Effects of variability in ambient vibration data on model updating and damage identification of a 10-story building

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

doi:10.1016/j.engstruct.2017.08.044

Cited by 41 publications

(22 citation statements)

References 43 publications

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“…In addition to numerical finite element analyses and analytical calculation, nondestructive ambient vibration examinations are widely conducted to get the dynamic features in functioning condition without any external forces. A vast amount of literature can be found regarding the finite element model updating of steel structures, 13–20 reinforced concrete structures, 21–27 base isolated structures, 28 timber structures, 29,30 and masonry structures 31–36 . However, there are very limited studies about the ambient vibration test‐based system identification and FEM updating of concrete steel composite structures 37–39 …”

Section: Introductionmentioning

confidence: 99%

“…Damages can occur in a structure's service lifetime because of several reasons such as overloading, extreme seismic loading, thermal expansion, different settlement, blast, impact loading, and dynamic vibration 40,41 . The location, type, and severity of damage are very important to structural safety assessment 25 . Several approaches are being used to detect and identify the damage status.…”

Section: Introductionmentioning

confidence: 99%

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Model updating‐based automated damage detection of concrete‐encased composite column‐beam connections

Nasery

Hüsem

Okur

et al. 2020

Struct Control Health Monit

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Summary This paper presents a comparative study about the numerical and experimental system identification and model updating‐based automated damage detection of concrete‐encased composite column‐beam connections. Four different column‐beam connection types named as CBC#A, CBC#B, CBC#C, and CBC#D were considered without any changes in geometrical configuration. Numerical and experimental dynamic characteristics were extracted by initial finite element analysis and nondestructive experimental measurements for undamaged condition. Enhanced frequency domain decomposition (EFDD) method was used for experimental modal extractions. A good agreement was observed between experimental natural frequencies but not enough correlation between damping ratios for all connection types. Maximum differences between initial finite element and undamaged experimental results were calculated as 34.68% for CBC#A, 40.78% for CBC#B, 33.15% for CBC#C, and 47.13% for CBC#D type connection details. The differences were reduced to 0.24%, 0.24%, 0.16%, and 0.15%, respectively, by automated model updating procedure using spring coefficient, modulus of elasticity and weight per unit volume. In the case of the damaged condition, lateral forces were applied to simulate the effect of an earthquake. Cracks strongly affected the natural frequencies. The mode shapes were not broken evidently after damaged conditions. The mode shapes definitely deviated a bit, but generally the same after damages. The natural frequencies have decreased non‐monotonically due to the decrease in the rigidity of the column and beam at the cracked section. Maximum differences were calculated as 14.96% for CBC#A, 41.08% for CBC#B, 31.35% for CBC#C, and 45.93% for CBC#D type connection details. After automated model updating the above‐mentioned differences were reduced to 0.35%, 0.78%, 0.73%, and 0.85% respectively. Contour diagrams of changes of updating parameters were plotted for damage identification. The regions with high difference rates indicated the location of the damage.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model updating‐based automated damage detection of concrete‐encased composite column‐beam connections

Nasery

Hüsem

Okur

et al. 2020

Struct Control Health Monit

View full text Add to dashboard Cite

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“…Structural vibration data has popularly been used in the field for system identification (Juang and Pappa, 1985;James III et al, 1993;Andersen, 1997;Huang, 2001;Pakzad et al, 2011;Dorvash and Pakzad, 2012;Pakzad, 2013, 2014;Dorvash et al, 2013;Cara et al, 2014;Matarazzo and Pakzad, 2016c;Nagarajaiah and Chen, 2016), finite element model updating (Shahidi and Pakzad, 2014a,b;Yousefianmoghadam et al, 2016;Nozari et al, 2017;Song et al, 2017), and damage-sensitive feature extraction (Sohn et al, 2001;Gul and Catbas, 2009;Kullaa, 2009;Dorvash et al, 2015;Shahidi et al, 2015), with the ultimate goal of inferring information about the current condition of the monitored structure. Regarding regression models, He and De Roeck (1997) shows their utility for describing structural vibration responses, and Shahidi et al (2015) and Yao and Pakzad (2012) provide structural damage-sensitive features created using the parameters of regression models.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Autoregressive-Exogenous and Autoregressive Models Subject to Missing Data Using Expectation Maximization

Horner

Pakzad

Gulgec

2019

Front. Built Environ.

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Missing observations may present several problems for statistical analyses on datasets if they are not accounted for. This paper concerns a model-based missing data analysis procedure to estimate the parameters of regression models fit to datasets with missing observations. Both autoregressive-exogenous (ARX) and autoregressive (AR) models are considered. These models are both used to simulate datasets, and are fit to existing structural vibration data, after which observations are removed. A missing data analysis is performed using maximum-likelihood estimation, the expectation maximization (EM) algorithm, and the Kalman filter to fill in missing observations and regression parameters, and compare them to estimates for the complete datasets. Regression parameters from these fits to structural vibration data can thereby be used as damage-sensitive features. Favorable conditions for accurate parameter estimation are found to include lower percentages of missing data, parameters of similar magnitude with one another, and selected model orders similar to those true to the dataset. Favorable conditions for dataset reconstruction are found to include random and periodic missing data patterns, lower percentages of missing data, and proper model order selection. The algorithm is particularly robust to varied noise levels.

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“…Although these approaches are straightforward and easy to implement, only using the resonance frequency data could not ensure successful model updating (Salawu 1997). Alternatively, other modal properties, such as mode shapes and modal flexibility, are included in the optimization objective function to utilize more information and thus provide better updating results (Jaishi and Ren 2006;Koh and Shankar 2003;Moaveni et al 2013;Nozari et al 2017;Sanayei et al 2001;Yousefianmoghadam et al 2018;Zhang and Johnson 2013). To this end, the modal dynamic residual approach accomplishes FE model updating by forming an optimization problem that minimizes the residuals of the generalized eigenvalue equations in structural dynamics (Farhat and Hemez 1993;Kosmatka and Ricles 1999;Zhu et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Sparse Sum-of-Squares Optimization for Model Updating Through Minimization of Modal Dynamic Residuals

Wang

2019

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

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This research investigates the application of sum-of-squares (SOS) optimization method on finite element model updating through minimization of modal dynamic residuals. The modal dynamic residual formulation usually leads to a nonconvex polynomial optimization problem, the global optimality of which cannot be guaranteed by most off-the-shelf optimization solvers. The SOS optimization method can recast a nonconvex polynomial optimization problem into a convex semidefinite programming (SDP) problem. However, the size of the SDP problem can grow very large, sometimes with hundreds of thousands of variables. To improve the computation efficiency, this study exploits the sparsity in SOS optimization to significantly reduce the size of the SDP problem. A numerical example is provided to validate the proposed method.

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Effects of variability in ambient vibration data on model updating and damage identification of a 10-story building

Cited by 41 publications

References 43 publications

Model updating‐based automated damage detection of concrete‐encased composite column‐beam connections

Model updating‐based automated damage detection of concrete‐encased composite column‐beam connections

Parameter Estimation of Autoregressive-Exogenous and Autoregressive Models Subject to Missing Data Using Expectation Maximization

Sparse Sum-of-Squares Optimization for Model Updating Through Minimization of Modal Dynamic Residuals

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