Large-Scale Physical Model Tests for Wave Loads on Wood- Framed Residential Structures

Large-scale wave flume tests of a 1:6 scaled model of a wood-framed residential house were conducted to investigate the structural failure mechanism. The water surface elevation, wave particle velocities, structural displacement, and acceleration data were collected from the wave gages, ADVs, string pots and accelerometers. Both regular and random waves with three-different surge levels, considering storm wave and surge, were generated until the failure of the model occurred. The preliminary results show that the accelerations can be an indicator of structural response and failure.This study is partially supported by U.S. Department of Homeland Security (2015-ST-061-ND0001- 01) and Korea Ministry of Oceans and Fisheries (No. 20170265)

A Study of the Optimal Deployment of Tsunami Observation Instruments in Korea

It has been an issue among researchers that the tsunamis that occurred on the west coast of Japan in 1983 and 1993 damaged the coastal cities on the east coast of Korea. In order to predict and reduce the damage to the Korean Peninsula effectively, it is necessary to install offshore tsunami observation instruments as part of the system for the early detection of tsunamis. The purpose of this study is to recommend the optimal deployment of tsunami observation instruments in terms of the higher probability of tsunami detection with the minimum equipment and the maximum evacuation and warning time according to the current situation in Korea. In order to propose the optimal location of the tsunami observation equipment, this study will analyze the tsunami propagation phenomena on the east sea by considering the potential tsunami scenario on the west coast of Japan through numerical modeling using the COrnell Multi-grid COupled Tsunami (COMCOT) model. Based on the results of the numerical model, this study suggested the optimal deployment of Korea's offshore tsunami observation instruments on the northeast side of Ulleung Island

Wave Celerity Estimation using Unsupervised Image Registration from Video Imagery

본 논문은 대용량 비디오 영상에서 파랑 추적을 통한 파랑 이동 속도 추정을 위하여 비지도 학습 기반 영상 정합 기술을 소개한다. 연안에서 파랑은 자체의 높은 비선형성과 비선형 상호작용에 의한 높은 복잡도, 그리고 센서 기반 관측의 어려움으로 물리 방정식을 이용한 파랑 이동 속도의 정확한 추정이 어렵다. 본 연구에서 제안하는 방법은 연안 비디오 영상을 이용한 다양한 비선형 파랑 거동에 대한 학습을 통하여 파랑을 정확히 추적함으로써 파랑의 이동 속도를 계산할 수 있다. 오토인코더를 이용하여 주변광 등 외부 요인이 제거된 파랑 이동 장면 영상을 추출하고, 이로부터 시-공간 비선형 영상 정합을 통해 파랑 추적에 대한 변위 벡터를 추출하여, 단위 시간에 대한 파랑의 이동 변위를 계산한다. 추정된 파랑이동 속도의 정확도 평가를 위해 실제 관측값과 기존 영상 처리 방법으로 추정된 값을 비교 분석하였다. In this paper, we propose an image registration method based on unsupervised learning to estimate wave celerity by tracking wave movements using a large amount of video imagery. It is difficult to estimate the wave celerity accurately using physics-based modeling in the coastal region, owing to the limitations of in-situ measurement and the high nonlinearity of wave phenomena itself as well as high complexity from nonlinear interactions. In order to estimate wave celerity, the proposed method learns the nonlinear wave behavior from the video imagery. Autoencoder is applied to separate hydrodynamics scenes from environmental factors, such as daylights. The displacement vector of propagating waves is computed by non-linear spatio-temporal image registration. The wave celerity is estimated by accumulating the displacement vectors along time. In this paper, we compare the wave celerity measurement with conventional image processing methods and actual measurement using sensors for accuracy evaluation