Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
HVSR-based Site Classification Approach Using General Regression Neural Network (GRNN): Case Study for China Strong Motion Stations
Seismic site classification, which is fundamental for site-specific seismic hazard assessment, also plays an important role in accurate interpretation of ground motion data. However, detailed borehole information is not always available in many countries, e.g., China. Therefore, this study investigated application of the generalized regression neural network (GRNN) method to seismic site classification using China strong motion stations as example case. First, stations from KiK-net in Japan were classified based on their borehole information and individually assigned to I, II, III, and IV site classes as defined in Chinese seismic code. Then, mean horizontal-to-vertical spectral ratio (HVSR) curves for each site class were calculated as reference patterns. The overall recall rates for I, II, and III sites could reach 66.60%, 67.57%, and 68.42%, respectively, regarding use of KiK-net stations. The GRNN site classification scheme was validated using borehole information of K-NET stations, with recall rates for I and II site classes reaching 68% and 60%, respectively. Finally, based on HVSR curves calculated using strong ground motion data acquired during 2007–2015 in China, the site conditions of 167 National Strong Motion Observation Network System stations were estimated using the GRNN classification scheme. The results were partially validated using borehole information of 73 stations. The similarity between the mean HVSR curves and reference pattern curves indicated that the GRNN seismic site classification scheme is robust and could produce plausible results succinctly.
HVSR-based Site Classification Approach Using General Regression Neural Network (GRNN): Case Study for China Strong Motion Stations
Seismic site classification, which is fundamental for site-specific seismic hazard assessment, also plays an important role in accurate interpretation of ground motion data. However, detailed borehole information is not always available in many countries, e.g., China. Therefore, this study investigated application of the generalized regression neural network (GRNN) method to seismic site classification using China strong motion stations as example case. First, stations from KiK-net in Japan were classified based on their borehole information and individually assigned to I, II, III, and IV site classes as defined in Chinese seismic code. Then, mean horizontal-to-vertical spectral ratio (HVSR) curves for each site class were calculated as reference patterns. The overall recall rates for I, II, and III sites could reach 66.60%, 67.57%, and 68.42%, respectively, regarding use of KiK-net stations. The GRNN site classification scheme was validated using borehole information of K-NET stations, with recall rates for I and II site classes reaching 68% and 60%, respectively. Finally, based on HVSR curves calculated using strong ground motion data acquired during 2007–2015 in China, the site conditions of 167 National Strong Motion Observation Network System stations were estimated using the GRNN classification scheme. The results were partially validated using borehole information of 73 stations. The similarity between the mean HVSR curves and reference pattern curves indicated that the GRNN seismic site classification scheme is robust and could produce plausible results succinctly.
HVSR-based Site Classification Approach Using General Regression Neural Network (GRNN): Case Study for China Strong Motion Stations
Ji, Kun (Autor:in) / Ren, Yefei (Autor:in) / Wen, Ruizhi (Autor:in) / Zhu, ChuanBin (Autor:in) / Liu, Ye (Autor:in) / Zhou, Baofeng (Autor:in)
Journal of Earthquake Engineering ; 26 ; 8423-8445
10.12.2022
23 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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| British Library Online Contents | 2019
| British Library Online Contents | 2019