The Use of Vibration Signals for Structural Health Monitoring, System Identification, Test Planning/Optimization, and Dynamic Model Validation/Updating
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Identification of Torsionally Coupled Shear Buildings Models Using a Vector Parameterization
Abstract
A methodology to estimate the shear model of seismically excited, torsionally coupled buildings using acceleration measurements of the ground and floors is presented. A vector parameterization that considers Rayleigh damping for the building is introduced that allows identifying the stiffness/mass and damping/mass ratios of the structure, as well as their eccentricities and radii of gyration. This parameterization has the advantage that its number of parameters is smaller than that obtained with matrix parameterizations or when Rayleigh damping is not used. Thus, the number of spectral components of the excitation signal required to identity the structural parameters is reduced. To deal with constant disturbances and measurement noise that corrupt acceleration measurements, Linear Integral Filters are used that guarantee elimination of constant disturbances and attenuation of noise.
1. Introduction
The parameter identification of torsionally coupled shear building models has been a topic of interest in the last three decades [1â€“13]. Its identification is important because it allows verifying the structural health, or designing control law techniques that attenuate the vibration of the building when it is excited by external forces as earthquakes or wind [14â€“16]. This kind of model more closely approximates a shear building than the planar frame model, which is widely used in the literature [17â€“19]. The reason is that most buildings present torsional movements under purely translational excitations since their centers of resistance and mass do not generally coincide [20]. Although in some identification schemes buildings are modeled as two independent planar frames where torsional motion is neglected, the focus in this paper is in recovering the model of buildings where the centers of mass and the centers of torsion in each floor do not coincide and, therefore, significant torsional motions are expected. There are significant differences between the responses of these two approaches, whose comparison is beyond the scope of this paper.
References [1â€“8] propose techniques to estimate the modal parameters of torsionally coupled buildings using acceleration measurements. Li and Mau [1] present a methodology that identity the modal parameters by minimizing the error between the measured accelerations and the ones predicted by the solution of the Duhamel integral. Ueng et al. [2], Lin et al. [3], and Nayeri et al. [4] obtain the parameters of a structure excited by means of ambient vibrations; [2, 3] combine an extended decrement random method and the Ibrahim Time Domain estimation technique, whereas [4] identifies a fullscale 17story building using the NExT/ERA (Natural Excitation Technique in conjunction with the Eigensystem Realization Algorithm) and a time domain identification technique for chainlike MDOF systems. In [5, 6], identification techniques that employ the Eigensystem Realization Algorithm (ERA) in order to generate a building state space model are proposed. Hegde and Sinha [6] use only acceleration measurements of the top and first floor levels, but their method is applied only to structures whose masses and eccentricities are equal for all stories. Antonacci et al. [7] identify the modal parameters by means of the following methods: Enhanced Frequency Domain Decomposition (EFFD), ERA, Stochastic Subspace Identification (SSI), and TimeFrequency Instantaneous Estimators (TFIE). The algorithms in [8, 9] estimate the stiffness matrix of torsionally coupled buildings; Torkamani and Ahmadi [8] firstly identify the natural frequencies and modal shapes by means of the Fourier spectra of the acceleration of the floors; then, a parameter identification technique, which assumes knowledge of the building floors masses, is used to obtain the stiffness matrix. Omrani et al. [9] obtain the stiffness matrix from a methodology that uses the structure response to ambient excitation and the knowledge of all the masses and eccentricities of the floors. Wang et al. [10] proposed a procedure that combines the identification technique SRIM (System Realization using Information Matrix) and a damage index in order to estimate the damage of torsional coupled buildings. AngelesCervantes and AlvarezIcaza [13] propose a technique that combines the online Least Squares Method with a parametrization of the building, which in the sequel will be called matrix parameterization, that is used for estimating the complete matrices and of the structure, where , , and are the mass, stiffness, and damping matrices, respectively; however, the identified matrices are overparameterized and, as a consequence the stiffness/mass and damping/mass ratios of the structure cannot be uniquely identified; in addition, with this parameterization, the zeros entries of the matrices and are also identified, which greatly increases the number of parameters to be estimated. Finally, [11, 12] propose an identification approach, based on the unscented Kalman filter, that identifies the stiffness and damping parameters of a torsionally coupled building, as well as the BoucWen model parameters that represent the hysteretic response of each lateral load resisting elements of the structure; the algorithm in [12] is an extension of [11] and also estimates the mass eccentricities of the building; the unscented Kalman filter, proposed in these two references, uses only acceleration measurements and also estimates the velocity and displacement of the structure, thus avoiding the numerical integration of the acceleration measurements, which leads to velocity and displacement histories that drift away linearly and quadratically with the time, respectively [21]. The approach in [11] attenuates measurement noise; however, the constant disturbance voltage in accelerometers output cannot be eliminated by a Kalman filter. The usual approach to eliminate these constant disturbances is the offline processing of the accelerometer signals.
