Uncertainty quantification and propagation in dynamic models using ambient vibration measurements, application to a 10-story building

Behmanesh, Iman; Yousefianmoghadam, Seyedsina; Nozari, Amin; Moaveni, Babak; Stavridis, Andreas

doi:10.1016/j.ymssp.2018.01.033

Cited by 26 publications

(15 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To reduce the number of independent updating parameters, the covariance matrix

can be assumed to be diagonal, i.e., only diagonal terms need to be estimated, as has been done in past studies [ 40 , 54 , 55 , 56 ]. This assumption ignores the stiffness correlations of different structural components.…”

Section: Uncertainty Quantification and Propagation Through Hierarmentioning

confidence: 99%

See 1 more Smart Citation

Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview

Song

Behmanesh

Moaveni

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data. This paper provides an overview of hierarchical Bayesian model updating which has been recently developed for probabilistic integration of models with measured data, while accounting for different sources of uncertainties and modeling errors. The proposed hierarchical Bayesian framework allows one to explicitly account for pertinent sources of variability such as ambient temperatures and/or excitation amplitudes, as well as modeling errors, and therefore yields more realistic predictions. The paper reports observations from applications of hierarchical approach to three full-scale civil structural systems, namely (1) a footbridge, (2) a 10-story reinforced concrete (RC) building, and (3) a damaged 2-story RC building. The first application highlights the capability of accounting for temperature effects within the hierarchical framework, while the second application underlines the effects of considering bias for prediction error. Finally, the third application considers the effects of excitation amplitude on structural response. The findings underline the importance and capabilities of the hierarchical Bayesian framework for structural identification. Discussions of its advantages and performance over classical deterministic and Bayesian model updating methods are provided.

show abstract

“…To reduce the number of independent updating parameters, the covariance matrix

Section: Uncertainty Quantification and Propagation Through Hierarmentioning

confidence: 99%

“…The second application is a 10-story RC building in Utica, New York, as shown in Figure 8 a [ 55 ]. The eight-bay by four-bay building consisted of a slab-column structural system with peripheral RC walls.…”

Section: Applications To Three Full-scale Civil Structuresmentioning

confidence: 99%

Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview

Song

Behmanesh

Moaveni

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In structural dynamics, Behmanesh et al [14] have developed a hierarchical framework to model and consider the variability of modal parameters over dissimilar experiments. This framework has found extensive applications in uncertainty quantification and propagation of dynamical models based on experimental modal data when they are updated and calibrated under modeling errors [23][24][25]. Nagel and Sudret [26,27] have proposed a unified multilevel Bayesian framework for calibrating dynamical models for the special case of having noise-free vibration measurements.…”

Section: Introductionmentioning

confidence: 99%

Data-driven uncertainty quantification and propagation in structural dynamics through a hierarchical Bayesian framework

Sedehi¹,

Papadimitriou²,

Katafygiotis³

2020

Probabilistic Engineering Mechanics

View full text Add to dashboard Cite

In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of parameters. This problem is not due to any misconceptions in the Bayesian framework since it is absolutely robust with respect to the modeling assumptions and the observed data. Rather, this issue has deep roots in users' inability to develop an appropriate class of probabilistic models. This paper bridges this significant gap, introducing a novel Bayesian hierarchical setting, which breaks time-history vibrational responses into several segments so as to capture and identify the variability of inferred parameters over multiple segments. Since computation of the posterior distributions in hierarchical models is expensive and cumbersome, novel marginalization strategies, asymptotic approximations, and maximum a posteriori estimations are proposed and outlined under a computational algorithm aiming to handle both uncertainty quantification and propagation tasks. For the first time, the connection between the ensemble covariance matrix and hyper distribution parameters is characterized through approximate estimations. Experimental and numerical examples are employed to illustrate the efficacy and efficiency of the proposed method. It is observed that, when the segments correspond to various system conditions and input characteristics, the proposed method delivers robust parametric uncertainties with respect to unknown phenomena such as ambient conditions, input characteristics, and environmental factors.

show abstract

“…Most of the Bayesian model updating studies focus on numerical examples and small-scale structural models under laboratory conditions [15][16][17][18][19][20][21][22][23]. Only a few applications have been implemented to full-scale real-world structures, and much fewer applications are involved with refined FEMs [24][25][26][27][28][29][30][31][32], such as applications in a 14-story RC factory building [24], a coupled-slab system in a building [25,26], a coupled building including the main and complementary parts [27] and a sailboat-shaped 20-story office building [28].…”

Section: Introductionmentioning

confidence: 99%

Bayesian model updating of a twin-tower masonry structure through subset simulation optimization using ambient vibration data

Liu

Huang

Song

et al. 2020

J Civil Struct Health Monit

View full text Add to dashboard Cite

A Bayesian model updating method using subset simulation optimization given ambient vibration data is proposed to improve the efficiency in sampling and avoid local optimums, which is applied to a full-scale high-rise structure. In the proposed method, the Bayesian fast Fourier transform method is used to extract the most probable values (MPVs) and coefficients of variation (COVs) of modal parameters of the structure, which are considered as the weighting factors in the likelihood function. The posterior probability density function (PDF) of the updating parameters is then derived considering the prior PDF as the regularization term. The subset simulation optimization algorithm is extended to find the global optimal solution of the posterior PDF. The MPVs and COVs of the updating parameters are finally presented. The proposed method is first verified by updating the 15 story stiffness scaling factors of a shear building model. Then, the proposed method is investigated through a numerical application of a four-story frame structure, in which accurate parameter estimations are observed independent of the prior PDF. Finally, the proposed method is applied to a real-world 13-story twin-tower masonry structure which is a modern heritage building. Ambient vibrations were measured through a series of accelerometers instrumented in the structure. The updating parameters are the moduli of elasticity of selected substructures with two model classes studied: three parameters and six parameters. The model-predicted modal parameters from the updated model are in good agreement with their identified counterparts. It is also shown that the case using three parameters provides larger evidence based on the Bayesian model class selection method. The updated refined finite element model provides a basis for the long-term structural health monitoring.

show abstract

Uncertainty quantification and propagation in dynamic models using ambient vibration measurements, application to a 10-story building

Cited by 26 publications

References 53 publications

Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview

Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview

Data-driven uncertainty quantification and propagation in structural dynamics through a hierarchical Bayesian framework

Bayesian model updating of a twin-tower masonry structure through subset simulation optimization using ambient vibration data

Contact Info

Product

Resources

About