Challenges in developing confidence intervals on modal parameters estimated for large civil infrastructure with stochastic subspace identification

Carden, E. Peter; Mita, Akira

doi:10.1002/stc.358

Cited by 20 publications

(26 citation statements)

References 35 publications

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“…Since linear combinations of normally distributed random variables are normally distributed, it follows that the estimates of the modal characteristics are also asymptotically normally distributed up to first order accuracy [5]. It should be kept in mind though that these results are asymptotic in the number of data samples, and deviations from the normal distribution have been observed for short data sequences [27].…”

Section: System Matrices and Modal Characteristics: Probability Distrmentioning

confidence: 92%

Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications

Reynders

Maes

Lombaert

et al. 2016

Mechanical Systems and Signal Processing

157

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a b s t r a c tIdentified modal characteristics are often used as a basis for the calibration and validation of dynamic structural models, for structural control, for structural health monitoring, etc. It is therefore important to know their accuracy. In this article, a method for estimating the (co) variance of modal characteristics that are identified with the stochastic subspace identification method is validated for two civil engineering structures. The first structure is a damaged prestressed concrete bridge for which acceleration and dynamic strain data were measured in 36 different setups. The second structure is a mid-rise building for which acceleration data were measured in 10 different setups. There is a good quantitative agreement between the predicted levels of uncertainty and the observed variability of the eigenfrequencies and damping ratios between the different setups. The method can therefore be used with confidence for quantifying the uncertainty of the identified modal characteristics, also when some or all of them are estimated from a single batch of vibration data. Furthermore, the method is seen to yield valuable insight in the variability of the estimation accuracy from mode to mode and from setup to setup: the more informative a setup is regarding an estimated modal characteristic, the smaller is the estimated variance.

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Section: System Matrices and Modal Characteristics: Probability Distrmentioning

confidence: 92%

Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications

Reynders

Maes

Lombaert

et al. 2016

Mechanical Systems and Signal Processing

157

View full text Add to dashboard Cite

show abstract

“…Then, q sets of structural modal parameters (modal frequency, damping ratio, and model shape) need to be obtained for later use. The modal parameters can be easily identified using popularly used modal identification algorithms, such as the random decrement technique, Hilbert‐Huang transform, and stochastic subspace identification . After that, several accelerometers should be arranged on the structure to measure dynamic responses constantly.…”

Section: Methodsmentioning

confidence: 99%

“…The modal parameters can be easily identified using popularly used modal identification algorithms, such as the random decrement technique, 48 Hilbert-Huang transform, 49,50 and stochastic subspace identification. 51,52 After that, several accelerometers should be arranged on the structure to measure dynamic responses constantly. The second step is Kalman filtering operation for identification of earthquake ground motion.…”

Section: Implementation Summarymentioning

confidence: 99%

Identification of earthquake ground motion based on limited acceleration measurements of structure using Kalman filtering technique

Luo

Wan

et al. 2019

Struct Control Health Monit

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Summary Identification of earthquake ground motion from structural health monitoring (SHM) data provides a good means to reconstruct seismic loads that are essential for postearthquake safety assessments and disaster simulations of structures. Because the data measured by an SHM system are structural absolute response, they cannot be directly applied to the structural motion equation, which is established in relative coordinate system. As such, this paper originally derives the motion equation in absolute coordinate system and then expands the equation into modal space. In addition, the proposed method allows for identifying earthquake ground motion using incomplete modal information and limited measurements through the standard Kalman filter. Subsequently, a numerical two‐dimensional frame is used to validate the feasibility of the proposed method, and the influences of modal parameters and measurement noise on the identification accuracy are also fully investigated. The results show that the proposed method is sensitive to frequency and measurement noise but insensitive to modal shape and damping ratio. It is also found that the identified ground motion subjected to certain measure noise can still be reliably employed for postseismic response calculations of medium‐ and long‐period structures. Finally, a shaking table test performing on a five‐floor frame further demonstrates the effectiveness and accuracy of the proposed identification algorithm for practical application.

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“…The data-driven stochastic subspace identification (SSI-DATA) method [23][24][25] is adopted to identify the modal parameters. The SSI-DATA method is formulated in the state space in the form…”

Section: Simulative Experimental Verificationmentioning

confidence: 99%

“…It shows that the first six natural frequencies can be identified accurately using SSI-DATA when 30 triaxial sensors are optimally placed on the structure based on the second proposed method. The corresponding relative error is defined in Equation (25). Although the noise has Figure 11.…”

Section: Simulative Experimental Verificationmentioning

confidence: 99%

Optimal multiaxial sensor placement for modal identification of large structures

Lian

et al. 2013

Struct. Control Health Monit.

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Research on optimal sensor placement has become a very important topic because of the need to obtain effective testing results with limited testing resources in modal identification and structural health monitoring. An integer-encoding multi-swarm particle swarm optimisation (IMPSO) algorithm is proposed to place multiaxial sensors optimally on large structures for modal identification. The concepts of grade evaluation and migration strategy and the mutation operators of genetic algorithm are introduced into the integer-encoding particle swarm optimisation algorithm. Three different fitness functions for optimal multiaxial sensor placement (OMSP) are investigated. The second fitness function considers spatial correlation based on Moran's I to solve the information redundancy of multiaxial sensor placement, whereas the other two functions evolve from the existing methods for comparison with the second fitness function. The novel algorithm and three fitness functions are further applied to the Laxiwa arch dam for verifications. The results show that the proposed IMPSO outperforms two existing algorithms in its global optimisation capability. The results also prove that the second fitness function has advantages in sensor distribution and ensuring the well-conditioned information matrix and observability of multidimensional modal shapes. The multiaxial sensor placement scheme determined by the proposed method is applied to the modal test of the Laxiwa arch dam under simulative ambient excitation. The results show that the scheme determined by the second fitness function can identify the frequencies and multidimensional mode shapes accurately, indicating that this method may be used to provide guidance for OMSP in various types of large structures.An FE model of the full-scale arch dam is shown in Figure 2. We adopt 3D brick elements to simulate the arch dam and its foundation. The FE model of the arch dam has 18 306 nodes and 15 144 3D brick elements. The material parameters of the concrete dam are listed as follows: the dynamic module is 33 000 MPa, the mass density is 2400 kg/m 3 and the Poisson's ratio is 0.167. The material parameters of the foundation are as follows: the dynamic module is 22 000 MPa, the mass density is 2700 kg/m 3 and the Poisson's ratio is 0.25. The effect of hydrodynamic pressure is performed using the added mass to calculate the wet modal parameters of the arch dam. The Cartesian coordinate system of the model is shown in Figure 2.According to the feasibility of placement, 578 nodes downstream of the dam are selected as the candidate nodes (Figure 3). The candidate nodes are numbered 1 to 578, from left to right and top to bottom. The low frequency modes with higher modal participation factors can usually give sufficient information to describe the dynamic behaviour of a large structural system, so the first six modes are selected as the concerned modes. The first six frequencies and mode shapes are calculated using the FE model and illustrated in Figure 4. To monitor the spatial vibration ...

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Challenges in developing confidence intervals on modal parameters estimated for large civil infrastructure with stochastic subspace identification

Cited by 20 publications

References 35 publications

Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications

Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications

Identification of earthquake ground motion based on limited acceleration measurements of structure using Kalman filtering technique

Optimal multiaxial sensor placement for modal identification of large structures

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