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To overcome the unstable problem in the classical modal curvature method, the two-dimensional Fourier spectral modal curvature method has been proposed in the former work of authors.
The use of numerical differentiation procedures is identified as the main cause for the poor performance of the modal curvature method under sparse and noisy measurement.
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The lack of direct measurement method for modal curvature necessitates the use of the central difference estimation, which reduces the stability of the algorithm.
This study proposes the scale-wavenumber domain filtering method based on the combination of the continuous wavelets transform, the discrete Fourier transform-based modal curvature and the scale wavenumber domain filtering method.
In the present work, methods based on modal curvature and modal flexibility differences are employed for identifying and locating honeycomb damage in Reinforced Concrete beam models.
The proposed scheme is demonstrated to be superior to COMAC (Coordinate Modal Assurance Criterion) and the frequency response function curvature method in identifying damaged storeys.
Instead of numerical differentiation, the two-dimensional Fourier spectral method is employed to conduct the two-dimensional modal curvature estimation in this paper.
The presented method inherits the spatial filtering capability and the spectral accuracy of the Fourier spectral modal curvature, and features an efficient modal curvature algorithm in absence of the periodic extension.
Moreover, the proposed method inherits the spatial filtering capability and spectral accuracy of the Fourier spectrum-based modal curvature, which ensures the robustness of the modal curvature calculation in noisy conditions.
The existences of the wrap-around effect and the Gibbs phenomenon in the Fourier spectrum-based method, however, necessitate the use of periodic extensions, which affects the efficiency of the modal curvature algorithm.
The frequency-response-function (FRF) curvature method encompasses the first three referred steps being based on only the measured data without the need for any modal identification.
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