Structural identification (St-Id) using finite element models for optimum sensor configuration and uncertainty quantification

Erdoğan, Yıldırım Serhat; Çatbaş, F. Necati; Bakır, Pelin Gündeş

doi:10.1016/j.finel.2013.10.009

Cited by 12 publications

(5 citation statements)

References 45 publications

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“…Similarly, PSO was hybridized with many other algorithms including, for example, GA [138,139], GA and ACO [140] and other swarm intelligence methods [141]. HS was hybridized with GA [142] and PSO/RO [143].…”

Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

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This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines.

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Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

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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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“…In the literature, many methods for SSI are proposed. Most of them are based on the SMM (see, e.g., [30][31][32][33][34]). The details of the main SSI methods are shown in the literature and addressed by [35].…”

Section: Introductionmentioning

confidence: 99%

Observing Material Properties in Composite Structures from Actual Rotations

Emadi,

Sun,

Lozano-Galant

et al. 2023

Applied Sciences

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The shear deflection effects are traditionally neglected in most structural system identification methods. Unfortunately, this assumption might lead to significant errors in some structures, like deep beams. Although some inverse analysis methods based on the stiffness matrix method, including shear deformation effects, have been presented in the literature, none of these methods are able to deal with actual rotations in their formulations. Recently, the observability techniques, one of the first methods for the inverse analysis of structures, included the shear effects into the system of equations. In this approach, the effects of the shear rotation are neglected. When actual rotations on-site are used to estimate the mechanical properties in the inverse analysis, it can result in serious errors in the observed properties. This characteristic might be especially problematic in structures such as deep beams where only rotations can be measured. To solve this problem and increase the observability techniques’ applicability, this paper proposes a new approach to include the shear rotations into the inverse analysis by observability techniques. This modification is based on the introduction of a new iterative process. To illustrate the applicability and potential of the proposed method, the inverse analysis of several examples of growing complexity is presented.

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Structural identification (St-Id) using finite element models for optimum sensor configuration and uncertainty quantification

Cited by 12 publications

References 45 publications

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

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

Observing Material Properties in Composite Structures from Actual Rotations

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