| Budget Amount *help |
¥5,980,000 (Direct Cost: ¥4,600,000、Indirect Cost: ¥1,380,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Outline of Final Research Achievements |
In order to mitigate earthquake damage, many attempts have been made to improve the reliability of earthquake damage estimation. In this research, we developed an efficient artificial intelligence method for earthquake damage estimation by learning from large-scale numerical simulation results (supercomputed data) and physical considerations, and demonstrated its effectiveness through application examples. The reduction of the analysis cost is expected to contribute to fast and immediate earthquake damage estimation and to the plan for earthquake disaster prevention that takes into account many scenarios. We also show that artificial intelligence based on physical considerations can reduce the cost of analysis by shifting the analysis from conventional equation-based modeling to operations that are more suitable for recent computer architectures. The results show the potential of combining equation-based modeling and data-science methods.
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