Structural Dynamic Model Updating Techniques: A State of the Art Review

Sehgal, Shankar; Kumar, Harish

doi:10.1007/s11831-015-9150-3

Cited by 141 publications

(60 citation statements)

References 85 publications

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“…In this study, the objective limit is set as 10 −4 , and the objective function can be written as Equation :

italicOBJ ((), x) = \sum_{i = 1}^{n} {[(f_{i} (x) - f_{i, \exp}) / f_{i, \exp}]}^{2},

where x is the vector formed by the selected updating parameters; f i ( x ) and f i , exp are the frequencies of the i th identified vibration mode from the FE model and the measured one, respectively; and n is the number of natural frequencies considered. The widely used modal assurance criterion (MAC) between the FE simulated modal shape and SSI‐identified one is used herein as the identification index:

italicMAC = \frac{{(||, {\{φ_{FE}\}}^{T} ({}, φ_{\exp}))}^{2}}{((), {\{φ_{FE}\}}^{T} ({}, φ_{FE})) ((), {\{φ_{\exp}\}}^{T} ({}, φ_{\exp}))},

where { ϕ FE } is the simulated modal shape from the FE model and { ϕ exp } is the SSI‐identified modal shape from the shake table test. The SSI‐identified modal shapes are compared with the FE simulated ones in sequence to find the corresponding mode numbers.…”

Section: Case Studymentioning

confidence: 99%

“…Xin et al updated the nonlinear responses of a high‐voltage transmission tower structure using a Bayesian‐based method, in which the instantaneous characteristics of the structure are extracted from the dynamic responses and a gradient‐based interior‐point (IP) method is used for searching the optimum. A nice review on the development of FE model updating techniques and their applications can be found in Sehgal and Kumar …”

Section: Introductionmentioning

confidence: 99%

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Cluster computing‐aided model updating for a high‐fidelity finite element model of a long‐span cable‐stayed bridge

Lin

et al. 2020

Earthq Engng Struct Dyn

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Accurate and high-fidelity finite element (FE) models are in great demand in the design, performance assessment, and life-cycle maintenance of long-span cable-stayed bridges. The structural system of a long-span cable-stayed bridge is often huge in size and complex with many components connected and various materials constituted. Therefore, the FE model of a long-span cable-stayed bridge involves a large number of elements and nodes with many uncertainties. The model updating of the FE model to best represent a real bridge is necessary but very challenging. One of the challenging issues is that the numerical computation needed for searching the global optimum of a large set of structural parameters is so extensive that the existing FE (not surrogate) model-based updating methods cannot fulfill this task. In this study, a cluster computing-aided FE model updating framework is proposed for the highperformance FE model updating of large and complex structures. In the framework, several computer software packages, including MSC.Marc, Python, and MATLAB, are interconnected for making use of their respective functions of strength. The shake table test of a scaled physical structure of the Sutong cable-stayed bridge in China is used to validate the accuracy and efficiency of the proposed framework. The simulated bridge responses based on the updated FE model are in good agreement with the measured ones from the shake table test. The successful application of the proposed framework provides a reference for the model updating of other types of large and complex structures. K E Y W O R D S cluster computing, finite element model updating, large and complex structures, long-span cable-stayed bridge, shake table test

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“…In this study, the objective limit is set as 10 −4 , and the objective function can be written as Equation :

italicOBJ ((), x) = \sum_{i = 1}^{n} {[(f_{i} (x) - f_{i, \exp}) / f_{i, \exp}]}^{2},

italicMAC = \frac{{(||, {\{φ_{FE}\}}^{T} ({}, φ_{\exp}))}^{2}}{((), {\{φ_{FE}\}}^{T} ({}, φ_{FE})) ((), {\{φ_{\exp}\}}^{T} ({}, φ_{\exp}))},

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cluster computing‐aided model updating for a high‐fidelity finite element model of a long‐span cable‐stayed bridge

Lin

et al. 2020

Earthq Engng Struct Dyn

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show abstract

“…shapes between experimental and finite element modes correctly. The method such as modal assurance criterion (MAC) is usually used to quantify the mode shapes as to indicate the level of accuracy of the modes between finite element models and experimental model [24][25][26]. Generally, MAC is used to calculate experimental modes and finite element modes in matrix form, which can be calculated from Equation.…”

