V S30, slope, H 800 and f 0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response
Abstract The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m (V S30), the topographi...
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doaj-e957617b5cf34a9080929d1c7507d67d2020-11-25T00:37:54ZengSpringerOpenEarth, Planets and Space1880-59812017-09-0169112110.1186/s40623-017-0718-zV S30, slope, H 800 and f 0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site responseBoumédiène Derras0Pierre-Yves Bard1Fabrice Cotton2Risk Assessment and Management Laboratory (RISAM), Abou Bekr Belkaïd UniversityInstitut des Sciences de la Terre (ISTerre), IFSTTAR, CNRS, IRD, Bâtiment OSUG C, Grenoble-Alpes UniversitySection 2.6 Hazard and Stress Field, GFZ German Research Center for GeoscienceAbstract The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m (V S30), the topographical slope (slope), the fundamental resonance frequency (f 0) and the depth beyond which V s exceeds 800 m/s (H 800). We considered first the performance of each SCP taken alone and then the combined performance of the 6 SCP pairs [V S30–f 0], [V S30–H 800], [f 0–slope], [H 800–slope], [V S30–slope] and [f 0–H 800]. This analysis is performed using a neural network approach including a random effect applied on a KiK-net subset for derivation of ground-motion prediction equations setting the relationship between various ground-motion parameters such as peak ground acceleration, peak ground velocity and pseudo-spectral acceleration PSA (T), and M w, R JB, focal depth and SCPs. While the choice of SCP is found to have almost no impact on the median ground-motion prediction, it does impact the level of aleatory uncertainty. V S30 is found to perform the best of single proxies at short periods (T < 0.6 s), while f 0 and H 800 perform better at longer periods; considering SCP pairs leads to significant improvements, with particular emphasis on [V S30–H 800] and [f 0–slope] pairs. The results also indicate significant nonlinearity on the site terms for soft sites and that the most relevant loading parameter for characterising nonlinear site response is the “stiff” spectral ordinate at the considered period. Graphical Abstract .http://link.springer.com/article/10.1186/s40623-017-0718-zAleatory variabilitySite-condition proxiesKiK-netNeural networksGMPENonlinear site response |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Boumédiène Derras Pierre-Yves Bard Fabrice Cotton |
spellingShingle |
Boumédiène Derras Pierre-Yves Bard Fabrice Cotton V S30, slope, H 800 and f 0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response Earth, Planets and Space Aleatory variability Site-condition proxies KiK-net Neural networks GMPE Nonlinear site response |
author_facet |
Boumédiène Derras Pierre-Yves Bard Fabrice Cotton |
author_sort |
Boumédiène Derras |
title |
V S30, slope, H 800 and f 0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response |
title_short |
V S30, slope, H 800 and f 0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response |
title_full |
V S30, slope, H 800 and f 0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response |
title_fullStr |
V S30, slope, H 800 and f 0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response |
title_full_unstemmed |
V S30, slope, H 800 and f 0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response |
title_sort |
v s30, slope, h 800 and f 0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response |
publisher |
SpringerOpen |
series |
Earth, Planets and Space |
issn |
1880-5981 |
publishDate |
2017-09-01 |
description |
Abstract The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m (V S30), the topographical slope (slope), the fundamental resonance frequency (f 0) and the depth beyond which V s exceeds 800 m/s (H 800). We considered first the performance of each SCP taken alone and then the combined performance of the 6 SCP pairs [V S30–f 0], [V S30–H 800], [f 0–slope], [H 800–slope], [V S30–slope] and [f 0–H 800]. This analysis is performed using a neural network approach including a random effect applied on a KiK-net subset for derivation of ground-motion prediction equations setting the relationship between various ground-motion parameters such as peak ground acceleration, peak ground velocity and pseudo-spectral acceleration PSA (T), and M w, R JB, focal depth and SCPs. While the choice of SCP is found to have almost no impact on the median ground-motion prediction, it does impact the level of aleatory uncertainty. V S30 is found to perform the best of single proxies at short periods (T < 0.6 s), while f 0 and H 800 perform better at longer periods; considering SCP pairs leads to significant improvements, with particular emphasis on [V S30–H 800] and [f 0–slope] pairs. The results also indicate significant nonlinearity on the site terms for soft sites and that the most relevant loading parameter for characterising nonlinear site response is the “stiff” spectral ordinate at the considered period. Graphical Abstract . |
topic |
Aleatory variability Site-condition proxies KiK-net Neural networks GMPE Nonlinear site response |
url |
http://link.springer.com/article/10.1186/s40623-017-0718-z |
work_keys_str_mv |
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