Results 221 to 230 of about 40,588 (255)
Some of the next articles are maybe not open access.
Soil Dynamics and Earthquake Engineering, 2002
Abstract It is recognized that soil improvement techniques are not economically feasible for mitigation of liquefaction-induced lifeline damages because of the large areas served. Instead, it is more practical to execute an emergency action immediately after an earthquake in order to prevent or minimize possible lifeline failures caused by the soil ...
M.V. Kostadinov, I. Towhata
exaly +2 more sources
Abstract It is recognized that soil improvement techniques are not economically feasible for mitigation of liquefaction-induced lifeline damages because of the large areas served. Instead, it is more practical to execute an emergency action immediately after an earthquake in order to prevent or minimize possible lifeline failures caused by the soil ...
M.V. Kostadinov, I. Towhata
exaly +2 more sources
Artificial ground motion compatible with specified peak velocity and target spectrum
Acta Seismologica Sinica, 2006In this paper, a method, which synthesizes the artificial ground motion compatible with the specified peak velocity as well as the target acceleration response spectrum, was proposed. In this method, firstly, an initial acceleration time history a g (0) (t), which satisfies the ...
Feng-xin Zhao, Yu-shan Zhang
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Hybrid Deep-Learning Network for Rapid On-Site Peak Ground Velocity Prediction
IEEE Transactions on Geoscience and Remote Sensing, 2022Shanyou Li, Jindong Song, Jingbao Zhu
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Conditional Ground-Motion Model for Peak Ground Velocity for Active Crustal Regions
PEER Reports, 2020Conditional models for the horizontal and vertical peak ground velocity (PGV), given the pseudospectral acceleration [PSA(T)] values, are developed for active crustal regions. The period of the PSA(T) used in the conditional model, TPGV , is magnitude dependent, which captures the effect of the magnitude dependence of the earthquake source corner ...
Norman Abrahamson, Sarabjot Bhasin
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Relationship between Peak Ground Acceleration, Peak Ground Velocity, and Intensity in Taiwan
Bulletin of the Seismological Society of America, 2003Based on the strong-motion data set from the 1999 Chi-Chi, Taiwan, earthquake and a shaking damage statistics database, we investigated the correlations between strong ground motions and earthquake damage (fatalities and building collapses) through a regression analysis.
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On the Calculation of Peak Ground Velocity for Seismic Performance Assessment
Earthquake Spectra, 2015Ratios of pseudo‐spectral velocity (averaged over the period range of 0.5–2 seconds and at a period of 1 second) to peak ground velocity (PGV) were computed for hundreds of ground motion records selected from the PEER NGA Strong Motion Database. Two‐stage regression analyses were performed to develop models for the ratios.
Yin‐Nan Huang, Andrew S. Whittaker
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Bulletin of the Seismological Society of America, 2004
During a disastrous earthquake, the early assessment and timely reporting of the peak ground acceleration (PGA) and peak ground velocity (PGV) maps will be crucial in an effective emergency response operation. In this study, we first derive an empirical relationship between M L and M W.
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During a disastrous earthquake, the early assessment and timely reporting of the peak ground acceleration (PGA) and peak ground velocity (PGV) maps will be crucial in an effective emergency response operation. In this study, we first derive an empirical relationship between M L and M W.
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Evaluation of Peak Ground Velocity as a “Good” Intensity Measure for Near-Source Ground Motions
Journal of Engineering Mechanics, 2004This paper investigates the goodness of peak ground velocity as a dependable intensity measure for the earthquake shaking of civil structures.
Nicos Makris, Cameron J. Black
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Developing machine learning-based ground motion models to predict peak ground velocity in Turkiye
Journal of SeismologyElham Pashaei +2 more
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A Naive Bayes Classifier for Intensities Using Peak Ground Velocity and Acceleration
Bulletin of the Seismological Society of America, 2010Abstract A naive Bayes classifier is determined to predict intensities from peak ground velocity and acceleration. It is trained on the same dataset that was used in the study of Faenza and Michelini (2010). The naive Bayes classifier directly estimates a discrete probability distribution for the ordinal intensities.
N. M. Kuehn, F. Scherbaum
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