High‐resolution seismic monitoring of instrumented buildings using a model‐based state observer

Erazo, Kalil; Hernández, Eduardo

doi:10.1002/eqe.2781

Cited by 15 publications

(6 citation statements)

References 24 publications

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“…The test structure is a full‐scale RC building slice that consists of an RC web wall for lateral resistance in the direction of earthquake loads, a flange wall for transverse resistance, concrete slabs that are supported by four gravity columns, and a precast segmental posttensioned concrete column to maintain torsional strength . More detailed information about the structure and the instrumentation could be found in Panagiotou et al and Erazo and Hernandez . It is important to note that in this study, we use the recordings from the accelerometers that were placed on the closest slabs to the centroid of the web wall, which were sampled at a rate of 240 Hz.…”

Section: Validationmentioning

confidence: 99%

Structural damage detection using Bayesian inference and seismic interferometry

Uzun

Sun

Smit

et al. 2019

Struct Control Health Monit

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Funding information Shell SummaryWe present a computational methodology for structural identification and damage detection via linking the concepts of seismic interferometry and Bayesian inference. A deconvolution-based seismic interferometry approach is employed to obtain the waveforms that represent the impulse response functions with respect to a reference excitation source. Using the deconvolved waveforms, we study the following two different damage detection methods that utilize shear wave velocity variations: the arrival picking method and the stretching method.We show that variations in the shear wave velocities can be used for qualitative damage detection and that velocity reduction is more evident for more severely damaged states. Second, a hierarchical Bayesian inference framework is used to update a finite element model by minimizing the gap between the predicted and the extracted time histories of the impulse response functions.Through comparison of the model parameter distributions of the damaged structure with the updated baseline model, we demonstrate that damage localization and quantification are possible. The performance of the proposed approach is verified through two shake table test structures. Results indicate that the proposed framework is promising for monitoring structural systems, which allows for noninvasive determination of structural parameters. KEYWORDSBayesian inference, damage detection, probabilistic model updating, seismic interferometry, stretching method, structural health monitoring Struct Control Health Monit. 2019;26:e2445. wileyonlinelibrary.com/journal/stc

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Section: Validationmentioning

confidence: 99%

Structural damage detection using Bayesian inference and seismic interferometry

Uzun

Sun

Smit

et al. 2019

Struct Control Health Monit

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“…Some of these are (1) it has been formulated in such a way that it can be implemented directly as a modified structural model of system subjected to corrective forces. This allows for direct implementation of the state observer using large finite element models, (2) estimates of velocity are equal to the derivative of the estimates of displacement, and (3) it explicitly accounts for power spectral density of the excitations and measurement noise. This is important because in many structural applications, the unmeasured excitations are more compactly and accurately explained in the frequency domain using power spectral density.…”

Section: Finite Element Mbomentioning

confidence: 99%

“…Vibration-based damage assessment has been approached in two main directions: (1) data-driven approaches that operate on the basis of mathematical models extracted solely from measured vibrations [3][4][5][6][7][8] and (2) mechanistic approaches that operate by formulating a mechanics-based model of the structure and updating the quantities of interest in the model such that they are consistent with the measured vibrations during the ground motion. [9][10][11][12][13] The methodology proposed in this paper belongs to the latter.…”

Section: Introductionmentioning

confidence: 99%

Estimation of element‐by‐element demand‐to‐capacity ratios in instrumented SMRF buildings using measured seismic response

Hernández

Roohi

Rosowsky

2018

Earthq Engng Struct Dyn

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Summary This paper presents a methodology to estimate element‐by‐element demand‐to‐capacity ratios in instrumented steel moment‐resisting frames subject to earthquakes. The methodology combines a finite element model and acceleration measurements at various points throughout the building to estimate time history of displacements and internal force demands in all members. The estimated demands and their uncertainty are compared with code‐based capacity from which probabilistic bounds of demand‐to‐capacity ratios are obtained. The proposed methodology is verified using a simulated six‐story building and validated using acceleration data from California Strong Motion Instrumentation Programstation 24370 during the Northridge and Sierra Madre earthquakes.

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“…The estimation accuracy of the stochastic filters has been assessed in validation studies where it has been shown that under limited instrumentation and increased nonlinearity, the PF outperforms the UKF and EnKF. ()…”

Section: Experimental Validationmentioning

confidence: 99%

Bayesian estimation of acoustic emissions source in plate structures using particle-based stochastic filtering

Sen

Erazo

Nagarajaiah

2017

Struct Control Health Monit

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Summary The application of particle‐based stochastic filters to acoustic emission source localization in plate structures is presented. The approach employs time‐of‐flight measurements of guided waves using triangulation to estimate the acoustic emission source coordinates in a probabilistic framework using Bayesian inference, incorporating uncertainties related to material properties, measurement noise, and geometry of the system of interest. The estimate of the source location is given by a probability density function conditional on the guided wave measurements, found using particle‐based stochastic simulation algorithms; in this setting, a set of particles is used to explore the space of possible source locations and efficiently estimate the posterior. The use of 2 filters is explored: the ensemble Kalman filter and the particle filter. The former filter assumes that the posterior distribution can be approximated by a Gaussian distribution, although the latter provides a nonparametric estimate of the posterior in the form of a weighted set of samples, overcoming the challenges related to the evaluation of high‐dimensional integrals in an efficient way. Results of an experimental validation study conducted in a laboratory environment demonstrate the accuracy and efficiency of the particle filter‐based approach. In particular, it is shown that the proposed particle filter‐based approach has the capability to locate the emission source under minimal instrumentation, providing confidence intervals as a quantitative measure of the uncertainty in the estimates.

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High‐resolution seismic monitoring of instrumented buildings using a model‐based state observer

Cited by 15 publications

References 24 publications

Structural damage detection using Bayesian inference and seismic interferometry

Structural damage detection using Bayesian inference and seismic interferometry

Estimation of element‐by‐element demand‐to‐capacity ratios in instrumented SMRF buildings using measured seismic response

Bayesian estimation of acoustic emissions source in plate structures using particle-based stochastic filtering

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