position to be measured, and the two models are provided with the same simulation vibration source, so that the two dynamic finite element models can be associated, the excessively complicated models are avoided to be established, the quantity of the models to be established is moderate, the calculation efficiency can be effectively improved, and the resource waste is avoided.
Specifically, the prediction method comprises the following steps:
s1', respectively establishing at least two power finite element models in a project site, selecting any power finite element model as a first model, taking the power finite element model in which at least one target position is located as a second model, and respectively inputting the same simulated vibration source signal into the first model and the second model to respectively obtain a first reaction signal of the first model and a second reaction signal of the second model;
s2', simultaneously processing the analog vibration source signal, the first reaction signal and the second reaction signal through frequency spectrum analysis to respectively obtain a first vibration transfer function in the first model and a second vibration transfer function in the second model;
s3', actually measuring a first vibration signal of any vibration position in the first model, and inputting the first vibration signal into a first vibration transfer function to invert a corresponding equivalent source signal;
and S4', inputting the equivalent source signal into a second vibration transfer function to calculate a second vibration signal of the target position.
It should be noted that if the size of the building structure in the project site is small, the foundation burial depth is shallow or the building structure is directly seated near the ground surface, the influence of the building structure on the site vibration before and after construction does not need to be considered. That is, when the influence of the field wave field distribution caused by the vibration of the rail transit is not large regardless of whether the building structure is built or not, the ground vibration caused by the rail transit operation actually measured at the building seat (i.e., the vacant land without building the building) can be directly adopted as the vibration input of the building base, and the environmental vibration caused by the rail transit operation can be directly predicted for the building structure without building a field soil model.
However, if the building structure to be built is high in floors, large in size and deep in building foundation, and the wave field distribution on the ground of the site before and after the building is not negligible, the method of the present invention is preferably applied. The method specifically comprises the following steps: firstly, the ground vibration caused by rail transit operation at a building seat (an open space where a building is not built) is measured, a small amount of soil layers and a building structure are selected for modeling, a vibration transfer function of a wave field is solved under virtual broadband pulse input, an equivalent source signal at the bottom of the model is inverted, and then linear field wave field simulation containing the building structure without strong test source is carried out, so that rail transit environment vibration influence analysis can be accurately carried out.
No matter what the situation is, the prediction method can be applied to environmental vibration prediction, and the source intensity does not need to be simulated and actually measured during prediction, so that the trouble of source intensity test and the complexity of source intensity modeling are omitted.
Specifically, the following specific application examples are proposed for the two field conditions, specifically including: example 1 in which only the building structure and the soil layer are individually modeled without considering the influence on the field wave field vibration before and after the building structure is constructed, example 2 in which both are integrally modeled, further modified example 3 for the individual building structure based on example 2, and model correction application example 4 based on example 2.
