We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Site classification using deep‐learning‐based image recognition techniques.
- Authors
Ji, Kun; Zhu, Chuanbin; Yaghmaei‐Sabegh, Saman; Lu, Jianqi; Ren, Yefei; Wen, Ruizhi
- Abstract
Classification of local soil conditions is important for the interpretation of structural seismic damage, which also plays a vital role in site‐specific seismic hazard analyses. In this study, we propose to classify sites as an image recognition task using a deep convolutional neural network (DCNN)‐based technique. We design the input image as a combination of the topographic slope and the mean horizontal‐to‐vertical spectral ratio (HVSR) of earthquake recordings. A DCNN model with five convolutional layers is trained using 1649 sites in Japan. The recall rates for site classes C, D, and E using our DCNN classifier for Japanese sites are 82%, 70%, and 60%, respectively. When compared with existing site classification schemes relying on predefined standard HVSR curves, our proposed method achieves the highest total accuracy rate (between 73% and 75%). The generality and applicability of our trained classifier are further validated using sites in Europe with a total accuracy between 64% and 66%. The proposed data‐driven approach could be extended to other types of site amplification functions in the future.
- Subjects
JAPAN; CONVOLUTIONAL neural networks; EARTHQUAKE hazard analysis; IMAGE recognition (Computer vision); SEISMOGRAMS; EARTHQUAKE resistant design; SOIL classification
- Publication
Earthquake Engineering & Structural Dynamics, 2023, Vol 52, Issue 8, p2323
- ISSN
0098-8847
- Publication type
Article
- DOI
10.1002/eqe.3801