- Tomoshi Miyamura, Nihon University
- Takuzo Yamashita, National Research Institute for Earth Science and Disaster Resilience
- Daigoro Isobe, University of Tsukuba
- Makoto Ohsaki, Kyoto University
There is an increasing demand for high-fidelity analysis of structures in architectural and civil engineering. Using high-fidelity FE-models and large-scale parallel computing, we can evaluate local and global responses of structures simultaneously without resort to macro models of members and joints. Parallel computation also enables direct consideration of soil-structure interaction. Recently, data-driven approaches are playing important roles for discovering rules and new models in the seismic analysis and design. Machine learning is also applied to constructing surrogate/regression models for damage detection of a structure and material or structural parameter identification.
In this mini-symposium, we share recent developments of computational methods including high-fidelity computation and data-driven approach for advanced seismic response analysis and design. We welcome topics on computational methods such as advanced numerical algorithm, modeling and mesh generation, visualization, machine learning, data assimilation, and their application to seismic analysis and design. We are also interested in enhancement of seismic resilience, damage prediction, risk assessment, and so on. Topics on seismic behaviors of non-structural components such as furniture, doors, ceilings, walls, equipment placed in buildings, and devices such as braces and steel dampers for passive control as well as rubber bearings for base isolation are welcome.