Example 1
As shown in fig. 4, the specific process of the prediction method in the embodiment 1 in practical application is as follows:
(1) Acquiring soil layer distribution of a site and conventional physical and mechanical parameters of soil such as density, porosity ratio, water content, elastic modulus, wave speed and the like through geotechnical investigation data of a building site, establishing a site soil layer finite element model M1 according to the soil layer distribution, the soil layer selection depth is preferably 1.5 times of the building foundation depth, and the bottom and the side surfaces of the model can be processed in a viscoelastic boundary, transmission boundary and other modes;
(2) Suppose that a high-frequency cut-off frequency omega is applied to a point o1 at the bottom boundary of a soil finite element model M 0 The broadband pulse with the frequency of 200Hz is used as a simulation vibration source time-course signal x (t) for model excitation, and model reaction time-course signals yb1 (t) and yb2 (t) of a certain reference point b1 and b2 of the free earth surface on the upper part of a model M1 are obtained, wherein the reference points b1 and b2 can be points on the surface of a soil layer or points where a building structure of a building seat is located;
(3) Performing Fourier transform on an input simulated vibration source time-course signal x (t) and output response model reaction time-course signals yb1 (t) and yb2 (t) corresponding to the input simulated vibration source time-course signal x (t) respectively to obtain a simulated vibration source frequency-domain signal x (f) and model reaction frequency-domain signals yb1 (f) and yb2 (f), and dividing the model reaction frequency-domain signals yb1 (f) and yb2 (f) by the simulated vibration source frequency-domain signal x (f) respectively according to the mathematical expression (1) to obtain two vibration transfer functions tf1 and tf2 of the model M1;
(4) The earth surface vibration response of the free earth surface observation point b1 of the actual measurement model M1 is used as an actual vibration time-course signal rb1 (t);
(5) Performing fourier transform on the actual vibration time-course signal rb1 (t) to obtain a corresponding actual vibration frequency-domain signal rb1 (f), and dividing the actual vibration frequency-domain signal rb1 (f) by a vibration transfer function tf1 corresponding to the process (3), so as to obtain an equivalent source frequency-domain signal s (f) of a virtual vibration source position (namely an equivalent source position) o1 corresponding to the free surface observation point b1 in the model M1 by a transfer function method;
(6) Carrying out inverse Fourier transform on the equivalent source frequency domain signal s (f) in the process (5) to obtain an equivalent source time-course signal s (t);
(7) Acquiring the size of a main stress component (including a foundation) and detailed configuration (including reinforcing bars) in the component through a construction drawing of a building structure to establish a finite element model of a building unit;
(8) And (3) carrying out actual input of the finite element model of the building unit on the virtual excitation in the process (6) so as to deduce the vibration reaction of any position of the building structure, such as: the equivalent source frequency signal s (f) is input to the corresponding vibration transfer function tf2 in the process (3) to calculate the actual vibration frequency domain signal rb2 (f) of the free-surface observation point b2 of the model M1.
Similarly, the vibration time course prediction of other vibration positions in the model M1 can also be circularly completed by using the prediction method.
Example 2
As shown in fig. 5, the specific process of the prediction method described in this embodiment 2 in practical application is as follows:
(1) Acquiring soil layer distribution of a site, conventional physical and mechanical parameters of soil such as density, porosity ratio, water content, an elastic model, wave speed and the like through geotechnical survey data of a building site, acquiring the size of a main stressed member (including a foundation) and detailed configuration (including reinforcing bars) in the member through a construction drawing of a building structure, and establishing a soil layer-building structure integral finite element model M (M comprises M1 and M2) of a project site, wherein the selected depth of the soil layer is 1.5 times of the depth of the building foundation, and the bottom and the side surfaces of the model can be processed by adopting viscoelastic boundaries, transmission boundaries and other modes;
(2) Suppose that a high-frequency cut-off frequency ω is applied at the bottom boundary of the model M1 0 A broadband pulse a with the frequency of 200Hz is used as a simulation vibration source time-course signal x (t) for model excitation, and a model reaction time-course signal yb1 (t) of a certain reference point b1 on the free earth surface at the upper part of a model M1 and a model reaction time-course signal yc1 (t) of a building structure target floor position point c1 are obtained;
(3) Respectively performing Fourier transform on a simulated vibration source time-course signal x (t) of a reference point o1 and a model reaction time-course signal yb1 (t) of an output response position b1 corresponding to the simulated vibration source time-course signal x (t) to obtain a simulated vibration source frequency-domain signal x (f) and a model reaction frequency-domain signal yb1 (f), and dividing the model reaction frequency-domain signal yb1 (f) by the simulated vibration source frequency-domain signal x (f) according to the mathematical expression (1) to obtain a vibration transfer function tf1 of the model M1; similarly, a simulated vibration source time-course signal x (t) of the reference point o1 and a model reaction time-course signal yc1 (t) of the corresponding output response position c1 are respectively subjected to fourier transform to obtain a simulated vibration source frequency domain signal x (f) and a model reaction frequency domain signal yc1 (f), and the model reaction frequency domain signal yc1 (f) is divided by the simulated vibration source frequency domain signal x (f), so that a vibration transfer function tf2 of the model M2 is obtained;