This paper presents an identification technique that estimates the parameters of a torsionally coupled building model, which exhibits torsionally movements under purely translational excitations due to a seismic event. A vector parameterization of the building is proposed that has the following characteristics: (1) it assumes that the structure has classical Rayleigh damping and (2) it contains a vector whose entries depend on the building stiffness/mass ratios, which are estimated using accelerations measurements of the ground and floors. Once that these ratios are identified, it is possible to estimate the building damping/mass ratios and the modal parameters; additionally, it is also possible to identify the radii of gyration and eccentricities of the floors and the stiffness, damping, and mass matrices of the structure.
In contrast to the estimation methods in [6, 8, 9], the proposed technique does not assume that the eccentricities of all the floors are in the same direction or that all the floor masses are equal or known. The proposed vector parameterization is combined with the offline or with the online Least Squares Method (LSM) and can be used for estimating the complete model of the structure if all the floors are instrumented or be employed for identifying a reduced model of the building, if only some floors are equipped with accelerometers.
Assuming Rayleigh damping for the structure permits reducing the number of parameters contained in the proposed vector parameterization, since it is composed only of stiffness/mass ratios instead of stiffness/mass and damping/ratios as in the matrix parameterization [13] or the work of Omrani et al. [12]. The number of parameters contained in the vector parameterization is thus reduced with the positive impact on the (1) reduction of the computational effort that allows implementing the online LSM and (2) the reduction of the spectral richness of the excitation signal required to uniquely estimate the structural parameters.
This paper extends the use of the Linear Integral Filters (LIF), first introduced in [19], to eliminate constant disturbances in the acceleration measurements and to attenuate measurement noise, to the parameter estimation of torsionally coupled building models, a more challenging problem when realtime estimation is desired, as the number of parameters involved in the torsion based model greatly increases.
The paper is organized as follows. Section 2 describes the model of a torsionally coupled multistory building. Section 3 presents the proposed vector parameterization and introduces the LIF. Section 4 shows both the offline and the online LSMs employed to estimate the stiffness/mass ratios and describes the methodology that allows estimating the damping/mass ratios, eccentricities, radii of gyration, and the mass, stiffness, and damping matrices of the structure. Experimental results in a fivestory torsional building obtained with the vector parameterization and the LSM are presented in Section 5; three cases are considered, the first where all the floors are instrumented, the second where only the first, third, and fifth stories are equipped with accelerometers, and the third where only the first and the top floors are instrumented. Finally, Section 6 establishes the conclusions of this paper.
2. Mathematical Model of a Torsionally Coupled Shear Building
Figure 1 shows a torsionally coupled shear building that is seismically excited, where the centers of mass and centers of resistance of the floors do not lie on one vertical axis. The building model is defined as [2, 10, 22, 23]where , , and are, respectively, the mass, stiffness, and damping matrices. Moreover, the variable denotes a zero matrix of size , and represents the absolute ground acceleration induced by an earthquake, which is given bywhere is the total number of floors and the terms and are the ground accelerations in the and directions, respectively. Furthermore, vector is defined aswhere , , is the displacement vector in the th coordinate and and are the first and the second time derivatives of . Vectors , , have the following structure:
Note from Figure 1 that each story has three displacements, two in the and directions relative to the ground and a rotation of its center of mass about the vertical axis. Moreover, the terms and in this figure denote the center of mass and the center of resistance of the corresponding story, respectively.
