Method for evaluating dynamic comfort of engineering structure under wind load

Abstract

The application discloses a kind of engineering structure dynamic comfort evaluation methods under wind load action, including the dynamic response data of high-rise building under wind load action is collected and obtained;Acceleration time series signal is decomposed into several intrinsic mode signals based on empirical mode decomposition method, and the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal are obtained based on Hilbert transform;According to the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal, the weighted average instantaneous frequency and maximum instantaneous amplitude of each time are obtained;Based on Kalman filtering algorithm and introducing cumulative error correction mechanism, the estimated displacement data of engineering structure at each time under wind load action is obtained according to the acceleration time series signal collected, the comprehensive comfort score of each time is obtained, to realize the dynamic comfort evaluation of engineering structure under wind load action.The application solves the problem that the vibration generated by wind load often has the characteristics of randomness and time variation, so that the dynamic comfort evaluation of engineering structure under wind load action cannot be accurately realized due to the neglect of nonlinear and non-stationary dynamic response characteristics.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of high-rise building human comfort evaluation, and particularly relates to a method for evaluating dynamic comfort of engineering structure under wind load. BACKGROUND

[0002] In the field of high-rise buildings, dynamic response under wind load can cause significant vibration, affecting structural stability and human comfort. With the increase of building height, the vibration caused by wind load can produce uncomfortable lateral swing and acceleration fluctuation.

[0003] The current acceleration threshold method and traditional methods such as Fourier transform cannot effectively evaluate the instantaneous influence of wind-induced vibration on human body, because they only consider the overall frequency characteristics or fixed threshold, ignoring the nonlinear and non-stationary dynamic response characteristics. The vibration caused by wind load often has the characteristics of randomness and time-varying, which makes it impossible to accurately evaluate the dynamic comfort of engineering structure under wind load. SUMMARY

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

[0005] In order to achieve the above purpose, the technical scheme of the present application is:

[0006] A method for evaluating dynamic comfort of engineering structure under wind load, comprising the following steps:

[0007] S1: collecting and obtaining dynamic response data of high-rise building under wind load;

[0008] And the dynamic response data is the acceleration time series signal collected by the acceleration sensor installed at the key position of the high-rise building;

[0009] The key position of the high-rise building includes the center of mass position or the structure joint / connection position of the high-rise building

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

[0011] And based on Hilbert transform, the characteristic of each intrinsic mode signal is extracted to obtain the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal;

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

[0013] S4: obtaining the estimated displacement data of engineering structure at each time under wind load based on Kalman filtering algorithm and introducing the cumulative error correction mechanism according to the collected acceleration time series signal;

[0014] S5: performing proportional normalization on the obtained acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude and estimated displacement data, and performing weighted calculation on the normalized data to obtain comprehensive comfort scores at each time to realize dynamic comfort evaluation of the engineering structure under wind load.

[0015] Further, S2 specifically comprises the following steps

[0016] S21: decomposing the acceleration time series signal into a plurality of intrinsic mode signals based on the empirical mode decomposition method, and the expression is

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

[0018] In the formula: IMF i represents the i th intrinsic mode function, and IMF i (t) is a simplified form of the intrinsic mode signal, i.e. 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: Hilbert transform is performed on each intrinsic mode signal, and the expression is

[0020]

[0021] In the formula: H[IMF i (t)] represents the Hilbert transform of the intrinsic mode signal IMF i (t); τ represents the integral variable of the entire time range of the traversal signal; t represents the time variable;

[0022] S23: feature extraction is performed on the intrinsic mode signal after Hilbert transform to obtain instantaneous frequency and instantaneous amplitude, and the expression is

[0023]

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

[0025] In the formula: 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] Further, 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 time are obtained in S3, and the expression is

[0027]

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

[0029] In the formula: f i (t) represents the instantaneous frequency extracted by the i th eigenmode function and f i (t)∈f(t);N represents the total number of eigenmode functions;A i (t) represents the instantaneous amplitude extracted by the i th eigenmode function and A i (t)∈A(t);A max represents the maximum instantaneous amplitude;w i (t) represents the weight confirmed by the instantaneous amplitude of each mode.