Section: Sensitivity Analysismentioning

confidence: 99%

The effect of laser stitch welding residual stress on the dynamic behaviour of thin steel structure

Shah

Yunus

Rani

et al. 2019

JMES

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Laser stitch welding is one of the most reliable and efficient permanent metal joining processes in the automotive industry, particularly in the manufacturing of a car body-in-white (BIW). It is widely known that this welding process induces the generation of residual stresses that can influence the dynamic behaviour of welded structures. In order to accurately predict the dynamic behaviour of these welded structures, it is important to experimentally understand the influence of residual stress. Therefore, this study aims to addresses the finite element modelling method of thin steel welded structures with and without the influences of residual stress in order to identify its effect towards dynamic behaviour. The finite element models of thin steel welded structures are developed by employing the area contact model (ACM2) format element connector. The accuracy of the finite element models is then compared in terms of natural frequencies and mode shapes with the experimental counterparts. In the experimental part, the dynamic behaviour of the structure is obtained using an impact hammer with free-free boundary conditions and LMS SCADAS is used to process the data. Sensitivity analysis then is used to identify the most influential regions of the residual stress based on the measured data. Results show that, the MAC values of the finite element model with inclusions of residual stress were substantially increased to 0.85 compared to the finite element model without inclusions of residual stress which is 0.55. It can be noted that, the dynamic behaviour of the structure can be accurately predicted by considering the residual stress on the structure in finite element modelling.

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“…However, traditional local damage detection methods, such as the visual detection method and x-ray method, require specific prior knowledge, which is difficult to obtain. Hence, global damage detection methods based on physical quantities, such as natural frequencies [5,6], modal shapes [7], accelerations [8,9], static displacements, and strain responses [10,11], become popular.…”

Section: Introductionmentioning

confidence: 99%

A Novel Artificial Bee Colony Algorithm for Structural Damage Detection

Zhao

Yan

Yang

et al. 2020

Advances in Civil Engineering

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A novel artificial bee colony (ABC) algorithm to detect structural damage via modal and frequency analyses is proposed (named as TCABC algorithm). Compared to the standard ABC algorithm, tabu search method and chaotic search method are adopted in the proposed algorithm to enhance the exploration and exploitation ability. The tabu search method uses a memory function to avoid the solution being trapped in a local minimum, which increases the exploitation ability. Chaotic search method generates more searching points for finding the global minimum, which increases the exploration ability. Additionally, the first roulette wheel selection is replaced by the tournament selection to enhance the global searching ability of the TCABC algorithm. Several explicit test functions and an implicit damage detection function are employed to check the numerical results obtained from ABC and TCABC algorithms. Afterward, the damage detection accuracy of the TCABC algorithm is verified under different circumstances, and several recommendations are given for using the TCABC algorithm to detect structural damages under actual conditions. Finally, an experimental study is applied to examine the performance of TCABC algorithm for damage detection. The results show the following: (1) compared to traditional ABC algorithm, TCABC algorithm performs better; (2) fewer groups lead to faster convergence as demonstrated by both algorithms used in the same damage situation; (3) TCABC algorithm can infer the locations and extents of the damage when the groupings are inaccurate; (4) the accuracy of the field test data profoundly affects the precision of the damage detection results. In other words, stronger noises result in worse identification results; (5) whether or not the noises exist, the more data are measured, the more accurate the results can be achieved; (6) the TCABC algorithm can efficiently detect structural damage in the experimental study.

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Structural Dynamic Model Updating Techniques: A State of the Art Review

Cited by 141 publications

References 85 publications

Cluster computing‐aided model updating for a high‐fidelity finite element model of a long‐span cable‐stayed bridge

Cluster computing‐aided model updating for a high‐fidelity finite element model of a long‐span cable‐stayed bridge

The effect of laser stitch welding residual stress on the dynamic behaviour of thin steel structure

A Novel Artificial Bee Colony Algorithm for Structural Damage Detection

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