Mass matrix isThe entries and of are, respectively, diagonal matrices that contain the masses of the stories and the moment of inertia of them about the vertical axis that pass through their center of mass. These matrices are given bywhere is a square diagonal matrix with the elements of the vector on the main diagonal, and , are, respectively, the lumped mass and the moment of inertia at floor ; moreover, is the radius of gyration of the th floor around the vertical axis passing through its centers of mass. On the other hand, the structure of the stiffness matrix is the following:where the submatrices of have a size of and are defined as The elements and given in (8)â€“(11) are the equivalent stiffness between floors and along the  and axes, respectively; in (13) is the torsional stiffness of the th floor about a vertical axis at its center of resistance. Moreover, and with denote the static eccentricities in axis at floor with respect to stories and , respectively.
The damping of building (1) will be represented by a Rayleigh damping matrix, which is given bywhere and are constants computed by the next equation [17]:where and , , are the damping ratio and natural frequency of the th mode of the structure.
2.1. Assumptions
In order to apply the proposed identification technique, assume the following:(A1)Initial conditions and are zero, which is reasonable since the structure is at rest before an earthquake.(A2)Three acceleration measurements are available for each floor, two in the direction and one in the direction, or vice versa. Thus, with these measurements it is possible to obtain the acceleration of the centers of mass (CM) of the floors in the and directions, as well as their angular accelerations [3, 13]. Moreover, the ground accelerations in the and directions are also accessible.
Remark 1. In order to recover the acceleration of the CM of each floor with three accelerometers, it is necessary to determine the floorâ€™s CM position. This point can be deduced from the geometry of the story and the position and weight of its columns. Note that the column weight depends on its materials and dimensions.
Remark 2. The building can also be identified when only the three acceleration measurements of floors are available, where . In this case the torsional building is considered as a structure with floors, where every condensed floor of the building will consist of the instrumented level and all the floors beneath it that are not instrumented:â€‰(A3)Estimates and of two natural frequencies and of the structure are available. Note that these estimated frequencies can be extracted from Fourier spectra of the acceleration responses due to ambient or force excitations of the building [24].â€‰(A4)Estimates and of the damping ratios for the th and th modes are also accessible. These estimates can be obtained using the recommended damping values for buildings, which depend on the structure materials and can be found in Table from [17]. The next values of damping are recommended, 12% for steel buildings and 3â€“5% for reinforced concrete buildings [18]. This assumption allows computing the parameters and in (14) and (15) as follows:
Remark 3. The proposed identification method does not need the exact knowledge of the natural frequencies and used to compute the parameters and . This fact is reflected in Section 5.1, where the proposed method estimates practically the same model in the next three cases: (a) , ; (b) , ; and (c) , .â€‰(A5)All the acceleration measurements are corrupted by constant disturbances and measurement noise; that is,â€‰Terms and are the measured accelerations of the ground in the and directions, respectively. Variables , , and are vectors that contain the measured accelerations of the centers of mass of the floors in the , , and directions, respectively. Moreover, the elements of the vectors , , , and and are constant disturbances. Finally, the entries of , , , and and are measurement noises.
Define the following vectors:
Substituting the signals and (17) in (2) and using the equations in (19) lead to
Now defineUsing this definition and (18) produceswhere .
3. Vector Parameterization
Expression (1) is equivalent to
Substituting from (14) into (23) gives
The product in (24) can be parameterized aswhere the vector contains the stiffness/mass ratios of the structure and is defined in (A.1); moreover,The matrix has the same structure as and uses the signals .
Similarly, the product in (24) is parameterized as follows:where is the time derivative of (26).
Equalities (25) and (29) allow writing expression (24) as
The Laplace transform of (30) is given by
Multiplying (31) by in order to obtain only acceleration signals leads to
Equation (32) is equivalent towhere is the second time derivative of in (26).
Substituting the entries of the vector from (22) into leads to The matrix has the same structure as and is composed of measured accelerations; moreover,where , , and in (35) are obtained by replacing the signals , , and with the disturbances , , and in the submatrices , , and given in (26). Similarly, , , and are derived by substituting , , and with the noises , , and in the submatrices , , and .
Replacing (20), (22), and (34) into (33) giveswith , , and . Note that variables and depend on the constant disturbances and the measured noise, respectively.
Equation (37) can be written in the time domain aswhere the superscript , , represents the th time derivative, , and .
In order to avoid the use of the derivatives of the measured signals , , and and to attenuate the term that depends on the measurement noise, (41) is integrated five times over finite time periods using Linear Integral Filters (LIF). Before carrying out this integration, it is useful to define the next operator [25]:where is the number of integrations over finite time periods and is the integration time period defined as , with and as the sampling period. The Laplace transform of (42) is given by
Applying the operator to (41) leads towhere the expression , in (45) and (46) is the binomial coefficient.