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

[0031] S41: establishing a dynamic response state equation of the high-rise building under wind load;

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

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

[0034] In the formula: x k represents the state vector of moment k about the horizontal displacement and velocity of the high-rise building, and A represents a state transition matrix;B represents a control input matrix;u k represents a control input vector-acceleration signal;w k represents process noise;

[0035] S43: parameter initialization of Kalman filtering algorithm;

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

[0037] Control input matrix B:

[0038] Observation matrix H: H =

[10] ;

[0039] Covariance of process noise Q:

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

[0041] Error covariance P:

[0042] State vector x: Wherein 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 speed of the high-rise building;

[0043] S44: based on the Kalman filtering algorithm, the prediction estimation of the dynamic response state of the high-rise building under wind load and the error covariance are realized according to the state observation equation;

[0044] The expression of the prediction estimation of the dynamic response state is

[0045]

[0046] In the formula: k represents the time; The prediction estimation of the dynamic response state at k time; The updated state estimation at k-1 time;

[0047] The expression of the error covariance is

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

[0049] In the formula: P k|k-1 represents the prediction error covariance matrix at k time; P k-1|k-1 represents the updated error covariance matrix at k-1 time; 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 the high-rise building under the action of wind load is obtained according to the dynamic response state equation, and the expression is

[0051] z k =Hx k +v k

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

[0053] S46: the Kalman gain of the Kalman filtering algorithm is obtained according to the updating formula of the error covariance; 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] wherein K k represents a Kalman gain matrix; R represents an observation noise covariance matrix; H T represents a transpose of H;

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

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

[0058]

[0059] wherein: represents an updated state estimation at k time; represents a measurement residual for representing a difference between a measurement value and a predicted measurement value;

[0060] and taking the state estimation of the current state estimation update equation as the dynamic response of the high-rise building in the current iteration;

[0061] setting an accumulated update iteration threshold value, and confirming whether the accumulated update iteration number in the current iteration reaches the accumulated update iteration threshold value;

[0062] if it is confirmed that the accumulated update iteration number in the current iteration reaches the accumulated update iteration threshold value;

[0063] then performing error correction on the dynamic response of the high-rise building in the current iteration based on an accumulated error correction mechanism to obtain an optimized dynamic response and update it as the dynamic response of the high-rise building in the current iteration;

[0064] otherwise, not performing error correction on the dynamic response of the high-rise building in the current iteration;

[0065] when it is necessary to perform the next iteration, performing step S48;

[0066] S48: obtaining an updated covariance matrix according to a predicted error covariance matrix of the error covariance, and an expression of the updated covariance matrix is

[0067] P k|k =(I-K k ·H)·P k|k-1

[0068] and taking the dynamic response of the high-rise building in the current iteration and the updated covariance matrix as inputs of the Kalman filtering algorithm again, and repeating S44 to S47.

[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 cumulative 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 horizontal displacement estimate after Kalman filter update, that is, the updated state estimate of the dynamic response of the high-rise building under the current iteration 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 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 ,....,xn};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: the weighted calculation is performed on the normalized data, the comprehensive comfort degree score of each time is obtained, and the expression for the weighted calculation of the normalized data is

[0085] S=ω1·|α norm |+ω2·|f norm |+ω3·|A norm |+w4·|L norm |

[0086] In the formula, S represents the comprehensive comfort degree score of the high-rise building under the action of wind load; w i represents the weight of the comfort degree score; α norm represents the normalized value of the acceleration at t time; f norm represents the normalized value of the instantaneous frequency at t time; A norm represents the normalized value of the instantaneous amplitude at t time; L norm represents the normalized value of the displacement at t time;

[0087] S53: the grade score threshold for evaluating the comfort degree grade is set, and the grade score threshold satisfied by the comprehensive comfort degree score is confirmed, so that the dynamic comfort degree evaluation of the engineering structure under the action of wind load is realized.

