Comparison of adaptive structural damage identification techniques in nonlinear hysteretic vibration isolation systems

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

doi:10.1007/s11803-013-0204-y

Cited by 5 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This problem is particularly important in work focused on the use of elastic materials with nonlinear characteristics. Numerous vibration-isolating materials, such as polymers, are characterised by nonlinearity, which significantly hinders the setting of parameters for the model [57][58][59][60]. Moreover, there are methods that indirectly measure material parameters, e.g., Young's modulus can be obtained through the evaluation of material hardness.…”

Section: Introductionmentioning

confidence: 99%

A Low-Stress Method for Determining Static and Dynamic Material Parameters for Vibration Isolation with the Use of VMQ Silicone

Nering

2023

Materials

View full text Add to dashboard Cite

Progressive urbanisation causes building users to be affected by increasing amounts of noise and vibrations that come from transportation and other building users. This article presents a test method that can be used to identify quantities of methyl vinyl silicone rubber (VMQ) necessary to carry out solid mechanics finite element method simulations such as Young’s modulus, Poisson ratio, and damping parameters. These parameters are necessary to model the vibration isolation used for protection against noise and vibration. The article uses an original combination of dynamic response spectrum and image processing methods to determine these quantities. The tests were carried out using one machine for the range of normal compressive stresses of 64–255 kPa with cylindrical samples of various shape factors in the range of 1–0.25. The parameters for the simulation of solid mechanics in statics were obtained from image processing based on the deformation of the sample under load; for dynamic solid mechanics, the parameters were obtained from the response spectrum of the tested system. The article shows the possibility of determining the given quantities using the original method of the synthesis of dynamic response and FEM-supported image analysis, which states the article’s novelty. Additionally, limitations and preferred ranges of sample deformation in terms of load stress and shape factor are presented.

show abstract

Section: Introductionmentioning

confidence: 99%

A Low-Stress Method for Determining Static and Dynamic Material Parameters for Vibration Isolation with the Use of VMQ Silicone

Nering

2023

Materials

View full text Add to dashboard Cite

show abstract

“…These algorithms rely only on the analysis of structural mechanical behavior to obtain the modal parameters of the system, such as frequency, damping ratio, mode shape, and stiffness. At present, most vibration-based SI algorithms are non-parametric (e.g., short-time Fourier transform, 11 Volterra and Wiener series representations, 12 Hilbert Huang transform, 13 blind source separation, 14 nonlinear subspace identification, 15 conjugate pair decomposition, 16 stochastic subspace identification and deterministic subspace identification, 17 independent component analysis, 18 second-order blind identification, 19 fractal dimension, 20 neural networks, 21 and Hilbert vibration decomposition 22 ). The non-parametric algorithms have no prior assumptions about structural vibration forms; a set of equations without an explicit physical meaning are used to characterize the input–output relationship of the system.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Currently, online joint estimation of states and hysteretic parameters of nonlinear structures is a challenging research area in the engineering field. In recent years, extensive research studies such as the methods of the extended Kalman filter (Astroza et al, 2019; Ebrahimian et al, 2015; Yang and Ma, 2003; Zhang et al, 2002), least-squares estimation (Huang et al, 2009; Mu et al, 2013; Wu et al, 2015; Yang et al, 2006, 2012; Yang and Huang, 2007), unscented Kalman filter (Al-Hussein and Haldar, 2015; Asgarieh et al, 2014; Astroza et al, 2017; Astroza and Alessandri, 2019; Wu and Smyth, 2008), ensemble Kalman filter (Ghanem and Ferro, 2006), particle filter (Chatzi and Smyth, 2009; Wan et al, 2018) have been developed. In fact, it can be said that among the proposed methods, Bayesian inference–based techniques provide a robust solution for system identification using probabilistic logic and have been widely used for engineering applications (Huang and Beck, 2018; Yuen and Mu, 2015; Yuen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Online jointly estimation of hysteretic structures using the combination of central difference Kalman filter and Robbins–Monro technique

Tehrani

Bakhshi

Yang

2020

Journal of Vibration and Control

View full text Add to dashboard Cite

Rapid assessment of structural safety and performance right after the occurrence of significant earthquake shaking is crucial for building owners and decision-makers to make informed risk management decisions. Hence, it is vital to develop online and pseudo-online health monitoring methods to quantify the health of the building right after significant earthquake shaking. Many Bayesian inference–based methods have been developed in the past which allow the users to estimate the unknown states and parameters. However, one of the most challenging part of the Bayesian inference–based methods is the determination of the parameter noise covariance matrix. It is especially difficult when the number of unknown states and parameters is large. In this study, an effective online joint estimation method for nonlinear hysteretic structures, with consideration of degradation and pinching phenomena, is proposed. Simultaneous estimation of states and parameters is conducted using the combination of a central difference Kalman filter as an effective estimator and the Robbins–Monro stochastic approximation technique as the parameters noise covariance matrix regulator. The proposed algorithm is implemented on three shear buildings with 36, 54, and 90 unknown states and parameters. To verify the performance of the system identification method, robust simulations with synthetic measurement noises and modeling errors were generated using the Monte Carlo random simulation method. The result shows the proposed method can be used to estimate the unknown parameters and states of highly nonlinear systems efficiently and effectively.

show abstract

Comparison of adaptive structural damage identification techniques in nonlinear hysteretic vibration isolation systems

Cited by 5 publications

References 16 publications

A Low-Stress Method for Determining Static and Dynamic Material Parameters for Vibration Isolation with the Use of VMQ Silicone

A Low-Stress Method for Determining Static and Dynamic Material Parameters for Vibration Isolation with the Use of VMQ Silicone

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

Online jointly estimation of hysteretic structures using the combination of central difference Kalman filter and Robbins–Monro technique

Contact Info

Product

Resources

About