Experimental Study of an Adaptive Sequential Nonlinear LSE with Unknown Inputs for Structural Damage Tracking

Mu, Tengfei; Zhou, Li; Yang, Jann N.

doi:10.1155/2014/294163

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…The key issues are identifying a minimum and examining if the obtained minimum is a global minimum. Researchers have employed parametric algorithms for identifying time-varying modal parameters of civil structures (e.g., time-varying autoregressive with exogenous input model, 5 adaptive sequential nonlinear least square estimation with unknown inputs, 6 time-varying weighted nonlinear least squares estimator, 7 kernel principal component analysis, 8 functional series time-dependent autoregressive moving average, 9 and iterated unscented Kalman filter 10 ). However, the SI of complex structures with a large number of freedom (DOFs) requires estimating numerous modeling parameters when using numerical models.…”

Section: Introductionmentioning

confidence: 99%

Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures

Zhang

Shan

2021

Structural Health Monitoring

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Subspace-based system identification algorithms have been developed as an advanced technique for performing modal analysis. We introduce a novel tensor subspace-based algorithm to identify the time-varying modal parameters of bridge structures. A new time dimension is introduced in the traditional Hankel matrix, and a mathematical model of tensor subspace decomposition is established. Combined with the stabilization diagram, tensor parallel factor decomposition is used to estimate the frequencies, mode shapes, and modal damping ratios. The effectiveness of the proposed algorithm is validated by comparing it with the classical sliding-window–based stochastic subspace algorithm on a model cable-stayed bridge dynamic test. The proposed algorithm is further applied to process the dynamic responses of a real bridge health monitoring system to identify its time-varying modal frequencies. Our results demonstrated that the proposed algorithm significantly reduces computational efforts and extends the range of solution ideas for future out-only time-varying system identification problems.

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Section: Introductionmentioning

confidence: 99%

Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures

Zhang

Shan

2021

Structural Health Monitoring

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“…Yang et al [14] proposed arecursive least-squares estimation with unknown input (RLSE-UI) algorithm to identify unknown parameters and input. Later, Yang and Huang [15] proposed a refined algorithm referred to as adaptive sequential nonlinear leastsquares estimation with unknown input (ASNLSE-UI) for real-time identification of damage and input, and a series of experiments are performed to verify the applicability of this algorithm [16]. Zhu and Law [17] proposed a two-step inversion method to identify the moving loads and damage of an Euler-Bernoulli beam.…”

Section: Introductionmentioning

confidence: 99%

An Improved Sensitivity Method for the Simultaneous Identification of Unknown Parameters and External Loads of Nonlinear Structures

Wang

2018

Shock and Vibration

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Because structures may be subject to unknown loads and may simultaneously involve unknown parameters and because simple load identification or parameter identification algorithms cannot be applied under such conditions, it is necessary to seek algorithms that can simultaneously identify unknown parameters and external loads of structures. The sensitivity method is one of them, and this paper extends this method to nonlinear structures. In addition, the key issues associated with the sensitivity method are systematically studied, and suggestions for improvement are put forward, including the use of the difference method instead of the derivative method to calculate the sensitivity, the use of a fixed regularization parameter instead of the traditional regularization parameter calculation methods, and measures for guarantee of iterative convergence. The improved sensitivity method is applied to two types of nonlinear structures, and the effects of the regularization parameter, distribution of measured points, response types, noise levels, and the magnitude of the perturbation on the identified results are discussed.

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Comparison of the performance of nonlinear Kalman filter based algorithms for state‐parameter identification of base isolated structures

Paul

Dutta

Deb

2022

Structural Contr & Hlth

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Summary In this paper, different variants of Kalman filter based algorithms extended Kalman filter (EKF), two‐stage EKF, and unscented Kalman filter (UKF) are used to estimate and compare the performance of these algorithms in identifying the state and parameters of nonlinear hysteretic model, simulating unbonded fibre reinforced elastomeric isolator (U‐FREI). A two‐story masonry building supported on U‐FREI is utilized for the present study. The model is excited on a shake table using recorded excitations and measured responses are considered for the identification study. Since the hysteretic behavior of U‐FREI is different from that of conventional steel reinforced elastomeric isolator, U‐FREI is modeled using extended Bouc–Wen model. The considered model comprises of mainly six parameters and the identified parameters as obtained from various considered algorithms as well for different input excitations are observed to be different. In view of this, percentage error index (PEI) has been introduced to compare the performance of these algorithms. PEI is evaluated using the parameter weights obtained from parameter sensitivity analysis in the form of spider diagram. The state and parameters of the U‐FREI are also identified using noise corrupted responses from a numerically simulated model of the same considered two‐story masonry building. The responses are contaminated with varying level of artificially added Gaussian white noise. It is observed that the performances of these algorithms are quite stable and are unaffected due to artificially added noise. Further, studies have also been carried out for proper selection of measurement noise covariance and a significant influence on the performance of these algorithms are observed.

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Experimental Study of an Adaptive Sequential Nonlinear LSE with Unknown Inputs for Structural Damage Tracking

Cited by 3 publications

References 35 publications

Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures

Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures

An Improved Sensitivity Method for the Simultaneous Identification of Unknown Parameters and External Loads of Nonlinear Structures

Comparison of the performance of nonlinear Kalman filter based algorithms for state‐parameter identification of base isolated structures

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