A method and system for evaluating the vibration isolation effect of existing buildings in a subway operation area

Abstract

The application discloses a kind of metro operation area existing building vibration isolation effect evaluation method and system, specifically related to building vibration isolation technical field;According to the density of metro operation area existing building, the monitoring area is divided, the vibration signal in fixed time period is captured in real time, the nonlinear characteristics in vibration signal and the response characteristics of building under vibration are analyzed, when detecting abnormal vibration, the reverse vibration wave error change condition calculated by analysis control algorithm is analyzed, based on fuzzy logic comprehensive analysis abnormal vibration behavior and reverse vibration wave error change condition, the vibration isolation effect in monitoring area is quality divided, generates evaluation report, and takes corresponding improvement measures;Can master the vibration condition of building comprehensively, accurately diagnose abnormal vibration, improve the precision and adaptability of control system, provide scientific vibration isolation effect evaluation, formulate effective improvement measures, so as to improve the overall vibration isolation quality, ensure the structural safety and use comfort of building.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building vibration isolation, in particular to a method and system for evaluating the vibration isolation effect of existing buildings in a subway operation area. BACKGROUND

[0002] The vibration isolation of existing buildings in a subway operation area refers to taking technical measures to reduce the impact of vibrations generated by subway trains on surrounding existing buildings. By installing vibration isolation devices or improving the structure of the building, the intensity of vibration transmitted to the building is reduced, thereby protecting the structural integrity and residential comfort of the building. Such vibration isolation measures include installing vibration isolation pads, shock absorbers and other equipment at key parts of the building foundation, floor and other parts, or improving the building design to improve its vibration resistance. The evaluation of the vibration isolation effect needs to be measured and analyzed through vibration data to determine whether additional measures need to be taken to protect the safety of the building and the comfort of the use environment.

[0003] The active vibration isolation system, as a commonly used vibration isolation device, effectively reduces the intensity of vibration transmitted to the building through real-time monitoring and feedback control, and is particularly suitable for environments with extremely high vibration control requirements. By monitoring the vibration signal in real time, the external vibration is detected by the sensor, and the corresponding reverse vibration wave parameters are calculated by the controller to drive the brake to generate a reverse vibration wave, thereby actively canceling the original vibration wave and achieving efficient vibration reduction and stable environment effect. However, the building may exhibit nonlinear response characteristics when subjected to strong vibrations, meaning that when the building is subjected to strong vibrations, its vibration response characteristics will change, resulting in abnormal vibration behavior. Traditional control algorithms based on linear assumptions may not accurately calculate the reverse vibration wave, resulting in ineffective vibration isolation and continuous vibration impact on the building generated by the subway operation. SUMMARY

[0004] The purpose of the present application is to provide a method and system for evaluating the vibration isolation effect of existing buildings in a subway operation area to solve the problems in the background art.

[0005] In order to achieve the above purpose, the present application provides the following technical scheme: a method for evaluating the vibration isolation effect of existing buildings in a subway operation area, comprising the following steps:

[0006] S1: dividing the existing buildings in the subway operation area into several monitoring areas according to their density, and installing n vibration sensors at different positions of the buildings in the monitoring area for real-time capture of vibration signals within a fixed time period;

[0007] S2: after preprocessing the captured vibration signals, analyzing the nonlinear characteristics in the vibration signals and the real-time response characteristics of the building under vibration, and identifying abnormal vibration behavior of the building;

[0008] S3: when the building appears abnormal vibration behavior, the error change of the control algorithm calculating the reverse vibration wave is analyzed, and the reverse vibration wave parameters are dynamically adjusted according to the analysis result;

[0009] S4: based on fuzzy logic, the abnormal vibration behavior of the building and the error change of the control algorithm calculating the reverse vibration wave are comprehensively analyzed, the vibration isolation effect in each monitoring area is quality divided according to the analysis result, and an evaluation report is generated, and corresponding improvement measures are taken according to the evaluation report.

[0010] Preferably, according to the density of the existing buildings in the subway operation area, the buildings are divided into several monitoring areas, which are divided into high-density monitoring area, medium-density monitoring area and low-density monitoring area according to the density of the buildings.

[0011] Preferably, the phase amplitude coupling index is generated according to the nonlinear characteristics in the vibration signal, and the method for obtaining the phase amplitude coupling index is:

[0012] The original vibration signal is preprocessed, and the preprocessed signal is frequency band decomposed, and the signal is decomposed into different frequency band components by using a band pass filter; the Hilbert transform is performed on each frequency band component to obtain a complex form of Hilbert signal, and the Hilbert transformed signal xi(t) is expressed as: xi(t)=Ai(t)e jθi(t) ; Wherein, Ai(t) is the instantaneous amplitude of the signal, θi(t) is the instantaneous phase of the signal, e and j are constants, the instantaneous phase θlow(t) of the low frequency component and the instantaneous amplitude Ahigh(t) of the high frequency component are selected, and the instantaneous phase and amplitude of each frequency band component are obtained by the first Hilbert transform;

[0013] The instantaneous amplitude Ahigh(t) of the high frequency component is Hilbert transformed to obtain the instantaneous phase θAhigh(t) of its envelope, the phase difference Δθ(t) between the low frequency phase θlow(t) and the instantaneous phase θAhigh(t) of the high frequency amplitude is calculated, and the phase difference Δθ(t) is expressed as: Δθ(t)=θlow(t)-θAhigh(t); The phase amplitude coupling index PACI is calculated, and the phase amplitude coupling index PACI is calculated using the phase difference Δθ(t), and the specific calculation expression is: Wherein, N is the number of time points, and Δθ(t) is the phase difference of the tth time point.

