Symbolization-based differential evolution strategy for identification of structural parameters

Li, Rongshuai; Mita, Akira; Zhou, Jin

doi:10.1002/stc.1530

Cited by 12 publications

(11 citation statements)

References 26 publications

(26 reference statements)

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“…In fact, the state of the art of these methods has been reviewed in a number of works . According to most of these works, system identification methods can be classified as parametric and non‐parametric (genetic algorithms , evolutionary strategy , neural networks or least‐squares estimation ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of measurement and simulation errors in structural system identification by observability techniques

Lei

Galant

Nogal

et al. 2016

Struct. Control Health Monit.

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This is the peer reviewed version of the following article: [Lei, J., Lozano-Galant, J. A., Nogal, M., Xu, D., and Turmo, J. (2017) Analysis of measurement and simulation errors in structural system identification by observability techniques. Struct. Control Health Monit., 24: . doi: 10.1002/stc.1923.], which has been published in final form at http://onlinelibrary.wiley.com/wol1/doi/10.1002/stc.1923/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.During the process of structural system identification, errors are unavoidable. This paper analyzes the effects of measurement and simulation errors in structural system identification based on observability techniques. To illustrate the symbolic approach of this method a simply supported beam is analyzed step-by-step. This analysis provides, for the very first time in the literature, the parametric equations of the estimated parameters. The effects of several factors, such as errors in a particular measurement or in the whole measurement set, load location, measurement location or sign of the errors, on the accuracy of the identification results are also investigated. It is found that error in a particular measurement increases the errors of individual estimations, and this effect can be significantly mitigated by introducing random errors in the whole measurement set. The propagation of simulation errors when using observability techniques is illustrated by two structures with different measurement sets and loading cases. A fluctuation of the observed parameters around the real values is proved to be a characteristic of this method. Also, it is suggested that a sufficient combination of different load cases should be utilized to avoid the inaccurate estimation at the location of low curvature zones.Peer ReviewedPostprint (author's final draft

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Section: Introductionmentioning

confidence: 99%

Analysis of measurement and simulation errors in structural system identification by observability techniques

Lei

Galant

Nogal

et al. 2016

Struct. Control Health Monit.

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“…Symbolic time series analysis allows capturing the main features of the underlying system whilst alleviating the effects of harmful noise. The effectiveness of STSA in noisy condition has been demonstrated in several researches [25][26][27]. The results of research presented in [25] show that compared with artificial NN-based method and support vector machine-based method, STSA-based approach provides more robust damage indices in presence of measurement noise.…”

Section: A Background On Symbolic Time Series Analysismentioning

confidence: 98%

“…The effectiveness of STSA in noisy condition has been demonstrated in several researches [25][26][27]. The results of research presented in [25] show that compared with artificial NN-based method and support vector machine-based method, STSA-based approach provides more robust damage indices in presence of measurement noise. In another research, it has been indicated that symbolization can reduce sensitivity to measurement noise [26].…”

Section: A Background On Symbolic Time Series Analysismentioning

confidence: 98%

Damage localization based on symbolic time series analysis

Alamdari

Samali

2014

Struct. Control Health Monit.

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Summary The objective of this paper is to localize damage in a single or multiple state at early stages of development on the basis of the principles of symbolic dynamics. Symbolic time series analysis (STSA) of noise‐contaminated responses is used for feature extraction to detect and localize a gradually evolving deterioration in the structure according to the changes in the statistical behaviour of symbol sequences. Basically, in STSA, statistical features of the symbol sequence can be used to describe the dynamic status of the system. Symbolic dynamics has some useful characteristics making it highly demanded for implementation in real‐time observation application such as SHM. First, it significantly reduces the dimension of information and provides information‐rich representation of the underlying data. Second, symbolic dynamics and the set of statistical measures built upon it represent a solid framework to address the main challenges of the analysis of nonstationary time data. Finally, STSA often allows capturing the main features of the underlying system whilst alleviating the effects of harmful noise. The method presented in this paper consists of four primary steps: (i) acquisition of the time series data; (ii) creating the symbol space to produce symbol sequences on the basis of the wavelet transformed version of time series data; (iii) developing the symbol probability vectors to achieve anomaly measures; and (iv) localizing damage on the basis of any sudden variation in anomaly measure of different locations. The method was applied on a flexural beam and a 2‐D planar truss bridge subjected to varying Gaussian excitation in presence of 2% white noise to examine the efficiency and limitations of the method. Simulation results under various damage conditions confirmed the efficiency of the proposed approach for localization of gradually evolving deterioration in the structure; however, for the future work, the method needs to be verified by experimental data. Copyright © 2014 John Wiley & Sons, Ltd.

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“…One possible solution maybe substructure level damage identification adapted to the characteristics of each part, for instance, the cable and deck together for the suspension bridge. Furthermore, the damage is basically a local phenomenon, the damage detection methods based on the direct identification of local physical parameters, for instance, the stiffness and damping coefficients (Zhan et al 2014, Nayeri et al 2008, statistical properties of structural responses (Li et al 2013, Alamdari et al 2015 and wave propagation based methods may be feasible (Mohammadtaghi et al 2015).…”

Section: Damsmentioning

confidence: 99%

Structural health monitoring of innovative civil engineering structures in Mainland China

Li¹,

Li²,

Ren³

et al. 2016

Structural Monitoring and Maintenance

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This paper describes the backgrounds, motivations and recent history of structural health monitoring (SHM) developments to various types of engineering structures. Extensive applications of SHM technologies in bridges, high-rise buildings, sport avenues, offshore platforms, underground structures, dams, etc. in mainland China are summarily categorized and listed in tables. Sensors used in implementations, their deployment, damage identification strategies if applicable, preliminary monitoring achievements and experience are presented in the lists. Finally, existing problems and promising research efforts in civil SHM are discussed, highlighting challenges and future trends.

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Symbolization-based differential evolution strategy for identification of structural parameters

Cited by 12 publications

References 26 publications

Analysis of measurement and simulation errors in structural system identification by observability techniques

Analysis of measurement and simulation errors in structural system identification by observability techniques

Damage localization based on symbolic time series analysis

Structural health monitoring of innovative civil engineering structures in Mainland China

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