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2025 Vol.41, Issue 4 Preview Page
Study on Deep Learning-Based Image Analysis for Structural Vibration in Dynamic Centrifuge Modeling

동적원심모형실험 시 구조물 진동 계측을 위한 딥러닝 기반 이미지 분석 기법 연구

Seung-Jae Han1
,
See-He Han2
,
Yun Wook Choo3*
,
Jung-Eun Kim4
,
Susik Yoon5
한 승재1
,
한 세희2
,
추 연욱3*
,
김 정은4
,
윤 수식5
1Member, Graduate Student, Dept. of civil and Environmental Engineering, Kongju National Univ.
2Member, Graduate Student, Dept. of civil and Environmental Engineering, Kongju National Univ.
3Member, Prof., Dept. of Civil and Environmental Engineering, Kongju National Univ.
4Associate Prof., Department of Computer Science and Engineering, Inha Univ.
5Associate Prof., Department of Computer Science and Engineering, Korea Univ.
1정회원, 공주대학교 건설환경공학과 석사과정
2정회원, 공주대학교 건설환경공학과 석사과정
3정회원, 공주대학교 건설환경공학과 교수
4비회원, 인하대학교 컴퓨터공학과 조교수
5비회원, 고려대학교 컴퓨터공학과 조교수
*Corresponding Author
31 August 2025. pp. 53-64
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Abstract

A deep learning-based image analysis approach, You Only Look Once (YOLO), was adopted to quantify the dynamic displacement of a structure in centrifuge model tests. To verify the method’s effectiveness, a vibration excitation experiment was conducted prior to its application in the centrifugal model tests. The results demonstrated that the proposed YOLO-based model exhibited high stability and accuracy, outperforming conventional tracking software. In the centrifuge model tests, the approach that combined direct accelerometer measurements with preprocessing via a deep image enhancement technique achieved the highest accuracy, with an error rate of 7.096%. Additionally, fast Fourier transform analysis revealed comparable frequency characteristics, further validating the method’s reliability.

Keywords
Dynamic Displacement
Image preprocessing
Vibration
Centrifuge test

본 연구에서는 딥러닝 기반 영상 분석 기법인 You only look once(YOLO)를 활용하여 동적원심모형실험 상의 구조물 진동 변위를 측정하였다. 해당 기법의 적용성을 검증하기 위해 진동 가진기 실험을 선행으로 수행하였다. 그 결과 YOLO 기법으로 개발된 구조물 인식모델은 기존의 상용 트래킹 소프트웨어에 비해 우수한 안정성과 정확성을 보였다. 이후 원심모형실험에 이 기법을 적용하였고, 가속도계 이미지를 직접 인식하는 기법과 전처리 방식 중 가장 높은 정확도를 나타낸 Deep Image Enhancement 기법을 결합하여 평균 오차율 7.096%을 달성하였다. 또한 고속푸리에변환을 통해 유사한 주파수 특성이 도출되어 본 기법의 신뢰성을 추가로 입증하였다.

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Information
  • Publisher :The Korean Geotechnical Society
  • Publisher(Ko) :한국지반공학회
  • Journal Title :Journal of the Korean Geotechnical Society
  • Journal Title(Ko) :한국지반공학회 논문집
  • Volume : 41
  • No :4
  • Pages :53-64
  • Received Date : 2025-06-11
  • Revised Date : 2025-07-02
  • Accepted Date : 2025-07-07
  • DOI :https://doi.org/10.7843/kgs.2025.41.4.53
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