[0014] Preferably, the vibration signal amplitude distribution abnormality index is generated according to the signal real-time response characteristics of the building under vibration, and the method for obtaining the vibration signal amplitude distribution abnormality index is:

[0015] The vibration signal x(t) of each part of the building is denoised and filtered, and then wavelet transform is performed to obtain wavelet coefficients at different scales, and the wavelet transform is represented as: Wj,k = ∫x(t)ψj,k(t)dt; wherein Wj,k is the wavelet coefficient, representing the intensity of the signal at scale j and time position k, and ψj,k(t) is the wavelet base function. The amplitude of the wavelet coefficient at each scale and time position is calculated to obtain the local amplitude: Aj,k = |Wj,k|; wherein Aj,k is the local amplitude, representing the intensity of the vibration signal at a specific scale and time position. A detection threshold λ is set to identify abnormal points in the local amplitude Aj,k that are greater than or equal to the detection threshold λ, and an abnormal point set Ω is established, containing all the amplitude points identified as abnormal. The vibration signal amplitude distribution anomaly index is calculated, and the specific calculation expression is: M is the total number of detection points, and TMCK is the vibration signal amplitude distribution anomaly index.

[0016] Preferably, the phase amplitude coupling index and the vibration signal amplitude distribution anomaly index are converted into a first feature vector, and the first feature vector is used as the input of the machine learning model. The machine learning model predicts the building vibration behavior evaluation coefficient label as the prediction target to minimize the sum of prediction errors of all building vibration behavior evaluation coefficient labels as the training target. The machine learning model is trained until the sum of prediction errors converges, and the model training is stopped. The building vibration behavior evaluation coefficient is determined according to the model output result.

[0017] Preferably, the obtained building vibration behavior evaluation coefficient is compared with the pre-set building vibration behavior evaluation coefficient reference threshold value. If the building vibration behavior evaluation coefficient is greater than or equal to the pre-set building vibration behavior evaluation coefficient reference threshold value, the building has abnormal vibration behavior; if the building vibration behavior evaluation coefficient is less than the pre-set building vibration behavior evaluation coefficient reference threshold value, the building does not have abnormal vibration behavior.

[0018] Preferably, when the building has abnormal vibration behavior, the vibration data is recorded in real time according to the vibration sensors installed at different parts of the building, and the vibration data includes the original vibration signal and the reverse vibration wave signal; the error index is defined by comparing the reverse vibration wave calculated according to the control algorithm with the actually measured vibration signal.

[0019] The error index is used as a label to generate a corresponding label for each group of input data;

[0020] The collected data is divided into a training set and a test set;

[0021] The random forest model is trained using the training set data, and the input is the vibration signal feature and the output is the error index;

[0022] using the test set data to evaluate the performance of the model;

[0023] inputting the vibration signal features collected in real time into the trained random forest model to predict the error value of the current control algorithm;

[0024] judging the error change of the control algorithm for calculating the reverse vibration wave according to the error value of the control algorithm output by the model within a period of time.

[0025] Preferably, a dynamic adjustment strategy is designed to adjust the reverse vibration wave parameters according to the error change, and the error formula is: wherein Y x is the actual vibration value, EY x is the vibration value predicted by the model, and H is the sample quantity.

[0026] According to the error change, the reverse vibration wave parameters are adjusted gradually, the error value of the control algorithm output by the model within a period of time is collected, and a corresponding data set is established, the standard deviation of the data set is calculated, that is, the error fluctuation index is calculated, when the calculated error fluctuation index is greater than or equal to a preset threshold, the parameters are adjusted by a fixed increment, and the specific calculation expression is: Pnew=Pcurrent+ΔP; wherein ΔP is a fixed increment value, Pnew is the adjusted reverse vibration wave parameter, and Pcurrent is the reverse vibration wave parameter before adjustment.

[0027] Preferably, the building vibration behavior evaluation coefficient and the error fluctuation index of the control algorithm are taken as input items, and the vibration isolation effect in each monitoring area is taken as an output item.

[0028] The evaluation coefficient, the error fluctuation index and the vibration isolation effect are converted into fuzzy sets.

[0029] The actually measured vibration evaluation coefficient and the error fluctuation index are converted into member values in the fuzzy set.

[0030] According to the fuzzy rule base, the input fuzzy set is matched with the rules to obtain the corresponding fuzzy output.

[0031] Using the fuzzy reasoning method, the input items are mapped to the output items through the fuzzy rules to generate the fuzzy output result.

[0032] The fuzzy output is converted into a specific vibration isolation effect by the center value method.

