Organization

Risk Evaluation and Disaster Mitigation Research Division
Earthquake Engineering Lab
Associate Professor
Dr. Eng.

Concurrent: Graduate School of Engineering
Research Subject(s)
Ground motion prediction based on strong motion observation, earthquake early warning, structural health monitoring, relation betwwen seismic motion characteristics and vibration damage
Key Words
Strong Motion Observation / Strong Motion Prediction / Earthquake Early warning / Structural Health Monitoring / Vibration Damage
Research Activities

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.

Selected Works

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.
 

Selected Memberships
  • Architectural Institite of Japan
  • Japan Earthquake Engineering Society
  • Japan Society for Natural Disaster Science
  • The Seismological Society of Japan
  • Seismological Society of America