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

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...

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Bibliographic Details
Published inJournal of earthquake engineering : JEE Vol. 26; no. 8; pp. 4259 - 4279
Main Authors Xu, Yongjia, Lu, Xinzheng, Tian, Yuan, Huang, Yuli
Format Journal Article
LanguageEnglish
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
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Summary: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 measures (IMs) is proposed here. 48 IMs are used for representing the ground-motion characteristics comprehensively, and the workload of the nonlinear time-history analysis (NLTHA) method is replaced by model training in the non-urgent stage to promote efficiency. Case studies with various buildings prove the accuracy and efficiency of the proposed method, and corresponding key IMs are identified by iterative optimization.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1363-2469
1559-808X
DOI:10.1080/13632469.2020.1826371