Finally, the next vectorial parameterization is obtained from (44):with , is the regressor, and is the vector containing the stiffness/mass ratios that will be estimated.
Proposition 4. The term in (47) converges to zero at .
Proof. See Appendix B.
Moreover, by substituting the variable (40) into (43) allows obtaining the Laplace transform of , which is given bywhere is the following fifthorder low pass filter:Its frequency response is given bywhere and determine the bandwidth of and are given in rad/s and Hz, respectively.
Let be all the natural frequencies of the structure, which are ordered from the lowest to the highest frequency. If the structure is completely instrumented, it is suggested that the frequency in (51) is selected such that it takes a value within the interval . Values of above this interval could deteriorate the performance of the identification method, since high frequency noise could be passed by the filters in (49). On the other hand, if only floors are instrumented, where (see Remark 2), it is recommended to select such that takes the value within ; for example, if a fivestory structure is instrumented in only four floors, then would be chosen such that . Note that, in the reduced measurement case, only the first natural frequencies of the torsional building will be estimated, since a story building model will be obtained.
4. Parameter Identification of the Building
4.1. Estimation of the Stiffness/Mass Ratios
The expression (47) is valid for , , where is the sampling period. Then, (47) can be written as
Omitting leads to
In order to estimate the stiffness/mass ratios of the structure, the vector parameterization is combined with the Least Squares Method (LSM) [26], which is given bywhere is the total number of samples of and .
The stiffness/mass ratios can be also estimated online by means of the recursive LSM, given bywhere is the output estimation error, , , is the forgetting factor, and is the covariance matrix such that . Note that the online LSM allows detecting damage in a seismically excited structure by observing the variations of the parameter estimates.
It is worth mentioning that, according to Proposition 4, taking samples of and from to a final time assures that the LSM is insensible to constant disturbances.
4.2. Estimation of the Modal Parameters
Once that vector has been estimated, it is possible to construct matrices and of the building model (23). Matrix is obtained through the next expression:
The natural frequencies and modal damping of the estimated building model can be obtained by means of the roots of the next characteristic polynomial corresponding to model (23):
The roots of polynomial (57) are given bywhere and , , are the estimated natural frequencies and damping ratios, respectively. They are computed as
It is worth mentioning that the estimated modal shape matrix can be obtained by computing the eigenvectors of the matrix .
4.3. Estimation of the Eccentricities and Radii of Gyration
In order to identify the eccentricities of the structure, assume that the estimates of in (A.5), , satisfy the following equalities:where . From (60), the next estimates of the eccentricities are given byA similar procedure allows obtaining the estimates of the eccentricities .
On the other hand, suppose that the estimates of in (A.7), , fulfill the next equality:Then, the radius of gyration can be estimated as
4.4. Estimation of the Mass, Stiffness, and Damping Matrices
The next proposed methodology uses the knowledge of a building floor mass in order to identify the mass, stiffness, and damping matrices of the structure. Employing the entries of the estimated vectors , , and of , , and given in (A.1) and assuming the knowledge of the mass , it is possible to compute the entries of the mass and stiffness matrices as follows:where , , and . The terms are given in (63); on the other hand, an estimate of the damping matrix can be computed by the next expression:where the entries of the matrix are the parameters given in (64); moreover, is defined in (56). The procedure is similar if any other mass is assumed to be known.
Remark 5. Note that a parameter projection scheme as the one presented in [19] can be applied to the estimates of the denominators in (61), (63), and (64) in order to avoid divisions by zero and to recursively identify the eccentricities, radii of gyration, and the stiffness, mass, and damping matrices.
