Improving Fatigue Evaluations of Structures Using In-Service Behavior Measurement Data

Pasquier, Romain; Goulet, James-Alexandre; Acevedo, Claire; Smith, Ian F. C.

doi:10.1061/(asce)be.1943-5592.0000619

Cited by 37 publications

(37 citation statements)

References 24 publications

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“…Figure 12 presents a full-scale bridge that is studied in [35]. EDMF has been used for the study of this structure using measurement data recorded from static load tests in order to identify candidate models that are then used to predict remaining fatigue life of critical connections.…”

Section: Results Obtained Using Bayesian Model Class Selectionmentioning

confidence: 99%

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Robust system identification and model predictions in the presence of systematic uncertainty

Pasquier

Smith

2015

Advanced Engineering Informatics

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Context: Model-based data-interpretation techniques are increasingly used to improve the knowledge of complex system behavior. Physics-based models that are identified using measurement data are generally used for extrapolation to predict system behavior under other actions. In order to obtain accurate and reliable extrapolations, model-parameter identification needs to be robust in terms of variations of systematic modeling uncertainty introduced when modeling complex systems. Approaches such as Bayesian inference are widely used for system identification. More recently, error-domain model falsification (EDMF) has been shown to be useful for situations where little information is available to define the probability density function (PDF) of modeling errors. Model falsification is a discrete population methodology that is particularly suited to knowledge intensive tasks in open worlds, where uncertainty cannot be precisely defined.Objective: This paper compares conventional uses of approaches such as Bayesian inference and EDMF in terms of parameter-identification robustness and extrapolation accuracy.Method: Using Bayesian inference, three scenarios of conventional assumptions related to inclusion of modeling errors are evaluated for several model classes of a simple beam. These scenarios are compared with results obtained using EDMF. Bayesian model class selection is used to study the benefit of posterior model averaging on the accuracy of extrapolations. Finally, ease of representation and modification of knowledge is illustrated using an example of a full-scale bridge.Results: This study shows that EDMF leads to robust identification and more accurate predictions than conventional applications of Bayesian inference in the presence of systematic uncertainty. These results are illustrated with a full-scale bridge. This example shows that the engineering knowledge necessary to perform parameter identification and remaining-fatigue-life predictions of a complex civil structure is easily represented by the EDMF methodology. Conclusion:Model classes describing complex systems should include two components: (1) unknown physical parameters that are identified using measurements; (2) R. Pasquier and I. F. C. Smith. Robust system identification and model predictions in the presence of systematic uncertainty, in Advanced Engineering Informatics, vol. 29, num. 4, p. 1096Informatics, vol. 29, num. 4, p. -1109Informatics, vol. 29, num. 4, p. , 2015Informatics, vol. 29, num. 4, p. . doi:10.1016Informatics, vol. 29, num. 4, p. /j.aei.2015 that cannot be represented only as uncertainties related to physical parameters. In order to obtain accurate predictions, both components need to be included in the model-class definition. This study indicates that Bayesian model class selection may lead to over-confidence in certain model classes, resulting in biased extrapolation.

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Section: Results Obtained Using Bayesian Model Class Selectionmentioning

confidence: 99%

“…Candidate models have also been used to predict the remaining-fatigue-life of critical details in [35], wind flow around buildings [43] and leaks in water supply networks [21,29]. Model falsification has also been applied to sensor configuration [19,33,38].…”

Section: Error-domain Model Falsificationmentioning

confidence: 99%

Robust system identification and model predictions in the presence of systematic uncertainty

Pasquier

Smith

2015

Advanced Engineering Informatics

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“…Recent applications include model identification (Goulet et al, 2013b), leak detection (Goulet et al, 2013a;Moser et al, 2015), wind simulation (Vernay et al, 2015), prediction , fatigue life evaluation (Pasquier et al, 2014, and measurement system design (Goulet and Smith, 2012a,b;Papadopoulou et al, 2016).…”

Section: Error-domain Model Falsificationmentioning

confidence: 99%

“…Most data-interpretation methodologies assume that the uncertainty associated with the structural system is defined by a zero-mean independent Gaussian distribution. However, this assumption is rarely satisfied for civil infrastructure (Pasquier et al, 2014). Lack of knowledge of uncertainty related to aspects such as geometry of structural elements and model bias means that most sources can only be estimated as bounds.…”

Section: Introductionmentioning

confidence: 99%

“…Their results showed that the model falsification methodology provided accurate prediction in presence of non-Gaussian sources of uncertainty, model bias, and other sources of systematic uncertainty. Based on this observation, Pasquier et al (2014) and utilized model falsification for reserve capacity estimation of a full-scale bridge. However, no research was found that compares several data-interpretation methodologies for reserve capacity estimation on a full-scale case study.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge

Pai

Nussbaumer

Smith

2018

Front. Built Environ.

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Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four datainterpretation methodologies, residual minimization, traditional Bayesian model updating, modified Bayesian model updating (with an L -norm-based Gaussian likelihood func-∞ tion), and error-domain model falsification (EDMF), a method that rejects models that have unlikely differences between predictions and measurements, are compared. In the modified Bayesian model updating methodology, a correction is used in the likelihood function to account for the effect of a finite number of measurements on posterior probability-density functions. The application of these data-interpretation methodologies for condition assessment and fatigue life prediction is illustrated on a highway steel-concrete composite bridge having four spans with a total length of 219 m. A detailed 3D finite-element plate and beam model of the bridge and weigh-in-motion data are used to obtain the time-stress response at a fatigue critical location along the bridge span. The time-stress response, presented as a histogram, is compared to measured strain responses either to update prior knowledge of model parameters using residual minimization and Bayesian methodologies or to obtain candidate model instances using the EDMF methodology. It is concluded that the EDMF and modified Bayesian model updating methodologies provide robust prediction of fatigue life compared with residual minimization and traditional Bayesian model updating in the presence of correlated nonGaussian uncertainty. EDMF has additional advantages due to ease of understanding and applicability for practicing engineers, thus enabling incremental asset-management decision making over long service lives. Finally, parallel implementations of EDMF using grid sampling have lower computations times than implementations using adaptive sampling.

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Comparing Three Methodologies for System Identification and Prediction

Pai

Smith

2016

14th International Probabilistic Workshop

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Improving Fatigue Evaluations of Structures Using In-Service Behavior Measurement Data

Cited by 37 publications

References 24 publications

Robust system identification and model predictions in the presence of systematic uncertainty

Robust system identification and model predictions in the presence of systematic uncertainty

Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge

Comparing Three Methodologies for System Identification and Prediction

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