Structural model updating and prediction variability using Pareto optimal models

Christodoulou, Konstantinos; Ntotsios, Evangelos; Papadimitriou, Costas; Panetsos, Panagiotis

doi:10.1016/j.cma.2008.04.010

Cited by 65 publications

(67 citation statements)

References 37 publications

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“…To remove local minima as a source of error/ variability, global optimization techniques combining evolution algorithms and gradient methods [25] should be considered in future studies. Table 5 presents the natural frequencies computed from the updated FE model at each state considered together with their counterparts identified from ambient vibration data as well as the MAC values between analytical (FE computed) and experimental mode shapes.…”

Section: Damage Identificationmentioning

confidence: 99%

Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table

He²,

et al. 2010

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A full-scale seven-story reinforced concrete building section was tested on the UCSD-NEES shake table during the period October 2005 -January 2006. The shake table tests were designed to damage the building progressively through four historical earthquake records. At various levels of damage, ambient vibration tests and low amplitude white noise base excitations with root-mean-square accelerations of 0.03g and 0.05g were applied to the building, which responded as a quasi-linear system with parameters evolving as a function of structural damage.Modal parameters (natural frequencies, damping ratios and mode shapes) of the building were identified at different damage levels based on the response of the building to ambient as well as low amplitude white noise base excitations, measured using DC coupled accelerometers. This paper focuses on damage identification of this building based on changes in identified modal parameters. A sensitivity-based finite element model updating strategy is used to detect, localize and quantify damage at each damage state considered. Three sets of damage identification results 1. Corresponding author. Tel.: +1 858-822-4545; fax: +1 858-822-2260. E-mail address: jpconte@ucsd.edu 2 are obtained using modal parameters identified based on ambient, 0.03g, and 0.05g RMS white noise test data, respectively. The damage identification results obtained in all three cases do not exactly coincide, but they are consistent with the concentration of structural damage observed at the bottom two stories of the building. The difference in the identified damage results is mainly due to the significant difference in the identified modal parameters used in the three cases. The assumption of a quasi-linear dynamic system is progressively violated with increasing level of excitation. Therefore, application of nonlinear FE model updating strategies is recommended in future studies to resolve the errors caused by structural response nonlinearity.

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Section: Damage Identificationmentioning

confidence: 99%

Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table

He²,

et al. 2010

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“…Specifically, the distance of the Pareto points along the Pareto front from the origin is an indication of how well the model predicted modal characteristics fits the corresponding measured ones. The size of the Pareto front depends on the size of the model error and the sensitivity of the modal properties to the parameter values y [12]. Figure 7(b-d) shows the corresponding Pareto optimal solutions in the three-dimensional parameter space.…”

Section: Pareto Front and Variability Of Pareto Optimal Fe Modelsmentioning

confidence: 99%

“…The multi-objective parameter estimation methodology provides a complete set of multiple Pareto optimal structural models, consistent with the data and the residuals used. The set of Pareto optimal models contains the optimal models obtained by the weighted modal residuals method for any possible values of the weights [12]. However, additional Pareto optimal solutions may exist that do not correspond to a solution of the WRs method for any value of the weights.…”

Section: Introductionmentioning

confidence: 99%

“…Theoretical and computational issues arising in multi-objective identification have been addressed and the correspondence between the multi-objective identification and the WRs identification has been established [11,12]. Emphasis was also given in addressing computational issues associated with solving the resulting multi-objective and single-objective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Variability of updated finite element models and their predictions consistent with vibration measurements

Papadimitriou¹,

Ntotsios²,

Giagopoulos³

et al. 2011

Struct. Control Health Monit.

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Abstract:A case study on a small-scale laboratory vehicle frame is used to investigate the variability of the updated finite element (FE) models that arises from model and measurement errors and demonstrate the effect of this variability on response predictions. Conventional weighted modal residuals and recently introduced multi-objective identification methods for structural model updating are used to provide the entire spectrum of Pareto optimal FE models consistent with the measured modal data. Similarities and differences between the two model updating methods are explored and the advantages of the multi-objective identification methods are emphasized. A significant variability in Pareto optimal models is observed, which is indicative of the uncertainty in the updated FE models. The dependence of the variability of the Pareto models on the information contained in the measured data and the size of model and measurement errors is explored by varying the number of measured modes, number of sensors, FE mesh discretization sizes, and number of model parameters. The effectiveness of the updated Pareto optimal models and their predictive capabilities are assessed. Frequency response functions and fatigue lifetime predictions are used as example of structural performance variables in order to demonstrate the variability in the response predictions that arises from the variability in the Pareto optimal models. A large variability in the response predictions is observed that cannot be ignored in decisions based on updated FE models. The multi-objective optimization method provides the general framework for properly accounting for model uncertainty in model-based response predictions consistent with measured data.

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“…Furthermore, the suspension-fork subassembly is consisted of two linear parts (upper and lower fork part) connected with two springs and two seals which impose strong nonlinearity in the system. Issues related to estimating unidentifiable solutions [12][13][14][15] arising in FE model updating formulations are also addressed. A systematic study is carried out to demonstrate the effect of model error, finite element model parameterization, number of measured modes and number of mode shape components on the optimal models and their variability.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Nonlinear Large Scale Systems Through Stochastic Methods and Measurement of Its Dynamic Response

Giagopoulos¹,

Αραϊλόπουλος²

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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Structural model updating and prediction variability using Pareto optimal models

Cited by 65 publications

References 37 publications

Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table

Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table

Variability of updated finite element models and their predictions consistent with vibration measurements

Parameter Estimation of Nonlinear Large Scale Systems Through Stochastic Methods and Measurement of Its Dynamic Response

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