[0088] Beneficial effects: the application provides a dynamic comfort degree evaluation method of engineering structure under the action of wind load, the acceleration time sequence signal is decomposed through Hilbert transform, and the data characteristics of the instantaneous frequency and the instantaneous amplitude of the intrinsic mode signal are extracted, so that the accuracy of the vibration characteristics of the high-rise building under the action of wind load at each time is improved, and the weighted average instantaneous frequency and the maximum instantaneous amplitude at each time are obtained, and the accuracy of the obtained data is further improved; in order to improve the accuracy of the measured data, the collected acceleration time sequence signal is subjected to noise suppression based on the Kalman filtering algorithm and by introducing an accumulated error correction mechanism, the estimated displacement data of the engineering structure at each time under the action of wind load is accurately predicted, and the dynamic comfort degree evaluation of the engineering structure under the action of wind load is accurately realized by obtaining the comprehensive comfort degree score at each time. BRIEF DESCRIPTION OF DRAWINGS

[0089] 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 to be used in the embodiments or prior art description. Obviously, the drawings described below are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0090] Figure 1 The flowchart of the method for evaluating the dynamic comfort of the engineering structure under the wind load. DETAILED DESCRIPTION

[0091] 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, rather than all the 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.

[0092] The embodiment provides a method for evaluating the dynamic comfort of an engineering structure under a wind load, as shown in the figure, specifically comprising the following steps: Figure 1

[0093] S1: collecting and acquiring dynamic response data of a high-rise building under a wind load;

[0094] The dynamic response data is an acceleration time series signal collected by an acceleration sensor installed at a key part of the high-rise building. The key part of the high-rise building includes a mass center position or a structure joint / connection position of the high-rise building. The embodiment records acceleration signals in real time by installing an acceleration sensor at a key part of the building, and outputs original acceleration data in the form of a time series for subsequent analysis.

[0095] S2: decomposing the acceleration time series signal into a plurality of intrinsic mode signals based on an empirical mode decomposition method; and based on Hilbert transform, extracting features of each intrinsic mode signal to obtain an instantaneous frequency and an instantaneous amplitude of each intrinsic mode signal, specifically comprising the following steps

[0096] S21: decomposing the acceleration time series signal into a plurality of intrinsic mode signals based on an empirical mode decomposition method, and the expression is

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

[0098] IMF i represents the i th intrinsic mode function, and IMF i ​The simplified form of (t) is the eigenmode signal; m(t) represents the local mean value 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 eigenmode 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 transform 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 uses the Hilbert-Huang transform to extract the instantaneous frequency and instantaneous amplitude of the building under wind load, thereby more accurately analyzing the dynamic response characteristics of the building. Specifically, the acceleration signal is decomposed into different modal components by using the empirical mode decomposition method, and then the instantaneous frequency and instantaneous amplitude of each mode are calculated through the Hilbert transform.

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

[0108] In a specific embodiment, 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 eigenmode signal, 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 extracted by the i-th eigenmode function and f i (t)∈f(t);N represents the total number of eigenmode functions;A i (t) represents the instantaneous amplitude extracted by the i-th eigenmode function and A i (t)∈A(t);A max represents the maximum instantaneous amplitude;w i (t) represents the weight confirmed by the instantaneous amplitude of each mode, in the embodiment, the weighted average instantaneous frequency and the maximum instantaneous amplitude at each moment are obtained for evaluating the human comfort degree;

[0112] S4: based on the Kalman filtering algorithm and introducing the cumulative 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 sequence signal;

[0113] In specific embodiments, based on the Kalman filtering 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 sequence signal, specifically including

[0114] S41: a dynamic response state equation of the high-rise building under wind load is established;

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

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

[0117] In the formula: x k represents the state vector of the high-rise building at moment k about horizontal displacement and velocity and A represents a state transition matrix;B represents a control input matrix;u k represents a control input vector-acceleration signal;w k represents process noise;

[0118] S43: parameter initialization is performed on the Kalman filtering algorithm;

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

[0120] The control input matrix B:

[0121] The observation matrix H: H =

[10] ;

[0122] The covariance Q of the process noise: for characterizing the uncertainty in the dynamic response of the high-rise building with time under the action of wind load;

[0123] To cope with the vibration caused by wind load often has the characteristics of randomness and changes over time, so that its nonlinear, non-stationary dynamic response characteristics effectively participate in the evaluation analysis process, therefore, the following parameters are referred to, such as observation noise covariance R: a scalar set according to empirical value; the variance used to describe the observation noise, that is, the observation noise covariance describes the uncertainty in the measurement process, such as environmental noise, sensor accuracy and other disturbances, which is usually set as a scalar, for example, R = 0.1;

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

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

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

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

[0128]

[0129] In the formula: k represents the time; represents the prediction of the dynamic response state at time k; represents the updated state estimate at time k-1;

