Using the latest earthquake observation and information technology, we are developing efficient earthquake damage reduction technology. Based on researches about ground motion, building response, and earthquake damage prediction, we are researching disaster prevention measures such as earthquake early warning, shake-map estimation, vibration damage estimation, and structure health monitoring using real-time earthquake observation network.
We aim to minimize damage under constraint conditions based on optimization theory, taking into consideration seismic activity, ground motion characteristics, building shaking, and social conditions that depend on the area and location. Based on researches such as seismic hazard, ground motion characteristics using regional strong motion observation network, long-term structural monitoring, and seismic response of soil-structure system, we are researching comprehensive earthquake measures including seismic microzoning, ground to building response, and indirect damage.
Ohno, S. and Abe, D. (2020) REGION OPTIMIZATION OF 3-D DEEP SUBSURFACE STRUCTURE MODEL IN SENDAI BASIN JAPAN BASED ON ADJOINT METHOD, Proc. 17WCEE, 1d-0109.
Torky, A., Ohno, S. and Kashima T. (2020) DEEP LEARNING TECHNIQUES FOR STRUCTURAL RESPONSE PREDICTION DURING STRONG GROUND MOTIONS, Proc. 17WCEE, 9c-0018.
Kuyuk, H. S. and Ohno, S. (2018) Real-Time Classification of Earthquake using Deep Learning, Procedia Computer Science, 140, pp.298–305, doi:10.1016/j.procs.2018.10.316
Ohno, S. and Tsuruta R. (2018) Ground-motion prediction by ANN using machine learning for the Tohoku region, Japan, Proc. 11NCEE, Paper No. 998.