[0033] The application also provides a subway operation area existing building vibration isolation effect evaluation system, which comprises a vibration signal acquisition module, an abnormal vibration identification module, a control algorithm analysis module and a vibration isolation effect evaluation module.

[0034] The vibration signal acquisition module: according to the density of existing buildings in the subway operation area, the buildings are divided into several monitoring areas, and n vibration sensors are installed at different positions of the buildings in the monitoring area to capture the vibration signals in real time within a fixed time period;

[0035] The abnormal vibration identification module: after preprocessing the captured vibration signals, the nonlinear characteristics in the vibration signals and the real-time response characteristics of the buildings under vibration are analyzed to identify the abnormal vibration behavior of the buildings;

[0036] The control algorithm analysis module: when the buildings exhibit abnormal vibration behavior, the error change of the control algorithm in calculating the reverse vibration wave is analyzed, and the reverse vibration wave parameters are dynamically adjusted according to the analysis results;

[0037] The vibration isolation effect evaluation module: based on fuzzy logic, the abnormal vibration behavior of the buildings and the error change of the control algorithm in calculating the reverse vibration wave are comprehensively analyzed, the vibration isolation effect in each monitoring area is quality-divided according to the analysis results, and an evaluation report is generated, and corresponding improvement measures are taken according to the evaluation report.

[0038] In the above technical solution, the technical effects and advantages provided by the present application are:

[0039] 1、The present application divides the building density into monitoring areas and installs vibration sensors to capture vibration signals in real time and perform preprocessing and analysis to identify abnormal vibration behavior of the buildings. When abnormal vibration is detected, the control algorithm dynamically adjusts the reverse vibration wave parameters, and through fuzzy logic comprehensive analysis of abnormal vibration behavior and reverse vibration wave error change, the vibration isolation effect in the monitoring area is quality-divided, an evaluation report is generated, and improvement measures are developed. This method can fully grasp the vibration situation of the buildings, accurately diagnose abnormal vibration behavior, improve the precision and adaptability of the control system, provide scientific vibration isolation effect evaluation, and effectively improve the overall vibration isolation quality, ensuring the structural safety and use comfort of the buildings.

[0040] 2、The present application uses nonlinear characteristic analysis, machine learning model prediction, and error dynamic adjustment strategy, so that the system can maintain high-efficiency vibration isolation effect when the buildings are subjected to strong vibration. In particular, based on fuzzy logic comprehensive analysis, the vibration isolation effect in each monitoring area can be accurately evaluated, a detailed evaluation report can be generated, and effective improvement measures can be taken according to the evaluation results. Overall, the present application has significant advantages in solving the limitations of traditional vibration isolation control systems, effectively reducing the impact of subway vibration on buildings, and improving the scientificity and practicality of vibration isolation control. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0042] Figure 1 The method flowchart of the present application.

[0043] Figure 2 The system module diagram of the present application. DETAILED DESCRIPTION

[0044] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0045] Embodiment 1

[0046] Please refer to Figure 1 As shown in the figure, the building vibration isolation effect evaluation method for the subway operation area in the embodiment includes the following steps:

[0047] S1: According to the density of the existing buildings in the subway operation area, the buildings are divided into several monitoring areas, and n vibration sensors are installed at different positions of the buildings in the monitoring areas, which are used to capture vibration signals in a fixed time period in real time;

[0048] S2: After preprocessing the captured vibration signals, the nonlinear characteristics in the vibration signals and the real-time response characteristics of the signals of the buildings under vibration are analyzed, and the abnormal vibration behavior of the buildings is identified;

[0049] S3: When the abnormal vibration behavior of the buildings occurs, the error change of the reverse vibration wave calculated by the control algorithm is analyzed, and the reverse vibration wave parameters are dynamically adjusted according to the analysis result;

[0050] S4: Based on fuzzy logic, the abnormal vibration behavior of the buildings and the error change of the reverse vibration wave calculated by the control algorithm are comprehensively analyzed, the vibration isolation effect in each monitoring area is quality divided according to the analysis result, and an evaluation report is generated. According to the evaluation report, corresponding improvement measures are taken.

[0051] In S1, the subway operation area is divided into several monitoring areas according to the density of existing buildings, and n vibration sensors are installed at different positions of the buildings in the monitoring area to capture vibration signals in real time within a fixed time period.

[0052] According to the density of existing buildings in the subway operation area, it is divided into several monitoring areas, which are divided into high-density monitoring areas, medium-density monitoring areas and low-density monitoring areas according to the density of buildings, specifically:

[0053] High-density monitoring area: the area where buildings are densely distributed in the subway operation area, such as commercial centers, residential areas, etc. The monitoring area here is divided into small areas to ensure that each building is fully monitored.

[0054] Medium-density monitoring area: the area where buildings are relatively dispersed but still have a certain density, such as comprehensive office areas or light industrial areas. The size of the monitoring area is moderate to ensure that all important buildings are covered.

[0055] Low-density monitoring area: the area where buildings are less, such as parks, open spaces, warehouse areas, etc. The monitoring area here is divided into larger areas, but it still needs to ensure that key buildings are covered.

