A5060143477- Seismology and Earthquake Studies
- Seismic Performance and Analysis
- Scientific Computing and Data Management
- Probabilistic and Robust Engineering Design
- Nuclear Engineering Thermal-Hydraulics
- Dam Engineering and Safety
- Machine Fault Diagnosis Techniques
- Model-Driven Software Engineering Techniques
- Fluid Dynamics and Vibration Analysis
- Artificial Intelligence in Healthcare and Education
- Model Reduction and Neural Networks
- Combustion and Detonation Processes
- Fault Detection and Control Systems
- Geological Modeling and Analysis
Keihin (Japan)
2024
Tohoku University
2023
The integration of Large Language Models (LLMs), such as ChatGPT, into the workflows geotechnical engineering has a high potential to transform how discipline approaches problem-solving and decision-making. This paper investigates practical uses LLMs in addressing challenges based on opinions from diverse group, including students, researchers, professionals academia, industry, government sectors gathered workshop dedicated this study. After introducing key concepts LLMs, we present...
The application of data science technologies in geotechnical and earthquake engineering is a hot topic. This study aimed to identify the macroscopic dynamic properties soil from previous records seismic motions observed at ground surface utilizing mode decomposition (DMD). key our ingenuity was replace layer composition with single-degree-of-freedom (SDOF) vibration model based on observation records. In validation process, first, comparison made between proposed method analytical solution...
The integration of Large Language Models (LLMs) like ChatGPT into the workflows geotechnical engineering has a high potential to transform how discipline approaches problem-solving and decision-making. This paper delves innovative application LLMs in engineering, as explored hands-on workshop held Tokyo, Japan. event brought together diverse group 20 participants, including students, researchers, professionals from academia, industry, government sectors, investigate practical uses addressing...
本研究は, 地表面と工学的基盤において観測される地震動加速度記録の同時学習によるデータ駆動型地震応答解析手法の開発を目的とした基礎研究である. 地震応答解析においては, 入力地震動の非定常性と地盤材料の非線形性への対処が重要な課題となる. そこで, Koopman 作用素理論に基づき, 実空間で観測される時系列データに対して, 時間遅延埋め込みによる高次元化と動的モード分解による次元圧縮を融合した手法を適用し, 高次元空間において線形モデルによる時間発展表現を可能とする地震応答解析モデルの構築を試みた. 本研究では, 実地盤における観測記録に対して提案手法を適用し, 高精度に地震応答を再現する線形モデルの構築を実現させ, 外挿推定への有効性を示した.
9(4(20 6)( $,5+(0 0@(.0 (7(5 4(03 (20