Modal parameter identification and seismic assessment of historical timber structures under near-fault and far-fault ground motions

Altunışık, Ahmet Can; Sunca, Fezayil; Sevim, Barış

doi:10.1016/j.istruc.2022.11.143

Cited by 6 publications

(2 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Importantly, Equation (38) highlights the similarity between A and A. In progressing from the derivations of Equations ( 17) and ( 18) based on Equations ( 14) and ( 15), Equation ( 39) can be derived from Equations ( 34), (36), and (37).…”

Section: Formulation For Modal Parameter Identificationmentioning

confidence: 99%

“…These two classes of methods were first proposed by Moonen et al [32] and Van Overschee and De Moor [33], respectively. To enhance the efficiency of these original subspace approaches, an instrumental variable [34], a generalized Schur algorithm [35], empirical mode decomposition [36,37], a covariance-driven algorithm [38], and nuclear norm optimization [39] have been incorporated into subspace methods. To expand the application fields of subspace approaches from a time-invariant system to a time-variant system, the moving window technique [40,41] and recursive update algorithms [41][42][43][44][45] have been adopted.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modal Parameter Identification of a Structure Under Earthquake via a Wavelet-Based Subspace Approach

Su,

Chen,

Huang

2024

Applied Sciences

View full text Add to dashboard Cite

This paper introduces a novel wavelet-based methodology for identifying the modal parameters of a structure in the aftermath of an earthquake. Our proposed approach seamlessly combines a subspace method with a stationary wavelet packet transform. By relocating the subspace method into the wavelet domain and introducing a weighting function, complemented by a moving window technique, the efficiency of our approach is significantly augmented. This enhancement ensures the precise identification of the time-varying modal parameters of a structure. The capacity of the stationary wavelet packet transform for rich signal decomposition and exceptional time-frequency localization is harnessed in our approach. Different subspaces within the stationary wavelet packet transform encapsulate signals with distinct frequency sub-bands, leveraging the fine filtering property to not only discern modes with pronounced modal interference, but also identify numerous modes from the responses of a limited number of measured degrees of freedom. To validate our methodology, we processed numerically simulated responses of both time-invariant and time-varying six-floor shear buildings, accounting for noise and incomplete measurements. Additionally, our approach was applied to the seismic responses of a cable-stayed bridge and the nonlinear responses of a five-story steel frame during a shaking table test. The identified modal parameters were meticulously compared with published results, underscoring the applicability and reliability of our approach for processing real measured data.

show abstract

Section: Formulation For Modal Parameter Identificationmentioning

confidence: 99%