[0056] In order to effectively monitor the vibration impact of subway operation on existing buildings, sensors should be installed at key positions. Install sensors at the foundation and base of buildings in each monitoring area to monitor the vibration transmitted by the ground; install sensors at key structural support points such as load-bearing walls, columns and beams to monitor the vibration response of the structure; install sensors on the floors and ceilings of each floor to capture vertical vibration and overall structural response.

[0057] In addition, sensors should be arranged at different heights. Install dense sensors on the ground floor because the ground floor is directly affected by subway vibration; install sensors on the middle floors to understand the propagation and attenuation of vibration within the building; install sensors on the top floor to monitor the response of vibration at high levels, ensuring comprehensive coverage of vibration monitoring and accuracy of data.

[0058] In order to ensure accurate capture of the vibration impact of subway operation on buildings, fixed time period monitoring should be carried out. Sensors need to capture and record vibration signals in real time, achieving uninterrupted monitoring; at the same time, the signal is processed in segments, such as hourly, daily, weekly vibration signals, in order to carry out detailed analysis and comparison.

[0059] In addition, in order to improve the monitoring accuracy, the sensor should use high sampling rate (such as 100 times per second or more) to ensure that high-frequency vibration and subtle changes can be captured. Ensure that all sensors collect data synchronously, so that data from different positions can be compared and analyzed in time sequence, providing comprehensive and accurate vibration monitoring information.

[0060] S2: After preprocessing the captured vibration signal, the nonlinear characteristics in the vibration signal and the real-time response characteristics of the building under vibration are analyzed to identify abnormal vibration behavior of the building.

[0061] The captured vibration signal is preprocessed, including noise removal, filtering and smoothing, to improve signal quality and accuracy. The preprocessed signal is more pure, eliminating interference factors and providing a reliable basis for subsequent analysis.

[0062] After preprocessing the signal, feature extraction is performed on the signal, and through frequency spectrum analysis, time domain analysis and wavelet transform, the key features of the vibration signal such as frequency components, amplitude, energy density, etc. are extracted, providing detailed data information for evaluating the impact of subway vibration on buildings.

[0063] The phase amplitude coupling index is generated according to the nonlinear characteristics in the vibration signal, and the method for obtaining the phase amplitude coupling index is as follows:

[0064] The original vibration signal is preprocessed, and the preprocessed signal is decomposed into different frequency band components, usually using a bandpass filter;

[0065] Perform Hilbert transform on each frequency band component to obtain a complex form of Hilbert signal, and the Hilbert transformed signal xi(t) is expressed as: xi(t) = Ai(t)e jθi(t) ; Wherein Ai(t) is the instantaneous amplitude of the signal, θi(t) is the instantaneous phase of the signal, e, j are constants, the instantaneous phase of the low frequency component θlow(t) and the instantaneous amplitude of the high frequency component Ahigh(t) are selected, and the instantaneous phase and amplitude of each frequency band component are obtained through the first Hilbert transform;

[0066] Perform Hilbert transform on the instantaneous amplitude Ahigh(t) of the high frequency component to obtain the instantaneous phase θAhigh(t) of its envelope, calculate the phase difference Δθ(t) between the low frequency phase θlow(t) and the instantaneous phase θAhigh(t) of the high frequency amplitude, and the phase difference Δθ(t) is expressed as: Δθ(t) = θlow(t)-θAhigh(t); Calculate the phase amplitude coupling index PACI, use the phase difference Δθ(t) to calculate the phase amplitude coupling index PACI, and the specific calculation expression is: Wherein N is the number of time points, and Δθ(t) is the phase difference of the tth time point.

[0067] The greater the phase-amplitude coupling index (PACI), the higher the degree of coupling between the low-frequency phase and the high-frequency amplitude in the vibration signal. This means that the phase change of low-frequency vibration significantly affects the amplitude change of high-frequency vibration, showing strong phase modulation phenomena. Such phenomena usually indicate that the vibration behavior of the system is more complex and nonlinear, and there may be strong nonlinear effects or resonance phenomena.

[0068] A high phase-amplitude coupling index value indicates that there is significant abnormal vibration behavior in the vibration signal, such as resonance, harmonic amplification, etc. These abnormal vibration behaviors will make the vibration response of the system more unpredictable and difficult to accurately describe by a linear model. In particular, in buildings in the running area of the subway, strong phase-amplitude coupling can cause the building structure to be subjected to greater vibration impact, increasing the risk of structural damage.

[0069] Therefore, by monitoring and analyzing the phase-amplitude coupling index, abnormal behavior in the vibration signal can be identified and quantified, providing an important basis for taking targeted vibration isolation measures. A high phase-amplitude coupling index value suggests that special attention and treatment of these strong nonlinear vibration effects are needed to ensure the structural safety and use comfort of the building.

