A model-based data-interpretation framework for post-earthquake building assessment with scarce measurement data

Abstract

Recent earthquake events throughout the world have once again exposed the vulnerability of buildings with respect to earthquakes. It is unlikely and unsustainable to design and-especially in regions with low-to-moderate seismic hazard-to retrofit all buildings to remain within elastic displacement ranges during earthquakes with high return periods. Therefore, post-earthquake assessment plays a fundamental role in the resilience of cities, given the potential to reduce time between an earthquake event and the clearance for (renewed) occupancy of a building. In this paper, a framework for model-based data interpretation of measurements of earthquake-damaged structures is presented. The framework allows engineers to combine ambient-vibration measurements and visual inspection to reduce parametric uncertainty of a high-fidelity model using the error-domain modelfalsification methodology. For building types that have limited stiffness contributions from non-structural elements (i.e. shear-wall buildings) and for which non-ductile failure modes (such as out-of-plane failure) can be excluded, reduction in natural frequency and damage grades derived from visual inspection provide global measurement sources for structural identification. The application of the proposed methodology to a shear-resisting building tested on a shake table illustrates that vulnerability-curve predictions provide accurate damage estimates for subsequent earthquakes with probabilities between 50 and 100 percent for five measured scenarios. In complete absence of baseline information regarding the initial building state and the earthquake signal, parametric uncertainty is reduced by up to 76 percent. This study thus demonstrates usefulness for certain building types to enhance post-seismic vulnerability predictions.

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 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).…”

Data-Interpretation Methodologies for Practical Asset-Management

“…Pasquier and Smith [33] presented an iterative sequence-free data-interpretation framework using EDMF and highlighted the iterative nature of data interpretation. A similar framework for post-earthquake assessment using EDMF was presented by Reuland et al [34]. Zhang et al [35] developed a data-interpretation tool that employs BMU with the goal of assisting asset managers.…”

Quantifying the out-of-plane response of unreinforced masonry walls subjected to relative support motion

“…The numerical modelling of rocking masonry structures is a challenging task and many numerical modelling techniques based on e.g. finite element [21][22][23], discrete element [24][25][26][27][28], applied element [29,30] and rigid-block models [31] have been put forward in the recent years. In this study, the URM walls are modelled by means of a discrete element model, which was already validated and employed in a previous work on walls with fixed support motions [32].…”