[0130] The expression of the error covariance is

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

[0132] In the formula: P k|k-1 represents the prediction error covariance matrix at time k; P k-1|k-1 represents the updated 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: According to the dynamic response state equation, the measurement equation of the high-rise building under wind load is obtained, and its expression is

[0134] z k = Hx k +v k

[0135] In the formula: zk represents the measured horizontal displacement;

[0136] S46: obtaining the Kalman gain of the Kalman filtering algorithm according to the update formula of the error covariance; 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] In the formula, K k represents the Kalman gain matrix; R is the observation noise covariance matrix, representing the degree of measurement noise; H T represents the transpose of H;

[0139] S47: obtaining the current state estimation update equation of the high-rise building based on the Kalman gain, 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] In the formula, x represents the updated state estimation at the k time; represents the measurement residual for representing the difference between the measurement value and the predicted measurement value;

[0143] and taking the state estimation of the current state estimation update equation as the dynamic response of the high-rise building in the current iteration;

[0144] setting the cumulative update iteration threshold value, and confirming whether the cumulative update iteration number at the current iteration reaches the cumulative update iteration threshold value;

[0145] if it is confirmed that the cumulative update iteration number at the current iteration reaches the cumulative update iteration threshold value;

[0146] then performing error correction on the dynamic response of the high-rise building in the current iteration based on the accumulated error correction mechanism, to obtain the optimized dynamic response and update it as the dynamic response of the high-rise building in the current iteration;

[0147] otherwise, not performing error correction on the dynamic response of the high-rise building in the current iteration;

[0148] and the accumulated error correction mechanism is specifically

[0149] setting a cumulative error limit parameter a;

[0150] acquiring the high-rise building dynamic response corresponding to the iteration process when the cumulative update iteration threshold is met, to calculate the horizontal displacement cumulative error of the high-rise building dynamic response;

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

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

[0153] In the formula, h k|k represents the horizontal displacement estimation value after the Kalman filter update, that is, the updated state estimation of the high-rise building dynamic response at the current iteration z k represents the horizontal displacement value measured according to the measurement equation; and drift k represents the horizontal displacement cumulative error.

[0154] According to the horizontal displacement cumulative error and the cumulative error limiting parameter a, the dynamic response of the high-rise building at the current iteration is error-corrected to obtain an optimized dynamic response.

[0155] And the expression for error-correcting the dynamic response of the high-rise building at the current iteration is

[0156]

[0157] In the formula, represents the corrected dynamic response, that is, the estimation 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 performed.

[0159] S48: According to the prediction error covariance matrix of the error covariance, an update covariance matrix is obtained, and the expression is

[0160] P k|k = (I-K k ·H)·P k|k-1

[0161] The dynamic response of the high-rise building at the current iteration is taken as the input of the Kalman filter algorithm again, and steps S44 to S47 are repeatedly performed.

[0162] S5: The acquired acceleration time series signal, weighted average instantaneous frequency, maximum instantaneous amplitude and estimated displacement data are proportionally normalized, and the normalized data are weighted calculated to obtain the comprehensive comfort score at each time, so as to realize the dynamic comfort evaluation of the engineering structure under the action of wind load, which specifically includes the following steps:

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

[0164] And the expression of the ratio normalization process is

[0165]

[0166] 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; wherein the variables, namely the acceleration time series signal, the weighted average instantaneous frequency, the maximum instantaneous amplitude, and the estimated displacement data, are scaled and normalized by the threshold reference value T in Table 1 to eliminate bias;

[0167] Table 1. Reference table of ratio normalization thresholds

[0168]

[0169] 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:

[0170] S=ω1·|α norm |+ω2·|f norm |+ω3·|A norm |+w4·|L norm |

[0171] 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;

[0172] Specifically, the weight selection of comfort score in this implementation is as follows:

[0173] The weight of acceleration (main comfort) is 50%; according to the Building Structure Load Standard and ISO2631, acceleration is the most direct variable affecting human comfort, which can better reflect the sensitivity of the human body to vibration, so 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 frequency will bring more significant discomfort. The ISO 2631 specification and other specifications also show that frequency is an important influencing factor.

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

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

[0177] S53: Set the grade score threshold of the comfort level evaluation grade, and confirm the grade score threshold satisfied by the comprehensive comfort score, to realize the dynamic comfort evaluation of the engineering structure under the action of wind load.

