The MIT Green Building benchmark problem for structural health monitoring of tall buildings

Sun, Hao; Büyüköztürk, Oral

doi:10.1002/stc.2115

Cited by 22 publications

(18 citation statements)

References 38 publications

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“…This simplification essentially reduces the number of parameters so that Bayesian learning procedures become computationally affordable. Primary lateral stiffness matrix, that is, K 0 , and the diagonal lumped mass matrix, that is, M , are constructed using the procedure described in Sun and Büyüköztürk and Sun et al The procedure is based on assembling the lateral flexibility matrix by applying unit loads to each story level while tracing the story drifts. K 0 is then computed by taking the inverse of the acquired flexibility matrix.…”

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

Self Cite

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“…The fixed-base motion was established for sensor S3 by applying the following standard procedure (Sun, H. and Büyüköztürk, 2018):…”

Section: Experimental Seismic Response Of the Towermentioning

confidence: 99%

Seismic Reassessment of the Leaning Tower of Pisa: Dynamic Monitoring, Site Response, and SSI

et al. 2019

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The Tower of Pisa survived several strong earthquakes undamaged over the last 650 years, despite its leaning and limited strength and ductility. No credible explanation for its remarkable seismic performance exists to date. A reassessment of this unique case history in light of new seismological, geological, structural, and geotechnical information is reported, aiming to address this question. The following topics are discussed: (1) dynamic structural identification based on recorded earthquake data; (2) geophysical site characterization using a two-dimensional array; (3) seismic hazard and site response analysis considering horizontal and vertical motions; and (4) soil-structure interaction (SSI) analysis calibrated using lab and field data. A substantial shift in natural period, from about 0.35 s to over 1 s (a threefold increase, the largest known for a building of that height) caused by SSI, a wave parameter (1/ σ) of about 0.3, and a minor effect of vertical ground motion are identified and may explain the lack of earthquake damage on the Tower. Recommendations for future research, including the need to establish a seismic bedrock deeper than 500 m, are provided.

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“…As an effective technique, blind source separation has been widely used for system identification [8][9][10][11][12] and has the potential for SHM. Independent component analysis (ICA) is probably the most widely used method of performing blind source separation.…”

Section: Introductionmentioning

confidence: 99%

A numerical study on multi‐site damage identification: A data‐driven method via constrained independent component analysis

Zhang

Sun

2020

Struct Control Health Monit

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Summary This paper presents a solution to the multi‐site structural damage identification problem using a data‐driven method and constrained independent component analysis (cICA). While existing studies in this field presented encouraging results for single‐site damage identification, limited research effort has been devoted to identifying multi‐site damage due its complexity. Efficient features for single‐site damage identification may lose their effectiveness when multi‐site damage occurs. This paper extracts damage‐sensitive features from the ICA outcome of the structural responses under certain excitations. The information on structural damage contained in the response is compacted into the mixing matrix by enforcing identical independent components to that of intact structures. Hence, the cICA can significantly reduce the feature dimension and preserve all the valuable information of damage. A case study indicates that the mixing matrix elements, when used as damage features, can distinguish multi‐site damage cases from single‐site damage cases and locate the single‐site damage. Furthermore, the mixing matrix columns of multi‐site damage cases exhibit distinct correlation with that of the corresponding single‐site damage cases. As a result, the proposed method can progressively locate the structural damage. Moreover, the present study has the potential to identify multi‐site damage identification in data‐driven structural health monitoring without requiring multi‐site damage data as a reference. This relieves the burden from data incompleteness when using data‐based damage identification methods and pattern recognition.

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The MIT Green Building benchmark problem for structural health monitoring of tall buildings

Cited by 22 publications

References 38 publications

Structural damage detection using Bayesian inference and seismic interferometry

Structural damage detection using Bayesian inference and seismic interferometry

Seismic Reassessment of the Leaning Tower of Pisa: Dynamic Monitoring, Site Response, and SSI

A numerical study on multi‐site damage identification: A data‐driven method via constrained independent component analysis

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