[0070] According to the signal real-time response characteristics of the building under vibration, a vibration signal amplitude distribution anomaly index is generated, and the method for obtaining the vibration signal amplitude distribution anomaly index is:

[0071] After denoising and filtering the vibration signal x(t) of each part of the building, wavelet transform is performed to obtain wavelet coefficients at different scales. The wavelet transform is represented as: Wj,k = ∫x(t)ψj,k(t)dt; where Wj,k is the wavelet coefficient, representing the intensity of the signal at scale j and time position k, ψj,k(t) is the wavelet basis function, the amplitude of the wavelet coefficient at each scale and time position is calculated to obtain the local amplitude: Aj,k = |Wj,k|; where Aj,k is the local amplitude, representing the intensity of the vibration signal at a specific scale and time position. Set a detection threshold λ to identify abnormal amplitudes, identify abnormal points in the local amplitude Aj,k that are greater than or equal to the detection threshold λ, and establish an abnormal point set Ω containing all amplitude points identified as abnormal. Calculate the vibration signal amplitude distribution anomaly index, and the specific calculation expression is: M is the total number of detection points, and TMCK is the vibration signal amplitude distribution anomaly index.

[0072] When the amplitude distribution anomaly index of the vibration signal is larger, it indicates that there are a large number of vibration points with abnormally high amplitude in the vibration signal. These high-amplitude vibrations can cause the structural components of the building, such as beams, columns and walls, to bear stresses and strains far exceeding the design standards, increasing the possibility of structural damage. High-amplitude vibrations can also cause secondary structures (such as glass windows and interior walls) to break or fall off, affecting the safety and functionality of the building.

[0073] When the amplitude distribution anomaly index of the vibration signal is larger, not only are the amplitude of the abnormal vibration points higher, but also the number is larger. This means that the frequency of vibration anomalies is higher, indicating that the impact of the vibration source on the building is more frequent. Frequent abnormal vibrations can cause structural fatigue, increase the risk of material aging and damage, and shorten the service life of the building. Long-term exposure to high-frequency abnormal vibrations can cause structural problems such as cracks and settlement.

[0074] A higher amplitude distribution anomaly index of the vibration signal also indicates that the vibration signal contains complex nonlinear characteristics, and the vibration behavior becomes difficult to predict. This complexity may be due to the superposition effect of multiple vibration sources, the nonlinear response of the structure itself, and other factors. Complex vibration behavior makes it difficult for traditional linear vibration isolation and damping methods to be effective, and more advanced nonlinear analysis methods and intelligent control techniques are needed to cope with it to ensure the safety and stability of the building in a complex vibration environment.

[0075] The phase amplitude coupling index and the vibration signal amplitude distribution anomaly index are converted into a first feature vector, the first feature vector is taken as the input of the machine learning model, the machine learning model takes each group of first feature vectors as the prediction target to predict the building vibration behavior evaluation coefficient label, minimizes the sum of prediction errors of all building vibration behavior evaluation coefficient labels as the training target, trains the machine learning model until the sum of prediction errors converges to stop model training, and determines the building vibration behavior evaluation coefficient according to the model output result.

[0076] The method for obtaining the building vibration behavior evaluation coefficient is: obtaining the corresponding function expression CT=f1(PACI, TMCK) from the first feature vector training data of the trained machine learning model; in the formula, f1 is the output function of the model, PACI is the phase amplitude coupling index, TMCK is the vibration signal amplitude distribution anomaly index, and CT is the building vibration behavior evaluation coefficient.

[0077] The obtained building vibration behavior evaluation coefficient is compared with the pre-set building vibration behavior evaluation coefficient reference threshold value. If the building vibration behavior evaluation coefficient is greater than or equal to the pre-set building vibration behavior evaluation coefficient reference threshold value, it indicates that the building has abnormal vibration behavior; if the building vibration behavior evaluation coefficient is less than the pre-set building vibration behavior evaluation coefficient reference threshold value, it indicates that the building does not have abnormal vibration behavior.

[0078] S3: When the building has abnormal vibration behavior, analyze the error change of the control algorithm for calculating the reverse vibration wave, and dynamically adjust the reverse vibration wave parameters according to the analysis result.

[0079] When the building has abnormal vibration behavior, real-time vibration data is recorded according to the vibration sensors installed at different parts of the building, including original vibration signals and reverse vibration wave signals. Continuous collection of vibration data ensures coverage of various operating conditions and environmental conditions.

[0080] The collected vibration signals are denoised and filtered to remove random noise and interference signals. Key features such as frequency, amplitude, phase, acceleration, etc. are extracted from the pre-processed signals.

[0081] Compare the reverse vibration wave calculated by the control algorithm with the actual measured vibration signal, and define error indicators (such as mean square error, correlation coefficient, etc.).

[0082] Use the error indicators as labels to generate corresponding labels for each group of input data.

[0083] The collected data is divided into training set and test set, usually divided according to 80 / 20 ratio.

[0084] Use the training set data to train the random forest model, the input is the vibration signal feature, and the output is the error indicator.

[0085] Use the test set data to evaluate the performance of the model, calculate the accuracy, recall rate, F1 score and other indicators of the model.

[0086] Input the real-time collected vibration signal features into the trained random forest model to predict the error value of the current control algorithm.

[0087] According to the error value of the control algorithm output by the model within a period of time, judge the error change of the control algorithm for calculating the reverse vibration wave.

