Real-Time Seismic Damage Prediction and Comparison of Various Ground Motion Intensity Measures Based on Machine Learning

• Published in: Journal of earthquake engineering : JEE Vol. 26; no. 8; pp. 4259 - 4279
• Main Authors: Xu, Yongjia, Lu, Xinzheng, Tian, Yuan, Huang, Yuli
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 11.06.2022; Taylor & Francis Ltd
• Subjects: Accuracy; Algorithms; Earthquake damage; Earthquake prediction; Earthquakes; Efficiency; Emergency preparedness; Emergency response; Ground motion; intensity measure; Iterative methods; Learning algorithms; Machine learning; Methods; Optimization; Post-earthquake emergency response; Predictions; Real time; Seismic activity; seismic damage prediction; Seismic response

After earthquakes, an accurate and efficient seismic-damage prediction is indispensable for emergency response. Existing methods face the dilemma between accuracy and efficiency. A real-time and accurate seismic-damage prediction method based on machine-learning algorithms and multiple intensity mea...