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Load Recovery Using D-Optimal Sensor Placement and Full-Field Expansion Method

D-최적 실험 설계 기반 최적 센서 배치 및 모델 확장 기법을 이용한 하중 추정

  • Seong-Ju Byun (Graduate School, Department of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University) ;
  • Seung-Jae Lee (Department of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University) ;
  • Seung-Hwan Boo (Department of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University)
  • 변성주 (한국해양대학교 조선해양시스템공학과 대학원) ;
  • 이승재 (한국해양대학교 조선해양시스템공학과) ;
  • 부승환 (한국해양대학교 조선해양시스템공학과)
  • Received : 2023.12.16
  • Accepted : 2024.03.16
  • Published : 2024.04.20

Abstract

To detect and prevent structural damage caused by various loads on marine structures and ships, structural health monitoring procedure is essential. Estimating loads acting on the structures which are measured by sensors that are mounted properly are crucial for structural health monitoring. However, attaching an excessive number of sensors to the structure without consideration can be inefficient due to the high costs involved and the potential for inducing structural instability. In this study, we introduce a method to determine the optimal number of sensors and their optimized locations for strain measurement sensors, allowing for accurate load estimation throughout the structure using model expansion method. To estimate the loads exerted on the entire structure with minimal sensors, we construct a strain-load interpolation matrix using the strain mode shapes of the finite element (FE) model and select the optimal sensor locations by applying D-Optimal Design and the row exchange algorithm. Finally, we estimate the loads exerted on the entire structure using the model expansion method. To validate the proposed method, we compare the results obtained by applying the optimal sensor placement and model expansion method to an FE model subjected to arbitrary loads with the loads exerted on the entire FE model, demonstrating efficiency and accuracy.

Keywords

Acknowledgement

이 논문은 산업통상자원부 재원으로 한국산업기술진흥원의 지원을 받아 수행된 연구 결과임 (P0017006 스마트야드전문인력양성사업)

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