(4) Taking the earth surface vibration response of a free earth surface observation point b1 on the actual measurement model M1 as an actual vibration time-course signal rb1 (t);
(5) Performing fourier transform on the actual vibration time-course signal rb1 (t) to obtain a corresponding actual vibration frequency-domain signal rb1 (f), and dividing the actual vibration frequency-domain signal rb1 (f) by the vibration transfer function tf1 corresponding to the process (3), so as to obtain an equivalent source frequency-domain signal s (f) of a virtual vibration source position (namely an equivalent source position) o1 corresponding to the free surface observation point b1 on the model M1 in the model M by using a transfer function method;
(6) Multiplying the virtually input equivalent source frequency domain signal s (f) by a vibration transfer function tf2 of a building and site model M2 to obtain a predicted vibration response of a specified position point c1 on a building structure, namely obtaining an actual vibration frequency domain signal rc1 (f) of the specified position point c1 on the model M2 through prediction;
(8) And performing inverse Fourier transform on the actual vibration frequency domain signal rc1 (f) of the specified position c1 on the model M2 to obtain an actual vibration time-course signal rc1 (t), thereby completing response prediction at the specified position on the building structure.
Similarly, the vibration time course prediction of other point positions of the building structure can also be circularly completed by adopting the prediction method.
Example 3
In addition to embodiment 2, embodiment 3 proposes a prediction method capable of predicting a vibration environment for a simple building structure. The same parts of embodiment 3 as embodiment 2 will not be described again, but the differences are:
if excitation can be carried out on the soil layer earth surface of the soil layer-building finite element model, broadband pulse time-course excitation can be carried out on a certain point of the earth surface meeting the test condition, so that a pulse input vibration time course from the certain point on the ground to a certain influence point of a building floor is obtained, and a vibration transfer function from the certain point on the ground to the influence point of the floor is obtained; vibration of an excitation point preset on the ground surface is actually measured, and then deduction is carried out according to a transfer function method through the prediction method of the embodiment 2, so that time-course signals of the corresponding floor influence points are obtained, and then the environmental vibration environment prediction of the whole building structure is completed.
Based on the prediction methods of the three embodiments, the embodiment of the invention also provides a rail transit vibration environment prediction system. As shown in fig. 6, the prediction system includes a model building module 1, a vibration analysis module 2, a vibration inversion module 3, and a vibration estimation module 4, which are connected in sequence. The model building module 1 is used for building a dynamic finite element model of a project site and inputting a simulated vibration source signal into the dynamic finite element model to obtain a model reaction signal; the vibration analysis module 2 is used for simultaneously processing the simulated vibration source signal and the model reaction signal through frequency spectrum analysis so as to obtain a vibration transfer function in the dynamic finite element model; the vibration inversion module 3 is used for inputting an actual vibration signal of any vibration position of the project site obtained through actual measurement into the vibration transfer function so as to invert a corresponding equivalent source signal; the vibration estimation module 4 is configured to input the equivalent source signal into each of the other sets of vibration transfer functions, so as to obtain each of the corresponding actual vibration signals.
Based on the prediction methods described in the above three embodiments, the present embodiment further provides a vibration reduction measure evaluation method. The method for evaluating the vibration damping measures comprises the following steps:
and N1, actually measuring vibration signals of vibration positions on any model of the project site.
N2, taking the model reaction signal in each embodiment as a predicted vibration signal, comparing the predicted vibration signal with an actually measured vibration signal, and obtaining a model correction coefficient according to the difference value of the predicted vibration signal and the actually measured vibration signal to perform model correction so as to obtain a corrected power finite element model;
n3, applying vibration reduction measures to the building structure according to the corrected power finite element model to obtain the power finite element model to which the vibration reduction measures are applied;
and N4, re-predicting the dynamic finite element model after the vibration reduction measures are applied by using the prediction method of any one of the embodiments 1 to 3 so as to evaluate the vibration reduction effect of the vibration reduction measures.
