Dynamic Assessment of a Curved Cable-Stayed Bridge at the Malpensa 2000 Airport, Milan, Italy

Gentile, Carmelo; Cabrera, F. Martinez Y

doi:10.2749/101686601780324368

Cited by 10 publications

(2 citation statements)

References 1 publication

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“…Moreover, other authors use advanced functions to improve the correlation in modal parameters. For instance, (Gentile and Cabrera, 2001) adopts the normalized modal difference (NMD) to define the mode shape correlation, whereas (Doebling et al, 1997) introduces a modal strain energy index as a residual for FE model updating.…”

Section: Parameter Identification Via Objective Function Minimizationmentioning

confidence: 99%

Coupling Response Surface and Differential Evolution for Parameter Identification Problems

Vincenzi

Savoia

2015

Computer aided Civil Eng

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In the present article, a new surrogate-assisted evolutionary algorithm for dynamic identification problems with unknown parameters is presented. It is based on the combination of the response surface (RS) approach (the surrogate model) with differential evolution algorithm for global search. Differential evolution (DE) is an evolutionary algorithm where N different vectors collecting the parameters of the system are chosen randomly or by adding weighted differences between vectors obtained from two populations. In the proposed algorithm (called DE-Q), the RS is introduced in the mutation operation. The new parameter vector is defined as the one minimizing the second-order polynomial function (RS), approximating the objective function. The performances in terms of speed rate are improved by introducing the second-order approximation; nevertheless, robustness of DE algorithm for global minimum search of objective function is preserved, because multiple search points are used simultaneously. Numerical examples are presented, concerning: search of the global minimum of analytical benchmark functions; parameter identification of a damaged beam; parameter identification of mechanical properties (masses and member stiffnesses) of a truss-girder steel bridge starting from frequencies and eigenvectors obtained from an experimental field test

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Section: Parameter Identification Via Objective Function Minimizationmentioning

confidence: 99%

Coupling Response Surface and Differential Evolution for Parameter Identification Problems

Vincenzi

Savoia

2015

Computer aided Civil Eng

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“…Modal frequencies and mode shapes extracted from health monitoring records are both valuable information for model updating because objective functions can be formulated by applying the necessary transformations, like modal assurance criterion (Allemang, 2003;Pastor et al, 2012). Residuals in mode shapes were defined as the normalized modal difference by Gentile and Martinez y Cabrera (2001), while objective functions formed by modal flexibilities and strain energy residuals were proved to be effective in the damage detection case studies by Ren (2006, 2007). With novel optimization strategies, Moaveni et al (2008) and Jafarkhani and Masri (2011) explored the performance of dynamic model updating on damage identification with experiments.…”

Section: Introductionmentioning

confidence: 99%

Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference

Yuan

Yang

et al. 2022

Computer aided Civil Eng

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Model updating techniques are often applied to calibrate the numerical models of bridges using structural health monitoring data. The updated models can facilitate damage assessment and prediction of responses under extreme loading conditions. Some researchers have adopted surrogate models, for example, Kriging approach, to reduce the computations, while others have quantified uncertainties with Bayesian inference. It is desirable to further improve the efficiency and robustness of the Kriging-based model updating approach and analytically evaluate its uncertainties. An active learning structural model updating method is proposed based on the Kriging method. The expected feasibility learning function is extended for model updating using a Bayesian objective function. The uncertainties can be quantified through a derived likelihood function. The case study for verification involves a multisensory vehicle-bridge system comprising only two sensors, with one installed on a vehicle parked temporarily on the bridge and another mounted directly on the bridge. The proposed algorithm is utilized for damage detection of two beams numerically and an aluminum model beam experimentally. The proposed method can achieve satisfactory accuracy in identifying damage with much less data, compared with the general Kriging model updating technique. Both the computation and instrumentation can be reduced for structural health monitoring and model updating.

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Dynamic monitoring and evaluation of bell ringing effects for the structural assessment of a masonry bell tower

Vincenzi

Bassoli

Ponsi

et al. 2019

J Civil Struct Health Monit

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Dynamic Assessment of a Curved Cable-Stayed Bridge at the Malpensa 2000 Airport, Milan, Italy

Cited by 10 publications

References 1 publication

Coupling Response Surface and Differential Evolution for Parameter Identification Problems

Coupling Response Surface and Differential Evolution for Parameter Identification Problems

Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference

Dynamic monitoring and evaluation of bell ringing effects for the structural assessment of a masonry bell tower

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