Abstract
Accurate measurement-data interpretation leads to increased understanding of structural behaviour and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updating, modified Bayesian model updating (with an $L_\infty$-norm-based Gaussian likelihood function) 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 219m. 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 non-Gaussian uncertainty. EDMF has additional advantages due to ease of understanding and applicability for practising 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.
Highlights
In this paper, four data-interpretation methodologies for model updating are compared to evaluate their applicability in predicting the remaining fatigue life of a full-scale bridge
Sanayei et al (2011) presented a manual model updating example where model predictions are manually compared to measurements and the model is calibrated based on engineering knowledge to minimize an objective function
Structural identification for the purpose of damage detection is limited to validation of structural response under uncertainty conditions that are similar to those used for model updating
Summary
Four data-interpretation methodologies for model updating are compared to evaluate their applicability in predicting the remaining fatigue life of a full-scale bridge. Analytical models of civil infrastructure systems possess large modeling uncertainty, including significant systematic errors and unknown correlations between measurement locations (Jiang and Mahadevan, 2008) These conditions have lead to recent studies of uncertainties and development of data-interpretation methodologies that are robust to incomplete knowledge (Goulet and Smith, 2013). The objective of this comparison is to verify the applicability of these methodologies for use in practice for the purpose of reserve capacity estimation They are compared based on their ability to provide robust identification and prediction for a full-scale structure in presence of systematic uncertainty and incomplete correlation information. These methodologies have been evaluated based on their compatibility with introduction of new information, ease of understanding for use in practice, and computation demand. The remaining fatigue life of the bridge is predicted under two traffic loading scenarios observed using a weigh-in-motion (WIM) station and one simulated future loading scenario
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