[0178] Compared with the prior art, the beneficial effects of the embodiment are: through Hilbert transform to decompose the acceleration time sequence signal, and extract the data characteristics of the instantaneous frequency and the instantaneous amplitude of the intrinsic mode signal, to improve the accuracy of the randomness and the vibration characteristics of the high-rise building under the action of wind load at each time, and obtain the weighted average instantaneous frequency and the maximum instantaneous amplitude at each time, to further improve the accuracy of the obtained data; in order to improve the accuracy of the measured data, based on the Kalman filtering algorithm and introducing the cumulative error correction mechanism, the noise of the collected acceleration time sequence signal is suppressed, and the estimated displacement data of the engineering structure at each time under the action of wind load is accurately predicted, and through obtaining the comprehensive comfort score at each time, the dynamic comfort evaluation of the engineering structure under the action of 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 application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating dynamic comfort of an engineering structure under wind load, characterized in that, Specifically comprising the following steps: S1: collecting and acquiring dynamic response data of the high-rise building under the action of wind load; And the dynamic response data is an acceleration time series signal collected by an acceleration sensor installed at a key position of the high-rise building; The key position of the high-rise building includes a centroid position or a structural joint / connection position of the high-rise building; S2: decomposing the acceleration time series signal into a plurality of intrinsic mode signals based on an empirical mode decomposition method; And based on Hilbert transform, the characteristic of each intrinsic mode signal is extracted to obtain the instantaneous frequency and the instantaneous amplitude of each intrinsic mode signal; Specifically comprising the following steps S21: decomposing the acceleration time series signal into a plurality of intrinsic mode signals based on an empirical mode decomposition method, and the expression is wherein: represents the nth eigenmode function, and is a simplified form of the eigenmode signal ; represents the local mean of the acceleration time series; represents the dynamic response data of the high-rise building under wind load S22: performing Hilbert transform on each intrinsic mode signal, and the expression is wherein: denotes the Hilbert transform of the eigenmode signal denotes the Hilbert transform of the eigenmode signal denotes an integration variable over the entire time range of the signal; denotes a time variable; S23: performing characteristic extraction on the intrinsic mode signal after Hilbert transform to obtain the instantaneous frequency and the instantaneous amplitude, and the expression is In the formula: denotes the Hilbert transform phase; denotes the instantaneous frequency extracted by each eigenmode function; denotes the instantaneous amplitude extracted by each eigenmode function; S3: obtaining the weighted average instantaneous frequency and the maximum instantaneous amplitude at each time according to the instantaneous frequency and the instantaneous amplitude of each intrinsic mode signal, and the expression is wherein: represents the weighted average instantaneous frequency at each time instant, represents the instantaneous frequency extracted by the i th eigenmode function and ; represents the total number of eigenmode functions; represents the instantaneous amplitude extracted by the i th eigenmode function and ; represents the maximum instantaneous amplitude; represents the weight confirmed by the instantaneous amplitude of each mode; S4: based on the Kalman filtering algorithm and introducing an accumulated error correction mechanism, the estimated displacement data of the engineering structure at each time under the action of wind load is obtained according to the collected acceleration time series signal; Specifically comprising S41: establishing a dynamic response state equation of the high-rise building under wind load; And the expression of the dynamic response state equation is wherein: denotes the state vector at time t regarding the horizontal displacement and velocity of the high-rise building k denotes the state vector at time t regarding the horizontal displacement and velocity of the high-rise building ; A denotes the state transition matrix; B denotes the control input matrix; denotes the control input vector - acceleration signal; denotes the process noise; S42: parameter initialization of the Kalman filtering algorithm; and the initialized parameters include a state transition matrix A : ; Control input matrix B: ; Observation matrix H: ; covariance of process noise Q : ; The observation noise covariance R is a scalar set according to the empirical value; error covariance P : state vector : wherein denotes an initial estimate of the current horizontal displacement of the high-rise building; denotes an initial estimate of the current velocity of the high-rise building; S43: based on the Kalman filtering algorithm, the prediction estimation and error covariance of the dynamic response state of the high-rise building under wind load are realized according to the state observation equation; The expression of the prediction estimation of the dynamic response state is where k represents the time instant; represents the predicted estimate of the dynamic response state at time instant k; represents the updated state estimate at time instant k-1; The expression of the error covariance is In the formula, represents k the prediction error covariance matrix at time k; represents the update error covariance matrix at time k-1; represents the process noise covariance matrix, i.e., the process noise in the dynamic response state equation; represents A the transpose of S44: obtaining the measurement equation of the high-rise building under wind load according to the dynamic response state equation, and the expression is In the formulae: represents the measured horizontal displacement; S45: obtaining the Kalman gain of the Kalman filtering algorithm according to the update formula of the error covariance; And the expression of the Kalman gain is wherein: denotes the Kalman gain matrix; denotes the observation noise covariance matrix; denotes the transpose of the transpose of S46: 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 prediction estimation of the dynamic response state; And the expression of the current state estimation update equation is In the formula: represents the updated state estimation at time k; represents a measurement residual for characterizing the difference between the measurement value and the predicted measurement value; And the state estimation of the current state estimation update equation is taken 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 at the current iteration reaches the cumulative update iteration threshold; If it is confirmed that the cumulative update iteration number at the current iteration reaches the cumulative update iteration threshold; Then the error correction is performed on the dynamic response of the high-rise building in the current iteration based on the accumulated error correction mechanism to obtain the optimized dynamic response and update it as the dynamic response of the high-rise building in the current iteration; Otherwise, the error correction is not performed on the dynamic response of the high-rise building in the current iteration; When the next iteration needs to be performed, step S47 is performed; S47: obtaining the update covariance matrix according to the prediction error covariance matrix of the error covariance, and the expression is The dynamic response of the high-rise building at the current iteration is taken as an input of the Kalman filtering algorithm again together with the updated covariance matrix, and S44-S46 are repeatedly executed. S5: The acceleration time series signal, the weighted average instantaneous frequency, the maximum instantaneous amplitude, and the estimated displacement data are subjected to proportional normalization processing, and the normalized data are subjected to weighted calculation to obtain the comprehensive comfort score at each moment, so as to realize the dynamic comfort evaluation of the engineering structure under the action of wind load.

