Multi-objective parameter identification of Euler–Bernoulli beams under axial load

Talic, Emir; Schirrer, Alexander; Kozek, Martin; Jakubek, Stefan

doi:10.1016/j.jsv.2014.12.012

Cited by 7 publications

(7 citation statements)

References 36 publications

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“…In the presence of modeling errors, there are no sufficiently accurate or “true” values of the model parameters, and the fitness function might not be significantly improved beyond a certain fitness threshold (Beck and Katafygiotis, ; Franco et al., ; Ulusoy et al., ). Because of the fitness‐oriented iterations, the identification procedures using single‐objective response matching may present the wrong or unstable system dynamics, even with a minimized output discrepancy and a stable iterative process (Talic et al., ). The candidate parameters may evolve to unfeasible values to compensate for the modeling errors, such as neglected DOFs, inexact modeling, and inappropriate assumptions (Smith and Saitta, ), which may be more pronounced than other issues such as incomplete measurements and noisy conditions (Haralampidis et al., ).…”

Section: Real‐world St‐idmentioning

confidence: 99%

“…The global optimum in performance objectives may provide an adequate model but not a sufficient one (Udwadia and Shah, ). According to the aforementioned publications and the illustrative examples, for the purpose of developing real‐world implementable methodologies (Waller, ), several characteristics of St‐Id may be desired as follows: (1) A stabilizing objective introduced along with the performance objective to improve the reliability and physical feasibility of the identified reduced‐order models (Shinozuka and Ghanem, ; Talic et al., ); (2) the generation of a population of candidate solutions with the output discrepancies below a composite error threshold to replace the unique and deterministic model (Haralampidis et al., ; Smith and Saitta, ); (3) validation data sets using unknown earthquake excitations to investigate the robustness of prediction (Luş et al., ; Haralampidis et al., ).…”

Section: Real‐world St‐idmentioning

confidence: 99%

“…Using computational intelligence, several multiobjective parameter and damage identification methods have been reported recently. The multiobjective configurations include combinations of frequencies and mode shapes (Haralampidis et al., ; Perera and Ruiz, ), diverse measurement sets under multiple load cases (Jung et al., ), the discrepancy and variance of the stochastic responses (Zhou et al., ), and the output errors along with the stability measure of the discretized partial differential equations (Talic et al., ). From these publications, it is indicated that the multiobjective constrained optimization may be necessary for parameter identification of large‐scale civil structures (Jiang and Adeli, ; Jiang and Adeli, , ; Catbas and Kijewski‐Correa, ; Amezquita‐Sanchez and Adeli, ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Seismic Data‐Driven Identification of Linear Models for Building Structures Using Performance and Stabilizing Objectives

Shan

Ouyang

Yuan

et al. 2016

Computer aided Civil Eng

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A two-stage dual-objective structural identification method is presented in this article. The complexity of the identification of story-level physical models for large-scale building structures is first addressed through a comparative study. A stiffness variation-based stabilizing objective is proposed to be necessarily incorporated into iterative optimization with the classical performance objectives to improve the model feasibility, and an area-type evaluation index is subsequently proposed for the stopping criteria. Accordingly, a two-stage differential evolution-based dual-objective optimization framework is presented for the computation of Pareto fronts for nondominated candidate solutions. Then, the proposed method is investigated using two illustrative examples, including a nine-story benchmark structure, and a real-world seven-story reinforced concrete structure. A series of condensed models are identified from the nondominated solutions on the Pareto front. The prediction performance of the single-objective optimal model and the dual-objective acceptable models is compared using the overall discrepancies of acceleration, interstory drift, and modal properties, within both estimation and

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Section: Real‐world St‐idmentioning

confidence: 99%

Section: Real‐world St‐idmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Seismic Data‐Driven Identification of Linear Models for Building Structures Using Performance and Stabilizing Objectives

Shan

Ouyang

Yuan

et al. 2016

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…Most of the damage identification problems are formulated as the single-objective optimization problem, whereby the goal of the optimization is to identify the damage state that best describes the observed sensor measurement. Some researchers employed the multiobjective optimization approach in order to improve the damage identification performance [8][9][10][11][12]. In particular, the multiobjective approach is shown to improve the robustness of damage identification when noise in the measurement is relatively high, or when available measurement is insufficient [8].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise

Jung

Song

2018

Shock and Vibration

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One of the important goals of structural health monitoring is to identify structural damage using measured responses. However, such damage identification is sensitive to noises in the response measurements. Even a small change in the measurement may result in a significantly biased damage assessment. e goal of this paper is to expand the multiobjective optimization approach developed for robust damage identification in order to facilitate its applications to more realistic bridge damage identification problems. Specifically, a benchmark problem on highway bridges, developed under the auspices of International Association for Bridge Maintenance and Safety (IABMAS), is investigated. Various issues regarding sensor noises, multiple measurements, and loading scenarios are addressed to improve the robustness of bridge damage identification. A major finding from this study is that the stochastic process of Pareto optimal solutions obtained in a single run not only captures the actual damage locations successfully but also provides useful information such as damage-detected ratio on the potential candidates for damage to be inspected on site. Moreover, it is shown through the success, failure, and partial detection rates that the robustness of the proposed approach can be improved by using appropriate excitation scenarios and multiple sets of measurement data.

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“…Aiming to reduce the above problems, it is necessary to obtain a method for solving ill-conditioned problem. Accordingly, regularization techniques, which can treat the ill-conditioned problem, have been utilized in many fields [10][11][12][13]. In the past few years, maximum entropy (ME) regularization techniques have been put forward one after another, which has been applied successfully in the wide areas of image reconstruction, signal processing, force identification problem, and so on [14], and the advantages of using ME regularization technique are as follows: firstly, some important information from the incomplete data can be extracted; then the probability distribution of the constraint is hidden in it; and, finally, entropy term can be changed according to different objects [15].…”

Section: Introductionmentioning

confidence: 99%

Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient

Ren

Wang

Liu

2017

Mathematical Problems in Engineering

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We propose a novel mathematical algorithm to offer a solution for the inverse random dynamic force identification in practical engineering. Dealing with the random dynamic force identification problem using the proposed algorithm, an improved maximum entropy (IME) regularization technique is transformed into an unconstrained optimization problem, and a novel conjugate gradient (NCG) method was applied to solve the objective function, which was abbreviated as IME-NCG algorithm. The result of IME-NCG algorithm is compared with that of ME, ME-CG, ME-NCG, and IME-CG algorithm; it is found that IME-NCG algorithm is available for identifying the random dynamic force due to smaller root mean-square-error (RMSE), lower restoration time, and fewer iterative steps. Example of engineering application shows that L-curve method is introduced which is better than Generalized Cross Validation (GCV) method and is applied to select regularization parameter; thus the proposed algorithm can be helpful to alleviate the ill-conditioned problem in identification of dynamic force and to acquire an optimal solution of inverse problem in practical engineering.

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Multi-objective parameter identification of Euler–Bernoulli beams under axial load

Cited by 7 publications

References 36 publications

Seismic Data‐Driven Identification of Linear Models for Building Structures Using Performance and Stabilizing Objectives

Seismic Data‐Driven Identification of Linear Models for Building Structures Using Performance and Stabilizing Objectives

Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise

Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient

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