[0088] Design a dynamic adjustment strategy to adjust the reverse vibration wave parameters according to the error change, the error formula is: Where Y x is the actual vibration value, EY x is the vibration value predicted by the model, and H is the sample size.

[0089] According to the error change, the reverse vibration wave parameters are adjusted step by step, the error values of the control algorithm output by the model in a period of time are collected, and a corresponding data set is established, the standard deviation of the data set is calculated, that is, the error fluctuation index is calculated, when the calculated error fluctuation index is greater than or equal to the preset threshold, the parameters are adjusted by a fixed increment, and the specific calculation expression is: Pnew=Pcurrent+ΔP; wherein, ΔP is a fixed increment value, Pnew is the adjusted reverse vibration wave parameter, and Pcurrent is the reverse vibration wave parameter before adjustment. The adjusted parameters are immediately fed back to the control system, the reverse vibration wave signal is updated in real time, the adjusted vibration response is continuously monitored, and the effect after parameter adjustment is recorded.

[0090] S4: Based on fuzzy logic, the abnormal vibration behavior of the building and the error change of the control algorithm calculated reverse vibration wave are comprehensively analyzed, the vibration isolation effect in each monitoring area is quality divided according to the analysis result, and an evaluation report is generated, and corresponding improvement measures are taken according to the evaluation report.

[0091] The building vibration behavior evaluation coefficient and the error fluctuation index of the control algorithm are taken as input items, and the vibration isolation effect in each monitoring area is taken as an output item.

[0092] The evaluation coefficient is converted into a fuzzy set, for example, the evaluation coefficient is divided into "low", "medium" and "high" three levels.

[0093] The error fluctuation index is converted into a fuzzy set, for example, the error is divided into "stable", "fluctuation" and "severe" three levels.

[0094] The vibration isolation effect of the building in each monitoring area, including vibration reduction rate, structure stability, resident comfort, etc. The vibration isolation effect is converted into a fuzzy set, for example, the vibration isolation effect is divided into "poor", "general" and "good" three levels.

[0095] According to different combinations of input items, fuzzy rules are formulated. For example: if the vibration evaluation coefficient is "high" and the error fluctuation index is "severe", the vibration isolation effect is "poor". If the vibration evaluation coefficient is "medium" and the error fluctuation index is "fluctuation", the vibration isolation effect is "general". If the vibration evaluation coefficient is "low" and the error fluctuation index is "stable", the vibration isolation effect is "good".

[0096] The actually measured vibration evaluation coefficient and error fluctuation index are converted into member values in the fuzzy set.

[0097] According to the fuzzy rule base, the input fuzzy set is matched with the rules to obtain the corresponding fuzzy output.

[0098] The input items are mapped to the output items through fuzzy rules using a fuzzy inference method (such as Mamdani inference or Sugeno inference), generating a fuzzy output result.

[0099] The fuzzy output is converted to a specific isolation effect by the center value method.

[0100] The vibration evaluation coefficient, error fluctuation index, and their fuzzy classification results are displayed. The isolation effect evaluation results of each monitoring area are provided, including charts and data analysis.

[0101] More isolation pads, shock absorbers, and other equipment are installed in monitoring areas with poor isolation effect to reduce vibration transmission. The building structure is reinforced to enhance its vibration resistance.

[0102] According to the error fluctuation index in the evaluation report, the parameters of the control algorithm are adjusted to make the generation of counter-vibration waves more accurate. An adaptive control algorithm is introduced to enable the system to automatically optimize the counter-vibration wave parameters based on real-time vibration data. Regular vibration data collection and analysis are conducted to evaluate the effectiveness of improvement measures, and the isolation facilities and control algorithm are dynamically adjusted to ensure the long-term stability and safety of the building.

[0103] In this embodiment, the existing buildings in the subway operation area are divided into several monitoring areas according to their density, and n vibration sensors are installed at different positions of the buildings in the monitoring areas to capture vibration signals in a fixed time period in real time. After preprocessing the captured vibration signals, the nonlinear characteristics and real-time response characteristics of the signals under vibration are analyzed to identify abnormal vibration behavior of the buildings. When abnormal vibration behavior of the buildings occurs, the error change of the control algorithm in calculating counter-vibration waves is analyzed, and the parameters of the counter-vibration waves are dynamically adjusted based on the analysis results. Based on fuzzy logic, the abnormal vibration behavior of the buildings and the error change of the control algorithm in calculating counter-vibration waves are comprehensively analyzed, the isolation effect in each monitoring area is quality-divided based on the analysis results, and an evaluation report is generated. According to the evaluation report, corresponding improvement measures are taken. This can comprehensively grasp the vibration situation of the buildings, accurately diagnose abnormal vibration behavior, improve the precision and adaptability of the control system, provide scientific isolation effect evaluation, and develop effective improvement measures to improve the overall isolation quality and ensure the structural safety and use comfort of the buildings.

[0104] Embodiment 2

[0105] Please refer to Figure 2 The subway operation area existing building isolation effect evaluation described in this embodiment includes a vibration signal acquisition module, an abnormal vibration identification module, a control algorithm analysis module, and an isolation effect evaluation module.