The evaluation method described in this embodiment may use the above prediction system to complete the evaluation. The evaluation method can quickly correct the model, so that accurate and effective vibration reduction measures are applied to the model, and the vibration reduction measures are reliably evaluated. In addition, in the model correction process, the subsequent operations of model correction, vibration reduction measure application and the like can be completed only by carrying out a small amount of actual measurement on the local model in the overall model without acquiring the source strength data of the substrate or the tunnel of the overall model, and the working efficiency and the accuracy are effectively improved.
In summary, the method for predicting rail transit environment vibration according to the embodiment includes: establishing a dynamic finite element model of a project site, and inputting a simulated vibration source signal to obtain a model reaction signal; simultaneously processing the simulated vibration source signal and the model reaction signal through frequency spectrum analysis to obtain a vibration transfer function in the dynamic finite element model; inputting an actual vibration signal actually measured at any vibration position on a project site into a vibration transfer function to invert a corresponding equivalent source signal; and respectively inputting the equivalent source signals into the rest groups of vibration transfer functions to respectively obtain corresponding actual vibration signals. The prediction method provided by the invention omits the step of acquiring actual source intensity data, omits source intensity modeling during numerical simulation or theoretical analysis, reduces the soil layer medium modeling to the maximum extent, only needs to consider the output effect of the source intensity, can restore the wave field of the structure to be analyzed only through a small amount of actual measurement, and can concentrate a large amount of modeling energy and computing resources on the fine simulation of the building structure of the vibration receptor so as to improve the computing precision and efficiency.
On one hand, the prediction method provided by the embodiment of the invention omits the actual source intensity obtaining step, and does not need to enter the interior of a subway tunnel or the interior of a line operation area to carry out vibration source intensity test work, so that the error problem which is easy to generate when the source intensity is obtained is avoided, the source intensity obtaining difficulty caused by the limitation of field test conditions is greatly reduced, and the operability is very strong;
on the other hand, the prediction method provided by the embodiment of the invention does not need to consider the tunnel-track bed-track-train structure during numerical modeling, and can simplify soil layer modeling as required, thereby greatly reducing the calculation amount and the input parameter requirements, and effectively improving the calculation accuracy and efficiency.
The embodiments of the present invention have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (11)
1. A rail transit environment vibration prediction method is characterized by comprising the following steps:
establishing a dynamic finite element model of a project site, inputting a simulated vibration source signal into the dynamic finite element model, and respectively obtaining model reaction signals of at least two vibration positions in the project site through dynamic finite element calculation;
taking the simulated vibration source signal and any one of the model reaction signals as a group of simulated data, and respectively processing each group of the simulated data through frequency spectrum analysis to respectively obtain a plurality of groups of vibration transfer functions in the dynamic finite element model;
actually measuring an actual vibration signal of any vibration position, and inputting the actual vibration signal into any group of vibration transfer functions to invert a corresponding equivalent source signal;
and respectively inputting the equivalent source signals into the rest groups of vibration transfer functions to respectively obtain corresponding actual vibration signals.
2. The method of predicting according to claim 1, wherein the establishing a dynamic finite element model of the project site, inputting a simulated vibration source signal into the dynamic finite element model, and obtaining model reaction signals of at least two vibration positions in the project site through dynamic finite element calculation respectively, further comprises:
and inputting a simulated vibration source time course signal into the dynamic finite element model to respectively obtain model reaction time course signals of at least two vibration positions in the project field.