5. Experimental Results
An experimental fivestory torsional building, which is shown in Figure 2, is used to verify the performance of the vector parameterization combined with the LSM. The signals (45) and (46) of the parameterization and the LSM are coded using the Matlab/Simulink software. All the experiments use a sampling time of 2â€‰ms and the integrals of and are computed through the trapezoidal numerical integration method. The experimental structure has dimensions â€‰cm and is mounted on a shaking table, which is actuated by servomotors from Parker model 406T03LXR, that moved it over the  and axes of the horizontal plane. Each floor is made from aluminum; moreover, the center of mass and resistance of each story do not coincide since only one column is made from aluminum and the remaining three columns are made from brass. Note that the stiffness of a brass column is larger that the one of an aluminum column. The dimensions of the columns of the first and the remaining four floors are â€‰cm and â€‰cm, respectively; in addition, the masses of the floors are approximately given by â€‰kg and â€‰kg. It is important to mention that the first floor is heavier that the others, since an iron mass of approximately â€‰kg is added to its edge. Twelve laser sensors from MicroEpsilon, model optoNCDT 1302, measure the absolute position of the shaking table and floors; these sensors are not used for parameter identification purposes. Two PCI6221 boards from National Instruments perform the data acquisition and their communication with a personal computer is carried out using the Matlab RealTime Windows Target toolbox. The absolute accelerations of the shaking table and floors are measured through dualaxis accelerometers from Analog Devices, model ADXL203, that have a range from â€‰g to â€‰g and can provide accelerations in the  and axes. The center of the shaking table contains an accelerometer that measures its acceleration in both axes; moreover, each floor is equipped with two accelerometers, represented as and , as shown in Figure 3; accelerometer provides the absolute accelerations of the center of mass (CM) of the floor in the and directions, which are denoted as and ; on the other side, accelerometer provides the absolute acceleration at the point in the direction; this acceleration is denoted as . In order to compute the angular acceleration of the th floor, the following approximation is employed [13]:where and are the absolute accelerations of the th floor at the points and , respectively, and 0.3â€‰m is the distance between the accelerometers in the direction, as depicted in Figure 3(a). On the other hand, the relative accelerations and of the th floor, which are employed in the matrices and in (45)(46), are obtained as follows:where and are the accelerations provided by the accelerometer of the shaking table.
(a) Position of the accelerometers
(b) Photo of the accelerometers
Let and , , be the natural frequencies of the building in rad/s and Hz, respectively. Table 1 presents the natural frequencies of the structure that are obtained by exciting it through a chirp signal from 0.1 to 30â€‰Hz. The natural frequencies are between and â€‰rad/s or and â€‰Hz, and they are also shown in Figure 4, which depicts the Fourier spectra of the acceleration measurements , , and of the second floor. This chirp signal is not used for estimating the parameter vector of the proposed parameterization given in (53). In order to estimate , the experimental structure is excited through the NorthSouth and EastWest components of the Mexico City 1985 earthquake, which is fitted in amplitude to be in agreement with the structure. Figures 5 and 6 depict the acceleration measurements of the shaking table and fourth floor, respectively; note that these measurements have offsets. Notice in Figure 4 that the lower frequencies in Table 1 have a small contribution in the spectra, a fact that will have an impact on the identification of the lower frequencies as it will be shown later.

(a) Fourier spectra of
(b) Fourier spectra of
(c) Fourier spectra of
(a) EastWest acceleration
(b) NorthSouth acceleration
(a) Measured acceleration
(b) Measured acceleration
(c) Measured acceleration
5.1. Experimental Results Using the Acceleration Measurements of All the Floors
This section presents the identified model of the structure obtained using acceleration measurements of all the floors and ground. It is assumed that estimates and of the damping ratios and of the structure are equal to . This assumption that the first and second modes of the structure are equal is reasonable based on experimental data [17]. The parameters and and the natural frequencies and are substituted into (16) in order to compute the constants and , corresponding to the Rayleigh damping (14), whose values are given byNote that these constants are employed in the signals (45) and in (46) of the proposed vector parameterization.
Tables 2 and 3 show, respectively, the identified natural frequencies and damping ratios obtained for three different values of the integration time period , which are â€‰s, â€‰s, and â€‰s. These modal parameters are computed using (59) and matrices and in (57), which are constructed using the vector estimate produced by the offline LSM (54), which takes samples of and from 5Î´ to 18â€‰s. Table 2 also shows the identification error in percentage (%) for the estimated natural frequency , which is defined aswhere ; note that the natural frequencies are identified with an error less than 15% and that the larger errors in Table 2 correspond to frequencies with low magnitude in Figure 4. On the other hand, the following index ,is also presented in Table 2 and it is computed in order to validate the quality of the identified model; the smaller the value of , the better the quality of the identified building model. Moreover, Figures 8(a) and 8(b) exhibit the graphs versus and versus , respectively. Note that relative low values of appear within â€‰s or within â€‰rad/s.
