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A platform for research: civil engineering, architecture and urbanism
Site classification using deep‐learning‐based image recognition techniques
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.
Site classification using deep‐learning‐based image recognition techniques
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.
Site classification using deep‐learning‐based image recognition techniques
Ji, Kun (author) / Zhu, Chuanbin (author) / Yaghmaei‐Sabegh, Saman (author) / Lu, Jianqi (author) / Ren, Yefei (author) / Wen, Ruizhi (author)
Earthquake Engineering & Structural Dynamics ; 52 ; 2323-2338
2023-07-01
16 pages
Article (Journal)
Electronic Resource
English
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