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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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
Online AccessGet full text
ISSN1363-2469
1559-808X
DOI10.1080/13632469.2020.1826371

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Abstract 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.
AbstractList 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.
Author Lu, Xinzheng
Tian, Yuan
Xu, Yongjia
Huang, Yuli
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  surname: Huang
  fullname: Huang, Yuli
  organization: Tsinghua University
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Snippet After earthquakes, an accurate and efficient seismic-damage prediction is indispensable for emergency response. Existing methods face the dilemma between...
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SubjectTerms 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
Title Real-Time Seismic Damage Prediction and Comparison of Various Ground Motion Intensity Measures Based on Machine Learning
URI https://www.tandfonline.com/doi/abs/10.1080/13632469.2020.1826371
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