3. The method of predicting according to claim 2, wherein the step of processing the sets of simulation data by spectrum analysis with the simulated vibration source signal and any one of the model reaction signals as a set of simulation data to obtain a plurality of sets of vibration transfer functions in the dynamic finite element model, further comprises:
respectively carrying out Fourier transform on the simulated vibration source time-course signal and each model reaction time-course signal to respectively obtain a simulated vibration source frequency-domain signal and each corresponding model reaction frequency-domain signal;
and taking the simulated vibration source frequency domain signal and any one of the model reaction frequency domain signals as a group of simulated frequency domain data, and respectively obtaining a plurality of groups of vibration transfer functions according to the groups of simulated frequency domain data.
4. The method of predicting according to claim 3, wherein said measuring an actual vibration signal at any one of said vibration locations and inputting this actual vibration signal into any one of said sets of vibration transfer functions to invert a corresponding equivalent source signal, further comprises:
actually measuring to obtain the actual vibration time-course signal of any vibration position;
performing Fourier transform on the actual vibration time-course signal according to frequency spectrum analysis to obtain a corresponding actual vibration frequency-domain signal;
and inputting the actual vibration frequency domain signal into the corresponding vibration transfer function to obtain a corresponding equivalent source frequency domain signal.
5. The prediction method according to claim 4, wherein the inputting the equivalent source signal into the remaining sets of the vibration transfer functions respectively to obtain corresponding actual vibration signals respectively further comprises:
inputting the same equivalent source frequency domain signal into the rest groups of vibration transfer functions respectively to obtain corresponding actual vibration frequency domain signals respectively;
and performing inverse Fourier transform on each actual vibration frequency domain signal to obtain each actual vibration time-course signal through inversion.
6. The prediction method according to claim 5, wherein the mathematical expression of the vibration transfer function is:
wherein:
tf is the vibration transfer function;
x (f) is the analog vibration source frequency domain signal;
y (f) is the model reaction frequency domain signal;
r (f) is the actual vibration frequency domain signal;
s (f) is the equivalent source frequency domain signal.
7. The prediction method according to claim 6, wherein the mathematical expression of a certain time-frequency characteristic pulse signal of the analog vibration source signal is as follows:
wherein:
x (t) is the time domain of the analog vibration source time-course signal;
x (omega) is the frequency domain amplitude of the analog vibration source frequency domain signal;
omega is angular frequency; i is an imaginary unit; e is an exponential symbol;
a 0 the time domain maximum amplitude of the analog vibration source time-course signal is obtained;
t 0 and gamma is the comprehensive bandwidth adjustment coefficient of the narrow-band pulse of the analog vibration source signal.
8. The prediction method according to claim 7, wherein, in the mathematical expression (2), the angular frequency ω includes an upper limit cutoff frequency ω 0 The upper limit cut-off frequency ranges from 100Hz to 200Hz, and the comprehensive bandwidth adjustment coefficient (t) of the narrow-band pulse of the analog vibration source signal 0 γ) ranges are:
9. the prediction method according to any one of claims 1-8, characterized in that the prediction method further comprises:
respectively establishing at least two power finite element models in the project site, selecting any one power finite element model as a first model, taking a power finite element model with at least one target position as a second model, and respectively inputting the same simulated vibration source signal into the first model and the second model to respectively obtain a first reaction signal of the first model and a second reaction signal of the second model;
simultaneously processing the analog vibration source signal, the first reaction signal and the second reaction signal through frequency spectrum analysis to respectively obtain a first vibration transfer function in the first model and a second vibration transfer function in the second model;
actually measuring a first vibration signal of any vibration position in the first model, and inputting the first vibration signal into the first vibration transfer function to invert the corresponding equivalent source signal;
and inputting the equivalent source signal into the second vibration transfer function to calculate a second vibration signal of the target position.