2. The method for evaluating dynamic comfort of an engineering structure under wind load according to claim 1, characterized in that, The accumulation error correction mechanism in S46 is specifically Setting accumulated error limit parameter ; The dynamic response of the high-rise building at the corresponding iteration process is obtained when the accumulation update iteration threshold is met, so as to calculate the horizontal displacement accumulation error of the dynamic response of the high-rise building. The calculation formula of the horizontal displacement accumulation error is In the formula: represents the updated horizontal displacement estimation value of Kalman filtering, that is, the updated state estimation of the dynamic response of the high-rise building at the current iteration displacement in the formula; represents the measured horizontal displacement value according to the measurement equation; represents the cumulative error of the horizontal displacement; According to horizontal displacement cumulative error and cumulative error limit parameter Error correction is performed on the dynamic response of the high-rise building at the current iteration to obtain an optimized dynamic response. The expression for error correction of the dynamic response of the high-rise building at the current iteration is In the formula: represents the corrected dynamic response, i.e. the estimated value of the horizontal displacement of the high-rise building at the current iteration.

3. The method for evaluating dynamic comfort of an engineering structure under wind load according to claim 2, characterized in that, S5 specifically includes the following steps: S51: The acceleration time series signal, the weighted average instantaneous frequency, the maximum instantaneous amplitude, and the estimated displacement data are subjected to proportional normalization processing. The expression for the proportional normalization processing is In the formulae: denotes the proportionally normalized data; denotes a numerical set of acceleration time series signals, weighted average instantaneous frequencies, maximum instantaneous amplitudes, and estimated displacement data and ; denotes a first i set of numerical data and ; denotes the total number of numerical sets. S52: The normalized data are subjected to weighted calculation to obtain the comprehensive comfort score at each moment, and the expression for the weighted calculation of the normalized data is In the formula: represents the comprehensive comfort score of the high-rise building under the action of wind load; represents the weight of the comfort score; represents the normalized value of acceleration at the moment; represents the normalized value of instantaneous frequency at the moment; represents the normalized value of instantaneous amplitude at the moment; represents the normalized value of displacement at the moment; S53: The grade score threshold of the evaluation comfort level is set, and the grade score threshold met by the comprehensive comfort score is confirmed, so as to realize the dynamic comfort evaluation of the engineering structure under the action of wind load.

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