A probabilistic approach to structural model updating

Katafygiotis, Lambros S.; Papadimitriou, Costas; Lam, Heung-Fai

doi:10.1016/s0267-7261(98)00008-6

Cited by 126 publications

(84 citation statements)

References 9 publications

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“…The figure also shows that the PDF value drops significantly when one moves away form the optimal model 1 a . This is the typical characteristic of an identifiable case ( [12], [14], and [15] c in Case C in Table 4 are very close to the true values. As the COV value of the first crack location is smaller than that of the second crack location, the result shows that the second crack location is relatively more uncertain when compared to the first.…”

Section: Identified Crack Characteristics (Stage Two)supporting

confidence: 70%

Experimental characterization of multiple cracks in a cantilever beam utilizing transient vibration data following a probabilistic approach

Lam¹,

Ng²,

Veidt³

2007

Journal of Sound and Vibration

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NOTICE: this is the author's version of a work that was accepted for publication in Journal of Sound and Vibration. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Sound and Vibration, 2007; 305(1-2) Elsevier's AAM Policy: Authors retain the right to use the accepted author manuscript for personal use, internal institutional use and for permitted scholarly posting provided that these are not for purposes of commercial use or systematic distribution. Permitted scholarly postingVoluntary posting by an author on open websites operated by the author or the author's institution for scholarly purposes, as determined by the author, or (in connection with preprints) on preprint servers. 3

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Section: Identified Crack Characteristics (Stage Two)supporting

confidence: 70%

Experimental characterization of multiple cracks in a cantilever beam utilizing transient vibration data following a probabilistic approach

Lam¹,

Ng²,

Veidt³

2007

Journal of Sound and Vibration

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“…Depending on the starting values of the parameter set y, the gradient-based algorithm for optimizing J 2 ðyÞ converges to one of the infinite number of optimal models in this lower dimensional sub-manifold. Such unidentifiable manifolds have been reported in previous studies [12,[16][17][18]] using simplified models of structures that involve a small number of DOFs and simulated data. This case study demonstrates that unidentifiability issues arise in updating FE models with a large number of DOFs, build for realistic structures and using real measurements.…”

Section: Unidentifiability Issuessupporting

confidence: 66%

“…This case study demonstrates that unidentifiability issues arise in updating FE models with a large number of DOFs, build for realistic structures and using real measurements. As it is noted in Figure 8(a), the unidentifiable solutions corresponding to the flat horizontal portion (points [12][13][14][15][16][17][18][19][20] in the objective space and the associated manifold in Figure 8(b) are readily estimated by the NBI method. From the engineering point of view, the most important point from this flat portion is the most left point 12 in Figure 8(a) since all other points in the flat portion deteriorate the fit in the objective function J 1 ðyÞ without altering the fit in J 2 ðyÞ.…”

Section: Unidentifiability Issuesmentioning

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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“…A Bayesian statistical system identification methodology [16,17] is used to estimate the values of the parameter set and their associated uncertainties using the information provided from dynamic test data. For this, the uncertainties in the values of the structural model parameters are quantified by probability density functions (PDF) that are updated using the dynamic test data.…”

Section: Bayesian Methodology For Model Parameter Estimationmentioning

confidence: 99%

Optimization Algorithms for System Integration

Papadimitriou¹,

Ntotsios²

2008

Emboding Intelligence in Structures and Integrated Systems

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Abstract. This work outlines the optimization algorithms involved in integrating system analysis and measured data collected from a network of sensors. The integration is required for structural health monitoring problems arising in structural dynamics and related to (1) model parameter estimation used for finite element model updating, (2) model-based damage detection in structures and (3) optimal sensor location for parameter estimation and damage detection. These problems are formulated as single-and multi-objective optimization problems of continuous or discrete-valued variables. Gradient-based, evolutionary, hybrid and heuristic algorithms are presented that effectively address issues related to the estimation of multiple local/global solutions and computational complexity arising in single and multi-objective optimization involving continuous and discrete variables.

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A probabilistic approach to structural model updating

Cited by 126 publications

References 9 publications

Experimental characterization of multiple cracks in a cantilever beam utilizing transient vibration data following a probabilistic approach

Experimental characterization of multiple cracks in a cantilever beam utilizing transient vibration data following a probabilistic approach

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

Optimization Algorithms for System Integration

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