10. A rail transit vibration environment prediction system is characterized by comprising:
the model establishing module is used for establishing a dynamic finite element model of a project site, inputting a simulated vibration source signal into the dynamic finite element model, and respectively obtaining model reaction signals of at least two vibration positions in the project site through dynamic finite element calculation;
the vibration analysis module is used for taking the simulated vibration source signal and any one model reaction signal as a group of simulated data, and respectively processing each group of the simulated data through frequency spectrum analysis so as to respectively obtain a plurality of groups of vibration transfer functions in the dynamic finite element model;
the vibration inversion module is used for actually measuring an actual vibration signal of any vibration position and inputting the actual vibration signal into any group of vibration transfer functions to invert a corresponding equivalent source signal;
and the vibration calculation module is used for respectively inputting the equivalent source signals into the rest groups of vibration transfer functions so as to respectively obtain the corresponding actual vibration signals.
11. A method for evaluating a vibration damping measure, comprising:
actually measuring an actually measured vibration signal of a vibration position on any model of an actual measurement project site;
taking the model reaction signal as a prediction vibration signal, and comparing the prediction vibration signal with the actually measured vibration signal to obtain a modified dynamic finite element model;
applying a vibration reduction measure to the building structure according to the modified power finite element model to obtain a power finite element model applied with the vibration reduction measure;
the second model after the vibration reduction measure is applied is subjected to re-prediction by using the rail transit environment vibration prediction method as claimed in any one of claims 1 to 9, so as to evaluate the vibration reduction effect of the vibration reduction measure.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910440909.1A CN110334393B (en) | 2019-05-24 | 2019-05-24 | Rail transit environment vibration prediction method, prediction system and vibration reduction measure evaluation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910440909.1A CN110334393B (en) | 2019-05-24 | 2019-05-24 | Rail transit environment vibration prediction method, prediction system and vibration reduction measure evaluation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110334393A CN110334393A (en) | 2019-10-15 |
| CN110334393B true CN110334393B (en) | 2022-10-21 |
Family
ID=68140144
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910440909.1A Active CN110334393B (en) | 2019-05-24 | 2019-05-24 | Rail transit environment vibration prediction method, prediction system and vibration reduction measure evaluation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110334393B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113201982B (en) * | 2021-04-20 | 2021-11-02 | 北京城建设计发展集团股份有限公司 | Vibration reduction method of rail transit structure |
| CN113447220B (en) * | 2021-05-08 | 2022-12-20 | 北京市劳动保护科学研究所 | Analogy prediction method and system for indoor vibration of subway vehicle section upper cover building |
| CN113642167B (en) * | 2021-08-04 | 2023-09-08 | 北京市科学技术研究院城市安全与环境科学研究所 | Multi-measure combined rail transit environment vibration reduction effect estimation method, device and equipment |
| CN114358072B (en) * | 2021-12-30 | 2024-06-07 | 南昌大学 | Leakage flow structure vibration source identification method based on prototype actual measurement vibration response |
| CN115019825B (en) * | 2022-06-02 | 2024-07-05 | 国网江苏省电力有限公司电力科学研究院 | Acoustic channel digital twin method, device and system for underground tunnel environment |
| CN115357992B (en) * | 2022-10-19 | 2023-02-03 | 北京市科学技术研究院城市安全与环境科学研究所 | Method for predicting environmental vibration of co-constructed building of railway transportation hub station |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049670A (en) * | 2013-01-14 | 2013-04-17 | 哈尔滨工程大学 | Pipe excitation source identification and prediction method of vibration response to pipe excitation source |
| CN107220407A (en) * | 2017-04-27 | 2017-09-29 | 株洲中车时代电气股份有限公司 | A kind of converter cabinet Vibration Simulation model building method |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102680079A (en) * | 2012-04-18 | 2012-09-19 | 上海现代建筑设计(集团)有限公司 | Analysis method for ground surface vibration caused by subway traffic |