[0106] The vibration signal acquisition module: according to the density of the existing buildings in the subway operation area, the buildings are divided into several monitoring areas, and n vibration sensors are installed at different positions of the buildings in the monitoring areas, which are used to capture vibration signals in a fixed time period in real time;

[0107] The abnormal vibration identification module: after preprocessing the captured vibration signals, the nonlinear characteristics in the vibration signals and the real-time response characteristics of the buildings under vibration are analyzed to identify the abnormal vibration behavior of the buildings;

[0108] The control algorithm analysis module: when the buildings have abnormal vibration behavior, the error change of the reverse vibration wave calculated by the control algorithm is analyzed, and the reverse vibration wave parameters are dynamically adjusted according to the analysis result;

[0109] The vibration isolation effect evaluation module: based on fuzzy logic, the abnormal vibration behavior of the buildings and the error change of the reverse vibration wave calculated by the control algorithm are comprehensively analyzed, the vibration isolation effect in each monitoring area is quality divided according to the analysis result, and an evaluation report is generated, and corresponding improvement measures are taken according to the evaluation report.

[0110] The above formulas are dimensionless values, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the nearest real situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0111] The above embodiments can be realized by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0112] It should be understood that the term "and / or" in this document is only used to describe associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after it, but it can also represent an "and / or" relationship. The specific meaning can be understood according to the context before and after it.

[0113] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0114] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0115] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for evaluating the vibration isolation effect of existing buildings in a subway operating area, characterized in that: Includes the following steps; S1: Divide the subway operating area into several monitoring zones based on the density of existing buildings. Install n vibration sensors at different locations of buildings within the monitoring zones to capture vibration signals in real time over a fixed period of time. S2: After preprocessing the captured vibration signals, analyze the nonlinear characteristics of the vibration signals and the real-time signal response characteristics of the building under vibration to identify abnormal vibration behavior of the building. S3: When the building exhibits abnormal vibration behavior, analyze the error changes in the control algorithm's calculation of the reverse vibration wave, and dynamically adjust the reverse vibration wave parameters based on the analysis results; A dynamic adjustment strategy is designed to adjust the reverse vibration wave parameters according to the error changes. The error formula is: ;in, This is the actual vibration value. H represents the vibration value predicted by the model, and H is the number of samples. Based on the error changes, the reverse vibration wave parameters are gradually adjusted. The error values ​​of the control algorithm output by the model over a period of time are collected, and a corresponding dataset is established. The standard deviation of the dataset is calculated, i.e., the error fluctuation index is calculated. When the calculated error fluctuation index is greater than or equal to a preset threshold, the parameters are adjusted by a fixed increment. The specific calculation expression is as follows: ;in, It is a fixed increment value. The adjusted reverse vibration wave parameters, The parameters of the reverse vibration wave before adjustment; S4: Based on fuzzy logic, the abnormal vibration behavior of the building and the error change of the reverse vibration wave calculated by the control algorithm are comprehensively analyzed. According to the analysis results, the vibration isolation effect in each monitoring area is classified by quality, and an evaluation report is generated. Based on the evaluation report, corresponding improvement measures are taken.

2. The method for evaluating the vibration isolation effect of existing buildings in a subway operating area according to claim 1, characterized in that: The subway operating area is divided into several monitoring zones based on the density of existing buildings, and further divided into high-density monitoring zones, medium-density monitoring zones, and low-density monitoring zones according to building density.

3. The method for evaluating the vibration isolation effect of existing buildings in a subway operating area according to claim 1, characterized in that: The phase-amplitude coupling index is generated based on the nonlinear characteristics of the vibration signal. The method for obtaining the phase-amplitude coupling index is as follows: The original vibration signal is preprocessed, and then the preprocessed signal is decomposed into frequency bands using a bandpass filter. A Hilbert transform is then applied to each frequency band component to obtain a complex Hilbert signal. The Hilbert-transformed signal xi(t) is expressed as: Where Ai(t) is the instantaneous amplitude of the signal. The instantaneous phase of the signal is θlow(t), and e and j are constants. The instantaneous phase of the low-frequency component θlow(t) and the instantaneous amplitude of the high-frequency component Ahigh(t) are selected. Through the first Hilbert transform, the instantaneous phase and amplitude of each frequency band component are obtained. Perform a Hilbert transform on the instantaneous amplitude Ahigh(t) of the high-frequency component to obtain its instantaneous phase θAhigh(t). Calculate the phase difference Δθ(t) between the low-frequency phase θlow(t) and the instantaneous phase θAhigh(t) of the high-frequency amplitude. The phase difference Δθ(t) is expressed as: Δθ(t) = θlow(t) − θAhigh(t). Calculate the phase-amplitude coupling index PACI using the phase difference Δθ(t). The specific calculation expression is as follows: Where N is the number of time points, and Δθ(t) is the phase difference at the t-th time point.

