TY - JOUR
T1 - Influence of Sensor Density on Seismic Damage Assessment
T2 - A Case Study for Istanbul
AU - Cheng, Qingle
AU - Fei, Yifan
AU - Lu, Xinzheng
AU - Liao, Wenjie
AU - Zhang, Wenyang
AU - Chen, Peng Yu
AU - Kurtulus, Asli
AU - Ghahari, Farid
AU - Vela, Viviana
AU - Taciroglu, Ertugrul
N1 - Publisher Copyright:
© Seismological Society of America.
PY - 2022/8
Y1 - 2022/8
N2 - The strong ground motions (GMs) recorded by strong motion networks are significant to increase the accuracy of seismic damage assessment. However, the influence of sensor density on seismic damage assessment remains unclarified. Therefore, a workflow is proposed in this study to quantitatively analyze the influence of sensor density on seismic damage assessment. The scenario-based earthquake simulation method is first used to provide the time history of GM at each location as the ground truth of the analysis. Subsequently, a GM prediction method, namely the interpolation method, is adopted to predict GMs at locations without sensors using measuring data from the limited sensors. Finally, the building scale and region scale seismic damage under different sensor densities are compared to quantitatively analyze the influence of sensor density on seismic damage assessment. A detailed case study for Zeytinburnu District, Istanbul, Turkey, is performed to demonstrate the proposed methods. The findings of this study can provide an important reference for seismic damage assessment and the deployment of strong motion networks.
AB - The strong ground motions (GMs) recorded by strong motion networks are significant to increase the accuracy of seismic damage assessment. However, the influence of sensor density on seismic damage assessment remains unclarified. Therefore, a workflow is proposed in this study to quantitatively analyze the influence of sensor density on seismic damage assessment. The scenario-based earthquake simulation method is first used to provide the time history of GM at each location as the ground truth of the analysis. Subsequently, a GM prediction method, namely the interpolation method, is adopted to predict GMs at locations without sensors using measuring data from the limited sensors. Finally, the building scale and region scale seismic damage under different sensor densities are compared to quantitatively analyze the influence of sensor density on seismic damage assessment. A detailed case study for Zeytinburnu District, Istanbul, Turkey, is performed to demonstrate the proposed methods. The findings of this study can provide an important reference for seismic damage assessment and the deployment of strong motion networks.
UR - http://www.scopus.com/inward/record.url?scp=85135090223&partnerID=8YFLogxK
U2 - 10.1785/0120220005
DO - 10.1785/0120220005
M3 - 期刊論文
AN - SCOPUS:85135090223
SN - 0037-1106
VL - 112
SP - 2156
EP - 2169
JO - Bulletin of the Seismological Society of America
JF - Bulletin of the Seismological Society of America
IS - 4
ER -