Encoding Time-Series Ground Motions as Images for Convolutional Neural Networks-Based Seismic Damage Evaluation - Yuan, Xinzhe; Tanksley, Dustin; Jiao, Pu; Li, Liujun; Chen, Genda; Wunsch, Donald (Frontiers in Built Environment, 2021)
Traditional methods for seismic damage evaluation require manual extractions of intensity measures (IMs) to properly represent the record-to-record variation of ground motions. Contemporary methods such as Convolutional Neural Networks (CNNs) for time series classification and seismic damage evaluation face a challenge in training due to a huge task of ground-motion image encoding. Presently, no consensus has been reached on the understanding of the most suitable encoding technique and image size (width × height × channel) for CNN-based seismic damage evaluation. In this study, we propose and develop a new image encoding technique based on Time-series Segmentation (TS) to transform acceleration (A), velocity (V), and displacement (D) ground motion records into a three-channel AVD image of the ground motion event with a pre-defined size of width × height. The proposed TS technique is compared with two time-series image encoding techniques, namely Recurrence Plot (RP) and Wavelet Transform (WT). The CNN trained through the TS technique is also compared with the IM-based machine learning approach. The CNN-based feature extraction has comparable classification performance to the IM-based approach. WT 1000×100 results in the highest 79.5% accuracy in classification while TS 100×100 with a classification accuracy of 76.8% is most computationally efficient. Both the WT 1000×100 and TS 100×100 three-channel AVD image encoding methods are promising for future studies of CNN-based seismic damage evaluation.