Review of the Application of Finite Element Model Updating to Civil Structures

Wang, De Jun; Tan, ZC; Yang, Li; Liu, Yang

doi:10.4028/www.scientific.net/kem.574.107

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…In recent years, MGA has been extended to the field of civil engineering. Although some progress has been made in the field of FEMU, it still faces the following problems: (1) difficult in finding multiple alternatives of the error objective function and selecting a set of “correct solution” from the multiple solutions [ 17 , 18 , 19 , 20 , 21 , 22 ]; (2) low computation efficiency [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Regarding the first question, relevant scholars carried out the following research: Zárate et al [ 18 ] proposed a FEMU technology based on hop skip and jump algorithm (HSJA), which can find multiple solutions of the objective function.…”

Section: Introductionmentioning

confidence: 99%

“…The commonly used proxy models are the response surface model, Kriging model, and neural network proxy model [ 24 , 25 , 26 ]. The Kriging model is composed of polynomial regression model and random refinement simulation, which can fit high-order nonlinear problems, and has been successfully applied to geological, mechanical engineering, aerospace, and other fields [ 27 , 28 ]. However, the Kriging model is still limited in the field of large-scale structural FEMU.…”

Section: Introductionmentioning

confidence: 99%

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Research on Multi-Alternatives Problem of Finite Element Model Updating Based on IAFSA and Kriging Model

Kang

Zhang

Cao

et al. 2020

Sensors

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Due to insufficient test data, insufficient constraint equations and uncertain objective function, the local optimal solution and the global optimal solution of the objective function in finite element model updating may represent the actual parameters of the structure. Based on this, this paper proposes an improved artificial fish school algorithm. By combining the niche technology with the artificial fish school algorithm, the improved algorithm can systematically find multiple global optimal solutions and local optimal solutions of the objective function. Aiming at the difficulty of determining the niche radius, an adaptive niche radius mechanism is proposed. The improved algorithm is used to study the multi-alternatives problem of finite element model updating after verifying its feasibility through numerical simulation analysis. In the case of benchmark framework model updating, it is confirmed that multi-alternative problems exist and the global optimal solution of the objective function does not necessarily represent the true parameters of the structure. In case 2, the improved algorithm combined with the Kriging model is applied to the model updating of a cable-stayed footbridge, and 15 sets of solutions are obtained, in which the error objective function values of the measured and theoretical values of the bridge modes are close but the solutions are completely different. Combining with the actual bridge condition and reanalysis technology, the author takes the suboptimal solution 2 as the most representative solution of the bridge parameters, which reduces the possibility of misjudgment of structural parameters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Multi-Alternatives Problem of Finite Element Model Updating Based on IAFSA and Kriging Model

Kang

Zhang

Cao

et al. 2020

Sensors

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“…Finite element (FE) model updating has been given a growing interest of researchers in recent decades to develop reliable finite element models of structures. [26][27][28][29] FE model updating can be described as a mathematical technique in which the system parameters gradually adjust to obtain a structural response of the model closest to the actual. 30 A powerful strategy for solving the problem of FE model updating in damage identification can be the minimization of the residual discrepancies between the vibrational characteristics of the damaged structure and the FE model.…”

Section: Introductionmentioning

confidence: 99%

Damage quantification in foam core sandwich composites via finite element model updating and artificial neural networks

Mardanshahi

Izadi

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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The main idea of this paper is to propose a nondestructive evaluation (NDE) system for two types of damages, core cracking and skin/core debonding, in fiberglass/foam core sandwich structures based on the inverse eigensensitivity-based finite element model updating using the modal test results, and the artificial neural networks. First, the modal testing was conducted on the fabricated fiberglass/foam core sandwich specimens, in the intact and damaged states, and the natural frequencies were extracted. Finite element modeling and inverse eigensensitivity-based model updating of the intact and damaged sandwich structures were conducted and the parameters of the models were identified. Afterward, the updated finite element models were employed to generate a large dataset of the first five harmonic frequencies of the damaged sandwich structures with different damage sizes and locations. This dataset was adopted to train the machine learning models for detection, localization, and size estimation of the core cracking and skin/core debonding damages. A multilayer perceptron neural network classification model was used for detection of types of damages and also a multilayer perceptron neural network regression model was fitted to the dataset for automatically estimation of the locations and dimensions of damages. This intelligent system of damage quantification was also used to make predictions on real damaged specimens not seen by the system. The results indicated that the extracted natural frequencies from the accurate finite element model, in coordination with the experimental data, and using the artificial neural networks can provide an effective system for nondestructive evaluation of foam core sandwich structures.

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“…Finite element (FE) model updating has been the subject of increasing interest in the last decade [ 9 – 12 ]. FE model updating can be defined as a mathematical methodology whereby a FE model is updated by gradually adjusting the model’s parameters and assumptions in such a way that the responses of the FE model progressively approach those of the counterpart real structure under investigation [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection using finite element model updating with evolutionary algorithms: a survey

Alkayem

Cao

Zhang³

et al. 2017

Neural Comput & Applic

183

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Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Various studies have addressed direct, sensitivity-based, probabilistic, statistical, and iterative methods for updating FE models for structural damage identification. In contrast, evolutionary algorithms (EAs) are a type of modern method for FE model updating. Structural damage identification using FE model updating by evolutionary algorithms is an active research focus in progress but lacking a comprehensive survey. In this situation, this study aims to present a review of critical aspects of structural damage identification using evolutionary algorithm-based FE model updating. First, a theoretical background including the structural damage detection problem and the various types of FE model updating approaches is illustrated. Second, the various residuals between dynamic characteristics from FE model and the corresponding physical model, used for constructing the objective function for tracking damage, are summarized. Third, concerns regarding the selection of parameters for FE model updating are investigated. Fourth, the use of evolutionary algorithms to update FE models for damage detection is examined. Fifth, a case study comparing the applications of two single-objective EAs and one multi-objective EA for FE model updating-based damage detection is presented. Finally, possible research directions for utilizing evolutionary algorithm-based FE model updating to solve damage detection problems are recommended. This study should help researchers find crucial points for further exploring theories, methods, and technologies of evolutionary algorithm-based FE model updating for structural damage detection.

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Review of the Application of Finite Element Model Updating to Civil Structures

Cited by 11 publications

References 34 publications

Research on Multi-Alternatives Problem of Finite Element Model Updating Based on IAFSA and Kriging Model

Research on Multi-Alternatives Problem of Finite Element Model Updating Based on IAFSA and Kriging Model

Damage quantification in foam core sandwich composites via finite element model updating and artificial neural networks

Structural damage detection using finite element model updating with evolutionary algorithms: a survey

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