2022
DOI: 10.1016/j.soildyn.2022.107515
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Spectrum-matched ground motion selection method based on Siamese Convolutional Neural Networks

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Cited by 5 publications
(3 citation statements)
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“…proposed a wavelet‐packets‐based algorithm for classifying acceleration pulses; and it is demonstrated that velocity pulses with different acceleration features can render clearly different structural responses 29–33 . Recently, Zhao et al 34 . developed an approach based on the Siamese convolutional neural networks, by which ground motions with similar pulse‐like features can be classified into various groups.…”
Section: Introductionmentioning
confidence: 99%
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“…proposed a wavelet‐packets‐based algorithm for classifying acceleration pulses; and it is demonstrated that velocity pulses with different acceleration features can render clearly different structural responses 29–33 . Recently, Zhao et al 34 . developed an approach based on the Siamese convolutional neural networks, by which ground motions with similar pulse‐like features can be classified into various groups.…”
Section: Introductionmentioning
confidence: 99%
“…Both algorithms could be taken as possible options for extracting the pulse parameters. However, one limitation of applying the two methods is that the pulse‐like significance of a motion should be known a priori, meaning that the job of pulse‐classification must be done in advance with resort to, for example, the above revisited algorithms, 20–28, 34, 35 and thereby it is not clear if the above approaches would always locate the global minimum when ground motions with unknown pulse‐like features are to be processed. In addition, aligning only the peaks of the Sd×Sv${S_d} \times {S_v}$ spectra between the pulse model and the ground motion in Mimoglou et al 88 .…”
Section: Introductionmentioning
confidence: 99%
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