| CN102880767A (en) * | 2012-10-19 | 2013-01-16 | 西南交通大学 | Method for predicating noise simulation of rail transit bridge structure |
| JP2015085395A (en) * | 2013-10-28 | 2015-05-07 | 株式会社豊田中央研究所 | Processing error amount prediction device and processing error amount prediction program |
| CN106250653B (en) * | 2016-08-16 | 2019-02-22 | 北京交通大学 | A kind of full information high-precision transmission function prediction technique |
| CN106844978B (en) * | 2017-01-23 | 2020-05-01 | 中国机械工业集团有限公司 | Complex system vibration design method based on consistency of calculated and actually measured error spectrums |
| CN107256204B (en) * | 2017-04-12 | 2020-12-01 | 华侨大学 | Experimental device and method for multipoint vibration response frequency domain prediction based on transfer function |
| CN108681615A (en) * | 2018-03-19 | 2018-10-19 | 哈尔滨工程大学 | Offshore platform structure wideband line spectrum vibration noise Fast Prediction method |
-
2019
- 2019-05-24 CN CN201910440909.1A patent/CN110334393B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049670A (en) * | 2013-01-14 | 2013-04-17 | 哈尔滨工程大学 | Pipe excitation source identification and prediction method of vibration response to pipe excitation source |
| CN107220407A (en) * | 2017-04-27 | 2017-09-29 | 株洲中车时代电气股份有限公司 | A kind of converter cabinet Vibration Simulation model building method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110334393A (en) | 2019-10-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110334393B (en) | Rail transit environment vibration prediction method, prediction system and vibration reduction measure evaluation method | |
| Zhang et al. | A frequency–spatial domain decomposition (FSDD) method for operational modal analysis | |
| Hussein et al. | The fictitious force method for efficient calculation of vibration from a tunnel embedded in a multi-layered half-space | |
| Huang | Structural identification from ambient vibration measurement using the multivariate AR model | |
| Zheng et al. | Structural damage identification based on power spectral density sensitivity analysis of dynamic responses | |
| Friswell et al. | Is damage location using vibration measurements practical | |
| Zhu et al. | A sensitivity-based structural damage identification method with unknown input excitation using transmissibility concept | |
| CN106525226B (en) | Evaluation method and system based on-site vibration load recognition | |
| Ding et al. | Average acceleration discrete algorithm for force identification in state space | |
| Lam et al. | Identification of rail-sleeper-ballast system through time-domain Markov chain Monte Carlo-based Bayesian approach | |
| Guo et al. | A dynamic load estimation method for nonlinear structures with unscented Kalman filter | |
| Lin et al. | Modal identification from nonstationary ambient response data using extended random decrement algorithm | |
| Levenberg | Inverse analysis of viscoelastic pavement properties using data from embedded instrumentation | |
| Si et al. | Random vibration of an elastic half-space subjected to a moving stochastic load | |
| Zhao et al. | Nonstationary seismic response analysis of long-span structures by frequency domain method considering wave passage effect | |
| Zhang et al. | Adaptive substructure identification for shear structures with virtual control system | |
| CN106126837A (en) | A kind of soft base sluice sluice foundation comes to nothing area recognizing method | |
| CN109297576A (en) | A kind of train wheel rail force load acquisition methods | |
| Liu et al. | Substructural condition assessment based on force identification and interface force sensitivity | |
| Türker | Ambient vibration test of building base slab for different ground conditions | |
| Li et al. | Identification of structural damping in time domain | |
| Zhou et al. | Research on moving load identification based on measured acceleration and strain signals | |
| Dudzik et al. | Optimal dynamic error formula for charge output accelerometer obtained by the neural network | |
| Łukasiewicz et al. | Analysis of vibrodiagnostics methods in the technical state study of designed multimedia mobile scenes | |
| Cai et al. | Estimation of statistical energy analysis loss factor for fiber reinforced plastics plate of yachts |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 55 Taoranting Road, Xicheng District, Beijing 100054 Applicant after: Institute of urban safety and environmental science, Beijing Academy of science and technology Address before: 55 Taoranting Road, Xicheng District, Beijing 100054 Applicant before: BEIJING MUNICIPAL INSTITUTE OF LABOUR PROTECTION |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |