Method for evaluating dynamic comfort level of engineering structure under wind load effect

Abstract

The invention discloses a method for evaluating the dynamic comfort degree of an engineering structure under the wind load effect. The method comprises the steps that dynamic response data of a high-rise building under the wind load effect are collected and obtained; decomposing the acceleration time sequence signal into a plurality of intrinsic mode signals based on an empirical mode decomposition method, and obtaining an instantaneous frequency and an instantaneous amplitude of each intrinsic mode signal based on Hilbert transform; according to the instantaneous frequency and the instantaneous amplitude of each intrinsic mode signal, obtaining the weighted average instantaneous frequency and the maximum instantaneous amplitude of each moment; and based on a Kalman filtering algorithm and introducing an accumulated error correction mechanism, acquiring estimated displacement data of the engineering structure at each moment under the wind load action according to the acquired acceleration time sequence signal, and acquiring a comprehensive comfort level score of each moment so as to realize evaluation of the dynamic comfort level of the engineering structure under the wind load action. The method solves the problem that the evaluation of the dynamic comfort of the engineering structure under the action of the wind load cannot be accurately realized because the nonlinear and non-stable dynamic response characteristics are neglected and the vibration generated by the wind load always has the characteristics of randomness and time variation.

Description

Technical Field

[0001] The invention relates to the technical field of human comfort evaluation of high-rise buildings, and in particular to a method for evaluating the dynamic comfort of an engineering structure under wind load. Background Art

[0002] In the field of high-rise buildings, the dynamic response under wind loads can cause significant vibrations, affecting structural stability and human comfort. As the building height increases, the vibration caused by wind loads may produce uncomfortable lateral swings and acceleration fluctuations.

[0003] The current traditional methods such as acceleration threshold method and Fourier transform are difficult to effectively evaluate the instantaneous impact of wind-induced vibration on the human body, because they only consider the overall frequency characteristics or fixed thresholds, ignoring the nonlinear and non-stationary dynamic response characteristics. The vibrations generated by wind loads are often random and time-varying, making it impossible to accurately evaluate the dynamic comfort of engineering structures under wind loads. Summary of the invention

[0004] The present invention provides a method for evaluating the dynamic comfort of an engineering structure under wind load to overcome the above technical problems.

[0005] In order to achieve the above object, the technical solution of the present invention is:

[0006] A method for evaluating the dynamic comfort of an engineering structure under wind loads specifically comprises the following steps:

[0007] S1: Collect and obtain dynamic response data of high-rise buildings under wind loads;

[0008] The dynamic response data is: acceleration time series signals collected by acceleration sensors installed at key locations of high-rise buildings;

[0009] The key parts of the high-rise building include the centroid position or structural joint / connection position of the high-rise building

[0010] S2: Decompose the acceleration time series signal into several intrinsic mode signals based on the empirical mode decomposition method;

[0011] Based on the Hilbert transform, feature extraction is performed on each intrinsic mode signal to obtain the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal;

[0012] S3: according to the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal, obtain the weighted average instantaneous frequency and the maximum instantaneous amplitude at each moment;

[0013] S4: Based on the Kalman filter algorithm and the introduction of the accumulated error correction mechanism, the estimated displacement data of the engineering structure at each moment under the action of wind load is obtained according to the collected acceleration time series signal;

[0014] S5: The acquired acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude and estimated displacement data are proportionally normalized, and weighted calculation is performed on the normalized data to obtain the comprehensive comfort score at each moment, so as to realize the dynamic comfort evaluation of the engineering structure under wind load.

[0015] Further, the S2 specifically includes the following steps:

[0016] S21: Based on the empirical mode decomposition method, the acceleration time series signal is decomposed into several intrinsic mode signals, whose expression is:

[0017] IMF i =a(t)-m(t)

[0018] Where: IMF i represents the i-th intrinsic mode function and is IMF i The simplified form of (t) is the intrinsic mode signal; m(t) represents the local mean of the acceleration time series signal; a(t) represents the dynamic response data of the high-rise building under wind load;

[0019] S22: Perform Hilbert transform on each intrinsic mode signal, and its expression is:

[0020]

[0021] Where: H[IMF i (t)] represents the intrinsic mode signal IMF i (t); τ represents the integral variable over the entire time range of the signal; t represents the time variable;

[0022] S23: Extract the features of the intrinsic mode signal after Hilbert transformation to obtain the instantaneous frequency and instantaneous amplitude, which are expressed as follows:

[0023]

[0024] A(t)=|H[IMF i (t)]|

[0025] Where: φ(t) represents the Hilbert transform phase; f(t) represents the instantaneous frequency extracted from each intrinsic mode function; A(t) represents the instantaneous amplitude extracted from each intrinsic mode function.

[0026] Furthermore, in S3, the weighted average instantaneous frequency and the maximum instantaneous amplitude at each moment are obtained according to the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal, and the expression is:

[0027]

[0028] A max =max(A i (t))

[0029] Where: represents the weighted average instantaneous frequency at each moment, f i (t) represents the instantaneous frequency of the ith eigenmode function extraction and f i (t)∈f(t); N represents the total number of eigenmode functions; A i (t) represents the instantaneous amplitude of the i-th eigenmode function extraction and A i (t)∈A(t); A max Indicates the maximum instantaneous amplitude; w i (t) represents the weight determined by the instantaneous amplitude of each mode.

[0030] Furthermore, in S4, based on the Kalman filter algorithm, the estimated displacement data of the engineering structure at each moment under the action of wind load is obtained according to the collected acceleration time series signal, specifically including

[0031] S41: Establish the state equation of the dynamic response of high-rise buildings under wind loads;

[0032] And the expression of the dynamic response state equation is

[0033] x k =Ax k-1 +Bu k +w k

[0034] Where: x k represents the state vector at time k regarding the horizontal displacement and velocity of the high-rise building and A represents the state transfer matrix; B represents the control input matrix; u k represents the control input vector - acceleration signal; w k represents process noise;

[0035] S43: Initialize parameters of the Kalman filter algorithm;

[0036] And the initialized parameters include the state transfer matrix A:

[0037] Control input matrix B:

[0038] Observation matrix H: H =

[10] ;

[0039] Covariance Q of process noise:

[0040] Observation noise covariance R: a scalar set according to empirical values;

[0041] Error covariance P:

[0042] State vector x: Where h(0) represents the initial estimated value of the current horizontal displacement of the high-rise building; v(0) represents the initial estimated value of the current velocity of the high-rise building;

[0043] S44: Based on the Kalman filter algorithm, the prediction estimation and error covariance of the dynamic response state of high-rise buildings under wind loads are realized according to the state observation equation;

[0044] The expression for the prediction estimate of the dynamic response state is:

[0045]

[0046] Where: k represents the time; represents the predicted estimate of the dynamic response state at time k; represents the updated state estimate at time k-1;

[0047] The error covariance is expressed as

[0048] P k|k-1 =AP k-1|k-1 A T +Q

[0049] Where: P k|k-1 represents the prediction error covariance matrix at time k; P k-1|k-1 represents the update error covariance matrix at time k-1; Q represents the process noise covariance matrix, that is, the process noise in the dynamic response state equation; A T represents the transpose of A;

[0050] S45: The measurement equation of a high-rise building under wind load is obtained based on the dynamic response state equation, which is expressed as follows:

[0051] z k =Hx k +v k

[0052] Where: z k represents the measured horizontal displacement;

[0053] S46: According to the update formula of the error covariance, the Kalman gain of the Kalman filter algorithm is obtained; and the expression of the Kalman gain is

[0054] K k =P k|k-1 ·H T ·(H·Pk|k-1 ·H T +R) -1

[0055] Where: K k represents the Kalman gain matrix; R represents the observation noise covariance matrix; H T represents the transpose of H;

[0056] S47: Based on the Kalman gain, the current state estimation update equation of the high-rise building is obtained according to the measurement equation and the predicted estimation of the dynamic response state;

[0057] And the expression of the current state estimation update equation is

[0058]

[0059] Where: represents the updated state estimate at time k; Residuals represent the difference between the measured and predicted values.

[0060] And the state estimate of the current state estimate update equation is used as the dynamic response of the high-rise building in the current iteration;

[0061] Set the cumulative update iteration threshold and confirm whether the cumulative update iteration number in the current iteration reaches the cumulative update iteration threshold;

[0062] If it is confirmed that the cumulative update iteration number of the current iteration reaches the cumulative update iteration threshold;

[0063] Based on the accumulated error correction mechanism, the dynamic response of the high-rise building in the current iteration is corrected to obtain the optimized dynamic response and update it to the dynamic response of the high-rise building in the current iteration;

[0064] Otherwise, no error correction is performed on the dynamic response of the high-rise building in the current iteration;

[0065] When the next iteration needs to be performed, step S48 is executed;

[0066] S48: According to the prediction error covariance matrix of the error covariance, obtain the updated covariance matrix, which is expressed as follows:

[0067] P k|k =(IK k ·H)·P k|k-1

[0068] The dynamic response of the high-rise building and the updated covariance matrix in the current iteration are used as inputs of the Kalman filter algorithm again, and S44 to S47 are repeatedly executed.

[0069] Furthermore, the accumulated error correction mechanism described in S47 is specifically:

[0070] Set the cumulative error limit parameter α;

[0071] Obtaining the dynamic response of the high-rise building obtained in the corresponding iterative process when the cumulative update iteration threshold is met, so as to calculate the accumulated error of the horizontal displacement of the dynamic response of the high-rise building;

[0072] And the calculation formula of the horizontal displacement cumulative error is:

[0073] drift k =h k|k -z k

[0074] Where: h k|k represents the estimated horizontal displacement after Kalman filtering update, i.e., the updated state estimate of the dynamic response of the high-rise building in the current iteration The displacement in z k Indicates the horizontal displacement value measured according to the measurement equation; drift k Indicates the accumulated error of horizontal displacement;

[0075] According to the accumulated error of horizontal displacement and the accumulated error limit parameter α, the dynamic response of the high-rise building under the current iteration is corrected to obtain the optimized dynamic response;

[0076] And the error correction expression for the dynamic response of the high-rise building in the current iteration is:

[0077]

[0078] Where: represents the modified dynamic response, that is, the estimated value of the horizontal displacement of the high-rise building in the current iteration.

[0079] Furthermore, the S5 specifically includes the following steps:

[0080] S51: performing proportional normalization processing on the acquired acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude and estimated displacement data;

[0081] And the expression of the ratio normalization process is

[0082]

[0083] Where: x norm represents the data after the proportional normalization processing; X represents the numerical set of the acceleration time series signal, the weighted average instantaneous frequency, the maximum instantaneous amplitude and the estimated displacement data, and X={x 1 ,x 2 ,....,xi ,....,x n}; x i represents the i-th group of numerical data and i=1,2,...,n; n represents the total number of numerical sets;

[0084] S52: Perform weighted calculation on the normalized data to obtain the comprehensive comfort score at each moment, and the expression for weighted calculation on the normalized data is:

[0085] S=ω 1 ·|α norm |+ω 2 ·|f norm |+ω 3 ·|A norm |+w 4 ·|L norm |

[0086] Where: S represents the comprehensive comfort score of high-rise buildings under wind load; w i represents the weight of the comfort score; α norm represents the normalized value of acceleration at time t; f norm A represents the normalized value of the instantaneous frequency at time t; norm Represents the normalized value of the instantaneous amplitude at time t; L norm represents the normalized value of displacement at time t;

[0087] S53: Setting a grade scoring threshold for evaluating the comfort grade, and confirming the grade scoring threshold satisfied by the comprehensive comfort grade, so as to realize the dynamic comfort assessment of the engineering structure under the action of wind load.

[0088] Beneficial effects: The present invention provides a method for evaluating the dynamic comfort of an engineering structure under wind load. The acceleration time series signal is decomposed by Hilbert transform, and the data features of the instantaneous frequency and instantaneous amplitude of the intrinsic mode signal are extracted to improve the accuracy of the randomness and time-varying vibration characteristics of the high-rise building under wind load at each moment, and the weighted average instantaneous frequency and maximum instantaneous amplitude at each moment are obtained to further improve the accuracy of the acquired data; in order to improve the accuracy of the measured data, based on the Kalman filter algorithm and introducing the accumulated error correction mechanism, the noise of the collected acceleration time series signal is suppressed, and the estimated displacement data of the engineering structure under wind load at each moment is accurately predicted, and by obtaining the comprehensive comfort score at each moment, the dynamic comfort evaluation of the engineering structure under wind load is accurately realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0090] Figure 1 The present invention is a flow chart of the method for evaluating the dynamic comfort of an engineering structure under wind load. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0092] This embodiment provides a method for evaluating the dynamic comfort of an engineering structure under wind load. Figure 1 As shown, the specific steps include:

[0093] S1: Collect and obtain dynamic response data of high-rise buildings under wind loads;

[0094] The dynamic response data is: acceleration time series signals collected by acceleration sensors installed at key locations of high-rise buildings; the key locations of high-rise buildings include the centroid position or structural joints / connection positions of high-rise buildings; this embodiment installs acceleration sensors at key locations of buildings to record acceleration signals in real time and outputs raw acceleration data in the form of time series for subsequent analysis;

[0095] S2: Decompose the acceleration time series signal into several intrinsic mode signals based on the empirical mode decomposition method; and extract the features of each intrinsic mode signal based on the Hilbert transform to obtain the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal, which specifically includes the following steps

[0096] S21: Based on the empirical mode decomposition method, the acceleration time series signal is decomposed into several intrinsic mode signals, whose expression is:

[0097] IMF i =a(t)-m(t)

[0098] Where: IMF i represents the i-th intrinsic mode function and is IMF iThe simplified form of (t) is the intrinsic mode signal; m(t) represents the local mean of the acceleration time series signal; a(t) represents the dynamic response data of the high-rise building under wind load;

[0099] S22: Perform Hilbert transform on each intrinsic mode signal, and its expression is:

[0100]

[0101] Where: H[IMF i (t)] represents the intrinsic mode signal IMF i (t); τ represents the integral variable over the entire time range of the signal; t represents the time variable;

[0102] S23: Extract the features of the intrinsic mode signal after Hilbert transformation to obtain the instantaneous frequency and instantaneous amplitude, which are expressed as follows:

[0103]

[0104] A(t)=|H[IMF i (t)]|

[0105] Where: φ(t) represents the Hilbert transform phase; f(t) represents the instantaneous frequency extracted from each intrinsic mode function; A(t) represents the instantaneous amplitude extracted from each intrinsic mode function;

[0106] This embodiment extracts the instantaneous frequency and instantaneous amplitude of the building under the action of wind load by Hilbert-Huang transform, so as to more accurately analyze the dynamic response characteristics of the building, that is, the acceleration signal is decomposed into different modal components by adopting the empirical mode decomposition method, and then the instantaneous frequency and instantaneous amplitude of each mode are calculated by Hilbert transform;

[0107] S3: according to the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal, obtain the weighted average instantaneous frequency and the maximum instantaneous amplitude at each moment;

[0108] In a specific embodiment, according to the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal, the weighted average instantaneous frequency and the maximum instantaneous amplitude at each moment are obtained, and the expression is:

[0109]

[0110] A max =max(A i (t))

[0111] Where: Represents the weighted average instantaneous frequency at each moment, f i(t) represents the instantaneous frequency of the ith eigenmode function extraction and f i (t)∈f(t); N represents the total number of eigenmode functions; A i (t) represents the instantaneous amplitude of the i-th eigenmode function extraction and A i (t)∈A(t); A max Indicates the maximum instantaneous amplitude; w i (t) represents the weight confirmed by the instantaneous amplitude of each mode. In this embodiment, the weighted average instantaneous frequency and the maximum instantaneous amplitude at each moment are obtained to evaluate the human body comfort;

[0112] S4: Based on the Kalman filter algorithm and the introduction of the accumulated error correction mechanism, the estimated displacement data of the engineering structure at each moment under the action of wind load is obtained according to the collected acceleration time series signal;

[0113] In a specific embodiment, based on the Kalman filter algorithm, the estimated displacement data of the engineering structure at each moment under the action of wind load is obtained according to the collected acceleration time series signal, specifically including

[0114] S41: Establish the state equation of the dynamic response of high-rise buildings under wind loads;

[0115] And the expression of the dynamic response state equation is

[0116] x k =Ax k-1 +Bu k +w k

[0117] Where: x k represents the state vector at time k regarding the horizontal displacement and velocity of the high-rise building and A represents the state transfer matrix; B represents the control input matrix; u k represents the control input vector - acceleration signal; w k represents process noise;

[0118] S43: Initialize parameters of the Kalman filter algorithm;

[0119] And the initialized parameters include the state transfer matrix A:

[0120] Control input matrix B:

[0121] Observation matrix H: H =

[10] ;

[0122] Covariance Q of process noise: To characterize the uncertainty in the dynamic response of high-rise buildings under wind loads over time;

[0123] In order to cope with the fact that the vibrations generated by wind loads often have the characteristics of randomness and time-varying characteristics, the nonlinear and non-stationary dynamic response characteristics are effectively involved in the evaluation and analysis process. Therefore, the following parameters are specially referred to, such as the observation noise covariance R: a scalar set according to empirical values; used to describe the variance of the observation noise, which describes the uncertainty in the measurement process, such as interference from environmental noise, sensor accuracy, etc., and is usually set to a scalar such as R = 0.1;

[0124] Error covariance P: Used to describe the uncertainty of state estimation, usually the identity matrix;

[0125] State vector x: Where h(0) represents the initial estimated value of the current horizontal displacement of the high-rise building; v(0) represents the initial estimated value of the current velocity of the high-rise building;

[0126] S44: Based on the Kalman filter algorithm, the prediction estimation and error covariance of the dynamic response state of high-rise buildings under wind loads are realized according to the state observation equation;

[0127] The expression for the prediction estimate of the dynamic response state is:

[0128]

[0129] Where: k represents the time; represents the predicted estimate of the dynamic response state at time k; represents the updated state estimate at time k-1;

[0130] The error covariance is expressed as

[0131] P k|k-1 =AP k-1|k-1 A T +Q

[0132] Where: P k|k-1 represents the prediction error covariance matrix at time k; P k-1|k-1 represents the update error covariance matrix at time k-1; Q represents the process noise covariance matrix, that is, the process noise in the dynamic response state equation; A T represents the transpose of A;

[0133] S45: The measurement equation of a high-rise building under wind load is obtained based on the dynamic response state equation, which is expressed as follows:

[0134] z k =Hx k +v k

[0135] Where: zk represents the measured horizontal displacement;

[0136] S46: According to the update formula of the error covariance, the Kalman gain of the Kalman filter algorithm is obtained; and the expression of the Kalman gain is

[0137] K k =P k|k-1 ·H T ·(H·P k|k-1 ·H T +R) -1

[0138] Where: K k represents the Kalman gain matrix; R is the observation noise covariance matrix, which represents the degree of measurement noise; H T represents the transpose of H;

[0139] S47: Based on the Kalman gain, the current state estimation update equation of the high-rise building is obtained according to the measurement equation and the predicted estimation of the dynamic response state;

[0140] And the expression of the current state estimation update equation is

[0141]

[0142] Where: represents the updated state estimate at time k; Residuals represent the difference between the measured and predicted values.

[0143] And the state estimate of the current state estimate update equation is used as the dynamic response of the high-rise building in the current iteration;

[0144] Set the cumulative update iteration threshold and confirm whether the cumulative update iteration number in the current iteration reaches the cumulative update iteration threshold;

[0145] If it is confirmed that the cumulative update iteration number of the current iteration reaches the cumulative update iteration threshold;

[0146] Based on the accumulated error correction mechanism, the dynamic response of the high-rise building in the current iteration is corrected to obtain the optimized dynamic response and update it to the dynamic response of the high-rise building in the current iteration;

[0147] Otherwise, no error correction is performed on the dynamic response of the high-rise building in the current iteration;

[0148] And the accumulated error correction mechanism is specifically:

[0149] Set the cumulative error limit parameter α;

[0150] Obtaining the dynamic response of the high-rise building obtained in the corresponding iterative process when the cumulative update iteration threshold is met, so as to calculate the accumulated error of the horizontal displacement of the dynamic response of the high-rise building;

[0151] And the calculation formula of the horizontal displacement cumulative error is:

[0152] drift k =h k|k -z k

[0153] Where: h k|k represents the estimated horizontal displacement after Kalman filtering update, i.e., the updated state estimate of the dynamic response of the high-rise building in the current iteration The displacement in z k Indicates the horizontal displacement value measured according to the measurement equation; drift k Indicates the accumulated error of horizontal displacement;

[0154] According to the accumulated error of horizontal displacement and the accumulated error limit parameter α, the dynamic response of the high-rise building under the current iteration is corrected to obtain the optimized dynamic response;

[0155] And the error correction expression for the dynamic response of the high-rise building in the current iteration is:

[0156]

[0157] Where: represents the modified dynamic response, i.e., the estimated value of the horizontal displacement of the high-rise building at the current iteration;

[0158] When the next iteration needs to be performed, step S48 is executed;

[0159] S48: According to the prediction error covariance matrix of the error covariance, obtain the updated covariance matrix, which is expressed as follows:

[0160] P k|k =(IK k ·H)·P k|k-1

[0161] The dynamic response of the high-rise building and the updated covariance matrix in the current iteration are used as inputs of the Kalman filter algorithm again, and S44 to S47 are repeatedly executed;

[0162] S5: Perform proportional normalization processing on the acquired acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude and estimated displacement data, and perform weighted calculation on the normalized data to obtain the comprehensive comfort score at each moment, so as to realize the dynamic comfort evaluation of the engineering structure under wind load, which specifically includes the following steps:

[0163] S51: Perform proportional normalization on the acquired acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude, and estimated displacement data;

[0164] And the expression for the proportional normalization is

[0165]

[0166] In the formula: x norm Represents the data after proportional normalization; X represents the numerical set of the acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude, and estimated displacement data, and X = {x 1 , x 2 ,...., x i ,...., x n}; x i Represents the i-th group of numerical data and i = 1, 2,..., n; n represents the total number of the numerical set; among them, through the threshold reference value T in Table 1, the variables, namely the acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude, and estimated displacement data, are proportionally normalized to eliminate the deviation;

[0167] Table 1. Proportional Normalization Threshold Reference Table

[0168]

[0169] S52: Perform weighted calculation on the normalized data to obtain the comprehensive comfort score at each moment, and the expression for performing weighted calculation on the normalized data is

[0170] S = ω 1 ·|α norm | + ω 2 ·|f norm | + ω 3 ·|A norm | + w 4 ·|L norm |

[0171] In the formula: S represents the comprehensive comfort score of the high-rise building under wind load; w i Represents the weight of the comfort score; α norm Represents the normalized value of the acceleration at time t; f norm Represents the normalized value of the instantaneous frequency at time t; A norm Represents the normalized value of the instantaneous amplitude at time t; L norm Represents the normalized value of the displacement at time t;

[0172] Specifically, in this implementation, the weight selection of the comfort score is specifically

[0173] The weight of acceleration (dominant comfort) is 50%. According to the Code for Loads on Building Structures and ISO2631, acceleration is the variable that most directly affects human comfort and can better reflect the sensitivity of the human body to vibration. Therefore, it accounts for a higher proportion in the comprehensive evaluation.

[0174] The weight of instantaneous frequency is 15%: vibration frequency affects human comfort, and lower frequencies will bring more significant discomfort. Specifications such as ISO 2631 also indicate that frequency is an important influencing factor.

[0175] The weight of instantaneous amplitude is 15%: The impact of instantaneous amplitude on comfort is relatively small, because it is only a momentary manifestation of acceleration and does not necessarily affect the overall comfort experience.

[0176] The weight of horizontal displacement is 20%: although the direct physiological impact of floor displacement may not be as obvious as acceleration, its psychological and perceptual impact cannot be underestimated;

[0177] S53: Setting a grade scoring threshold for evaluating the comfort grade, and confirming the grade scoring threshold satisfied by the comprehensive comfort grade, so as to realize the dynamic comfort assessment of the engineering structure under the action of wind load.

[0178] Compared with the prior art, the beneficial effects of this embodiment are as follows: the acceleration time series signal is decomposed by Hilbert transform, and the data features of the instantaneous frequency and instantaneous amplitude of the intrinsic mode signal are extracted to improve the accuracy of the randomness and time-varying vibration characteristics of the high-rise building under wind load at each moment, and the weighted average instantaneous frequency and maximum instantaneous amplitude at each moment are obtained to further improve the accuracy of the acquired data; in order to improve the accuracy of the measured data, based on the Kalman filter algorithm and introducing the accumulated error correction mechanism, the noise of the collected acceleration time series signal is suppressed, and the estimated displacement data of the engineering structure at each moment under the action of wind load is accurately predicted, and by obtaining the comprehensive comfort score at each moment, the dynamic comfort evaluation of the engineering structure under wind load is accurately realized.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the dynamic comfort of an engineering structure under wind load, characterized in that: The specific steps include: S1: Collect and obtain dynamic response data of high-rise buildings under wind loads; The dynamic response data is: acceleration time series signals collected by acceleration sensors installed at key locations of high-rise buildings; The key parts of the high-rise building include the centroid position or structural joint / connection position of the high-rise building; S2: Decompose the acceleration time series signal into several intrinsic mode signals based on the empirical mode decomposition method; Based on the Hilbert transform, feature extraction is performed on each intrinsic mode signal to obtain the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal; S3: according to the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal, obtain the weighted average instantaneous frequency and the maximum instantaneous amplitude at each moment; S4: Based on the Kalman filter algorithm and the introduction of the accumulated error correction mechanism, the estimated displacement data of the engineering structure at each moment under the action of wind load is obtained according to the collected acceleration time series signal; S5: The acquired acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude and estimated displacement data are proportionally normalized, and weighted calculation is performed on the normalized data to obtain the comprehensive comfort score at each moment, so as to realize the dynamic comfort evaluation of the engineering structure under wind load.

2. The method for evaluating the dynamic comfort of an engineering structure under wind load according to claim 1 is characterized in that: The S2 specifically includes the following steps S21: Based on the empirical mode decomposition method, the acceleration time series signal is decomposed into several intrinsic mode signals, whose expression is IMF i =a(t)-m(t) Where: IMF i represents the i-th intrinsic mode function and is IMF i The simplified form of (t) is the intrinsic mode signal; m(t) represents the local mean of the acceleration time series signal; a(t) represents the dynamic response data of the high-rise building under wind load; S22: Perform Hilbert transform on each intrinsic mode signal, and its expression is: Where: H[IMF i (t)] represents the intrinsic mode signal IMF i (t); τ represents the integral variable over the entire time range of the signal; t represents the time variable; S23: Extract the features of the intrinsic mode signal after Hilbert transformation to obtain the instantaneous frequency and instantaneous amplitude, which are expressed as follows: A(t)=|H[IMF i (t)]| Where: φ(t) represents the Hilbert transform phase; f(t) represents the instantaneous frequency extracted from each intrinsic mode function; A(t) represents the instantaneous amplitude extracted from each intrinsic mode function.

3. The method for evaluating the dynamic comfort of an engineering structure under wind load according to claim 2 is characterized in that: In S3, the weighted average instantaneous frequency and the maximum instantaneous amplitude at each moment are obtained according to the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal. The expression is: A max =max(A i (t)) Where: Represents the weighted average instantaneous frequency at each moment, f i (t) represents the instantaneous frequency of the ith eigenmode function extraction and f i (t)∈f(t); N represents the total number of eigenmode functions; A i (t) represents the instantaneous amplitude of the ith eigenmode function extracted and A i (t)∈A(t); A max Indicates the maximum instantaneous amplitude; w i (t) represents the weight determined by the instantaneous amplitude of each mode.

4. The method for evaluating the dynamic comfort of an engineering structure under wind load according to claim 3 is characterized in that: In S4, based on the Kalman filter algorithm, the estimated displacement data of the engineering structure at each moment under the action of wind load is obtained according to the collected acceleration time series signal, including S41: Establish the state equation of the dynamic response of high-rise buildings under wind loads; And the expression of the dynamic response state equation is x k =Ax k-1 +Bu k +w k Where: x k represents the state vector at time k regarding the horizontal displacement and velocity of the high-rise building and A represents the state transfer matrix; B represents the control input matrix; u k represents the control input vector - acceleration signal; w k represents process noise; S43: Initialize parameters of the Kalman filter algorithm; And the initialized parameters include the state transfer matrix A: Control input matrix B: Observation matrix H: H = [1 0]; Covariance Q of process noise: Observation noise covariance R: a scalar set according to empirical values; Error covariance P: State vector x: Where h(0) represents the initial estimated value of the current horizontal displacement of the high-rise building; v(0) represents the initial estimated value of the current velocity of the high-rise building; S44: Based on the Kalman filter algorithm, the prediction estimation and error covariance of the dynamic response state of high-rise buildings under wind loads are realized according to the state observation equation; The expression for the prediction estimate of the dynamic response state is: Where: k represents the time; represents the predicted estimate of the dynamic response state at time k; represents the updated state estimate at time k-1; The error covariance is expressed as P k|k-1 =AP k-1|k-1 A T +Q Where: P k|k-1 represents the prediction error covariance matrix at time k; P k-1|k-1 represents the update error covariance matrix at time k-1; Q represents the process noise covariance matrix, that is, the process noise in the dynamic response state equation; A T represents the transpose of A; S45: The measurement equation of a high-rise building under wind load is obtained based on the dynamic response state equation, which is expressed as z k =Hx k -v k Where: z k represents the measured horizontal displacement; S46: Obtaining the Kalman gain of the Kalman filter algorithm according to the update formula of the error covariance; And the expression of the Kalman gain is K k =P k|k-1 ·H T ·(H·P k|k-1 ·H T +R) -1 Where: K k represents the Kalman gain matrix; R represents the observation noise covariance matrix; H T represents the transpose of H; S47: Based on the Kalman gain, the current state estimation update equation of the high-rise building is obtained according to the measurement equation and the predicted estimation of the dynamic response state; And the expression of the current state estimation update equation is Where: represents the updated state estimate at time k; Represents the measurement residual that characterizes the difference between the measured value and the predicted measured value; And the state estimate of the current state estimate update equation is used as the dynamic response of the high-rise building in the current iteration; Set the cumulative update iteration threshold and confirm whether the cumulative update iteration number in the current iteration reaches the cumulative update iteration threshold; If it is confirmed that the cumulative update iteration number of the current iteration reaches the cumulative update iteration threshold; Based on the accumulated error correction mechanism, the dynamic response of the high-rise building in the current iteration is corrected to obtain the optimized dynamic response and update it to the dynamic response of the high-rise building in the current iteration; Otherwise, no error correction is performed on the dynamic response of the high-rise building in the current iteration; When the next iteration needs to be performed, step S48 is executed; S48: According to the prediction error covariance matrix of the error covariance, obtain the updated covariance matrix, which is expressed as P k|k =(IK k ·H)·P k|k-1 The dynamic response of the high-rise building and the updated covariance matrix in the current iteration are used as inputs of the Kalman filter algorithm again, and S44 to S47 are repeatedly executed.

5. The method for evaluating the dynamic comfort of an engineering structure under wind load according to claim 4 is characterized in that: The accumulated error correction mechanism described in S47 is specifically: Set the cumulative error limit parameter α; Obtaining the dynamic response of the high-rise building obtained in the corresponding iterative process when the cumulative update iteration threshold is met, so as to calculate the accumulated error of the horizontal displacement of the dynamic response of the high-rise building; The calculation formula of the horizontal displacement cumulative error is drift k =h k|k -z k Where: h k|k represents the estimated horizontal displacement after Kalman filtering update, i.e., the updated state estimate of the dynamic response of the high-rise building in the current iteration The displacement in z k Indicates the horizontal displacement value measured according to the measurement equation; drift k Indicates the accumulated error of horizontal displacement; According to the accumulated error of horizontal displacement and the accumulated error limit parameter α, the dynamic response of the high-rise building under the current iteration is corrected to obtain the optimized dynamic response; And the error correction expression for the dynamic response of the high-rise building in the current iteration is: Where: represents the modified dynamic response, that is, the estimated value of the horizontal displacement of the high-rise building in the current iteration.

6. The method for evaluating the dynamic comfort of an engineering structure under wind load according to claim 5 is characterized in that: The S5 specifically includes the following steps: S51: performing proportional normalization processing on the acquired acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude and estimated displacement data; And the expression of the ratio normalization process is Where: x norm represents the data after proportional normalization; X represents the numerical set of acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude and estimated displacement data, and X={x1,x2,....,x i ,....,x n }; x i represents the i-th group of numerical data and i=1,2,...,n; n represents the total number of numerical sets; S52: Perform weighted calculation on the normalized data to obtain the comprehensive comfort score at each moment, and the expression for weighted calculation on the normalized data is: S=ω1·|α norm |+ω2·|f norm |+ω3·|A norm |+w4·|L norm | Where: S represents the comprehensive comfort score of high-rise buildings under wind load; w i represents the weight of the comfort score; α norm represents the normalized value of acceleration at time t; f norm A represents the normalized value of the instantaneous frequency at time t; norm Represents the normalized value of the instantaneous amplitude at time t; L norm represents the normalized value of displacement at time t; S53: Setting a grade scoring threshold for evaluating the comfort grade, and confirming the grade scoring threshold satisfied by the comprehensive comfort grade, so as to realize the dynamic comfort assessment of the engineering structure under the action of wind load.

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