재킷 구조물에 대한 장기 변형률 계측 및 신경망 기반 예측모델 구성

Title
재킷 구조물에 대한 장기 변형률 계측 및 신경망 기반 예측모델 구성
Alternative Title
Long-term strain measurement on a jacket-type offshore structure and neural networks based prediction model
Author(s)
이진학; 박진순; 이광수
KIOST Author(s)
Yi, Jin-Hak(이진학)Park, Jin Soon(박진순)
Alternative Author(s)
이진학; 박진순; 이광수
Publication Year
2013-09-10
Abstract
Structural strain responses of a jacket-type offshore structure are analyzed and the prediction model is constructed based on the neural networks technique using long-term measurement data. Uldolmok tidal current power plant structure under severe tidal environments is utilized as an example structure. From the measurement data during normal operation, it is observed that strain responses are obviously fluctuated with M2 and M4 tidal constituent periods and also with relatively short period of about 11 min due to the peculiar tidal characteristics in the Uldolmok strait. The neural networks based prediction model is also constructed for the signal-based structural health monitoring system, and the predicted strain responses are well coincident with the measured data. severe tidal environments is utilized as an example structure. From the measurement data during normal operation, it is observed that strain responses are obviously fluctuated with M2 and M4 tidal constituent periods and also with relatively short period of about 11 min due to the peculiar tidal characteristics in the Uldolmok strait. The neural networks based prediction model is also constructed for the signal-based structural health monitoring system, and the predicted strain responses are well coincident with the measured data.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/26794
Bibliographic Citation
International Conference on Smart Structures and Systems, pp.1 - 8, 2013
Publisher
KAIST
Type
Conference
Language
English
Files in This Item:
There are no files associated with this item.

qrcode

Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse