A simple and intuitive procedure to identify pulse-like ground motions

Panella, Dante Sebastian; Tornello, Miguel E.; Frau, Carlos D.

doi:10.1016/j.soildyn.2017.01.020

Cited by 47 publications

(8 citation statements)

References 18 publications

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“…Zhao, Xu, and Xie () applied the zero velocity point method to inspect multiple pulses. Recently, Kardoutsou, Taflampas, and Psycharis () developed a new PI, which is set equal to the cross‐correlation factor of the significant pulse and the original record; whereas Panella, Tornello, and Frau () provided an alternative PI called “development length of velocity time‐history.”…”

Section: Introductionmentioning

confidence: 99%

Automated classification of near‐fault acceleration pulses using wavelet packets

Chang

Luca

Goda

2019

Computer aided Civil Eng

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This study proposes a new algorithm for automatically classifying two types of velocity‐pulses that are integral of a distinct acceleration pulse (acc‐pulse) or a succession of high‐frequency one‐sided acceleration spikes (non‐acc‐pulse). For achieving this, wavelet packet transform is used to filter the high‐frequency content and to extract the coherent velocity‐pulse. Then, the pulse period is unequivocally derived through the peak point method. Following the determination of the pulse‐starting (ts) and pulse‐ending (te) time instants in the velocity time‐history, a local acceleration time‐history truncated by ts and te is obtained. The maximum relative energy of the pulse between two adjacent zero crossings is then employed as indicator for distinguishing the two types of velocity‐pulses. The criteria for identifying acc‐pulses and non‐acc‐pulses are calibrated using a training data set of manually classified ground motions from the Next Generation Attenuation West 2 project. Finally, significance of such a classification between velocity‐pulses of different characteristics is assessed through the comparison of elastic acceleration response spectra of the two categories of pulse‐like records.

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Section: Introductionmentioning

confidence: 99%

Automated classification of near‐fault acceleration pulses using wavelet packets

Chang

Luca

Goda

2019

Computer aided Civil Eng

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“…Besides, low‐intensity ground motions do not have the potential to induce severe damage on structures. Thus, a peak ground velocity (PGV) threshold level of above 30 cm/s is often used as a preliminary selection criterion by many researchers . This PGV limit is also adopted here, and a database containing 357 preliminary candidate ground motions is obtained.…”

Section: Proposed Methodology For Pulse Identificationmentioning

confidence: 99%

“…Thus, a peak ground velocity (PGV) threshold level of above 30 cm/s is often used as a preliminary selection criterion by many researchers. 25,29,30,32,33 This PGV limit is also adopted here, and a database containing 357 preliminary candidate ground motions is obtained. All of the 357 candidate ground motions were visually inspected to produce a binary ground-motion classification.…”

Section: Criterion For Identification Of Pulse-like Ground Motionsmentioning

confidence: 99%

“…The zero velocity point method (hereafter abbreviated as the ZVP method), which is based on the properties of trigonometric functions, was proposed to quantitatively identify pulse‐like ground motions . More recently, 2 methods based on Mavroeidis and Papageorgiou wavelets and the development length of velocity time history were proposed. In most of the above identification methods, the pulse extracted by wavelet functions or mathematical models have simple regular waveforms.…”

Section: Introductionmentioning

confidence: 99%

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An efficient algorithm for identifying pulse‐like ground motions based on significant velocity half‐cycles

Zhai

Kunnath

et al. 2017

Earthq Engng Struct Dyn

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Summary Ground motions with strong velocity pulses are of particular interest to structural earthquake engineers because they have the potential to impose extreme seismic demands on structures. Accurate classification of records is essential in several earthquake engineering fields where pulse‐like ground motions should be distinguished from nonpulse‐like records, such as probabilistic seismic hazard analysis and seismic risk assessment of structures. This study proposes an effective method to identify pulse‐like ground motions having single, multiple, or irregular pulses. To effectively characterize the intrinsic pulse‐like features, the concept of an energy‐based significant velocity half‐cycle, which is visually identifiable, is first presented. Ground motions are classified into 6 categories according to the number of significant half‐cycles in the velocity time series. The pulse energy ratio is used as an indicator for quantitative identification, and then the energy threshold values for each type of ground motions are determined. Comprehensive comparisons of the proposed approach with 4 benchmark identification methods are conducted, and the results indicate that the methodology presented in this study can more accurately and efficiently distinguish pulse‐like and nonpulse‐like ground motions. Also presented are some insights into the reasons why many pulse‐like ground motions are not detected successfully by each of the benchmark methods.

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“…us, two predictive variables including the energy ratio and the corner frequency are chosen, as they provide a quantitative assessment of long-period characteristics. e works [13,28] pertaining to the quantitative classification of pulse-like motions suggest that logistic regression [29] has the potential to enable the classification of long-period motions if we choose variables associated with its long-period characteristics. In this regard, the training dataset was built in the form of the corner frequency and the energy ratio for each candidate record, after performing manual classifications for long-period and non-long-period motions, respectively.…”

Section: Classification Of Long-periodmentioning

confidence: 99%

Identification of Far‐Field Long‐Period Ground Motions Using Phase Derivatives

Minghui

Liu

et al. 2019

Advances in Civil Engineering

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The characteristics of long-period ground motions are of significant concern to engineering communities largely due to resonance-induced responses of long-period structures to far-field long-period ground motions which are excited by the existence of distant sedimentary basins. Classifications of records enable applications of far-field long-period ground motions in seismology and engineering practices, such as attenuation models and dynamic analysis of structures. Accordingly, the study herein aims to develop an approach for identifying the far-field long-period ground motions in terms of the later-arriving long-period surface waves. Envelope delays derived from phase derivatives are employed to determine the later-arriving long-period components on the basis of phase dispersion. A quantitative calibration for long-period properties is defined in terms of the ratio of energy from later-arriving long-period components to the total energy of a ground motion. In order to increase the accuracy of candidate far-field long-period records caused by sediments, recording stations within basins or plains are collected from the K-NET and KiK-net strong-motion networks. Subsequently, the motions are manually classified into two categories in order to form a training dataset by visual examinations on their velocity waveform. The two predictive variables, including the corner frequency obtained from envelope delays and the corresponding energy ratio, are used for the establishment of the classification criterion. Furthermore, the analysis of classification results provides insight into the causes for discrepancy and verifies the effectiveness of the proposed method. Finally, comparisons of the mean normalized acceleration response spectrum with respect to the predictors, as well as the local site effects, are performed.

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A simple and intuitive procedure to identify pulse-like ground motions

Cited by 47 publications

References 18 publications

Automated classification of near‐fault acceleration pulses using wavelet packets

Automated classification of near‐fault acceleration pulses using wavelet packets

An efficient algorithm for identifying pulse‐like ground motions based on significant velocity half‐cycles

Identification of Far‐Field Long‐Period Ground Motions Using Phase Derivatives

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