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

Abstract

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.

Model-Based Occupant Tracking Using Slab-Vibration Measurements

“…The footstep impact load-function is applied to a single node in simulations and thus, the uncertainty is further increased. The overall simplifications and omissions are estimated to result in a uniform modeluncertainty distribution of [−15, +25] % based on engineering judgement and heuristics (Pai et al, 2018;Proverbio et al, 2018;Reuland et al, 2019).…”

“…EDMF provides robustness for inverse problems in the presence of important uncertainties, including systematic modeling bias. EDMF is most useful when typical assumptions of traditional Bayesian model updating (independent zero-mean Gaussian distribution of uncertainties) cannot be made (Pai et al, 2018). EDMF has been successfully applied to more than fifteen full-scale systems (Smith, 2016) including structural identification , leak detection in water-supply pipes (Moser et al, 2015), wind simulations (Vernay et al, 2015), fatigue life evaluation (Pasquier et al, 2014(Pasquier et al, , 2016Pai et al, 2018), and post-seismic building assessment (Reuland et al, 2019).…”

Measurement-based support for post-earthquake assessment of buildings

“…have formalised the concept of model falsification into a data-interpretation technique, EDMF, that accommodates systematic and biased modelling uncertainties in addition to (generally white-noise) measurement uncertainties. EDMF has been shown to be more accurate than traditional applications of Bayesian model updating for prognosis tasks that involve extrapolation of linear elastic models (Pai, Nussbaumer, & Smith, 2018;Pasquier & Smith, 2015a) as well as nonlinear models (Reuland et al, 2017). Important decisions in the field of asset management (such as remaining-life calculation, repair, extension and improvement through retrofit) require extrapolation of behaviour models beyond the scope of measurements.…”

Sensor Data Interpretation in Bridge Monitoring—A Case Study

“…The falsification criteria involves computation of lower and upper threshold bounds (Goulet et al, 2013a;Pasquier and Smith, 2015). The candidate model set is created by those model instances which are not falsified (Papadopoulou et al, 2014;Pai et al, 2018).…”