4. The method for evaluating the vibration isolation effect of existing buildings in a subway operating area according to claim 3, characterized in that: The vibration signal amplitude distribution anomaly index is generated based on the real-time signal response characteristics of a building under vibration. The method for obtaining the vibration signal amplitude distribution anomaly index is as follows: After denoising and filtering the vibration signals x(t) from various parts of the building, wavelet transform is performed to obtain wavelet coefficients at different scales. The wavelet transform is expressed as: ;in, These are wavelet coefficients, representing the signal intensity at scale j and time position k. These are wavelet basis functions. The amplitude of the wavelet coefficients at each scale and time location is calculated to obtain the local amplitude. Where Aj,k is the local amplitude, representing the intensity of the vibration signal at a specific scale and time location. A detection threshold λ is set, and abnormal points in the local amplitude Aj,k that are greater than or equal to the detection threshold λ are identified. An abnormal point set Ω is established, containing all amplitude points identified as abnormal. The vibration signal amplitude distribution anomaly index is calculated, and the specific calculation expression is as follows: M is the total number of detection points, and TMCK is the vibration signal amplitude distribution anomaly index.

5. The method for evaluating the vibration isolation effect of existing buildings in a subway operating area according to claim 4, characterized in that: The phase amplitude coupling index and the vibration signal amplitude distribution anomaly index are converted into the first feature vector. The first feature vector is used as the input of the machine learning model. The machine learning model uses the prediction of the building vibration behavior evaluation coefficient label for each set of first feature vectors as the prediction objective. The training objective is to minimize the sum of prediction errors for all building vibration behavior evaluation coefficient labels. The machine learning model is trained until the sum of prediction errors converges and the model training stops. The building vibration behavior evaluation coefficient is determined based on the model output.

6. The method for evaluating the vibration isolation effect of existing buildings in a subway operating area according to claim 5, characterized in that: The obtained building vibration behavior evaluation coefficient is compared with the pre-set building vibration behavior evaluation coefficient reference threshold. If the building vibration behavior evaluation coefficient is greater than or equal to the pre-set building vibration behavior evaluation coefficient reference threshold, the building has abnormal vibration behavior; if the building vibration behavior evaluation coefficient is less than the pre-set building vibration behavior evaluation coefficient reference threshold, the building does not have abnormal vibration behavior.

7. The method for evaluating the vibration isolation effect of existing buildings in a subway operating area according to claim 1, characterized in that: When a building exhibits abnormal vibration behavior, vibration data is recorded in real time by vibration sensors installed at different parts of the building. The vibration data includes the original vibration signal and the reverse vibration wave signal. The reverse vibration wave calculated by the control algorithm is compared with the actual measured vibration signal to define an error index. Use the error index as a label to generate a corresponding label for each set of input data. The collected data is divided into training and testing sets; The random forest model is trained using the training set data, with vibration signal features as input and error index as output. Evaluate the model's performance using test set data; The vibration signal features collected in real time are input into the trained random forest model to predict the error value of the current control algorithm; Based on the error value of the control algorithm output by the model over a period of time, determine the error change of the control algorithm in calculating the reverse vibration wave.

8. The method for evaluating the vibration isolation effect of existing buildings in a subway operating area according to claim 1, characterized in that: The building vibration behavior evaluation coefficient and the error fluctuation index of the control algorithm are used as input items, and the vibration isolation effect in each monitoring area is used as the output item. The evaluation coefficients, error fluctuation index, and vibration isolation effect are converted into fuzzy sets. The actual measured vibration evaluation coefficients and error fluctuation index are converted into member values ​​in a fuzzy set; Based on the fuzzy rule base, the input fuzzy set is matched with rules to obtain the corresponding fuzzy output; Using fuzzy inference, input items are mapped to output items through fuzzy rules to generate fuzzy output results; The fuzzy output is converted into a specific vibration isolation effect by using the center value method.

9. A vibration isolation effect evaluation system for existing buildings in a subway operating area, used to implement the vibration isolation effect evaluation method for existing buildings in a subway operating area as described in any one of claims 1-8, characterized in that: It includes a vibration signal acquisition module, an abnormal vibration identification module, a control algorithm analysis module, and a vibration isolation effect evaluation module; Vibration signal acquisition module: The subway operating area is divided into several monitoring areas according to the density of existing buildings. n vibration sensors are installed at different locations of buildings within the monitoring area to capture vibration signals in real time within a fixed time period. Abnormal vibration identification module: After preprocessing the captured vibration signals, it analyzes the nonlinear characteristics of the vibration signals and the real-time signal response characteristics of the building under vibration to identify the abnormal vibration behavior of the building; Control algorithm analysis module: When a building exhibits abnormal vibration behavior, the module analyzes the error changes in the reverse vibration wave calculated by the control algorithm and dynamically adjusts the reverse vibration wave parameters based on the analysis results. Vibration isolation effect evaluation module: Based on fuzzy logic, it comprehensively analyzes the abnormal vibration behavior of the building and the error changes of the reverse vibration wave calculated by the control algorithm. Based on the analysis results, the vibration isolation effect of each monitoring area is classified into quality categories, and an evaluation report is generated. Based on the evaluation report, corresponding improvement measures are taken.

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