인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Fiber-reinforced plastic (FRP) is prone to invisible damage caused by low-velocity impact (LVI) during service. The structural health monitoring system is of great significance for damage monitoring and maintenance of composite materials. In this study, four fiber Bragg grating sensors were employed to collect the time domain strain signals of composite materials subjected to LVIs. Furthermore, a numerical simulation model was established to rapidly obtain impact signal dataset. The signal arrival time, peak time, and peak amplitude were selected as signal features, and the backpropagation neural network was successfully applied to determine the location and energy of LVIs. To address the issue of peak feature extraction in the strain signal processing, a genetic algorithm-based sliding window peak detection optimization method was proposed, which significantly improved the final prediction accuracy. The experimental results indicated that within a position range of 300 mm × 300 mm, the average positioning error can reach 5.1 mm; and in an energy range of 0.5–1 J, the average energy prediction error can reach 0.030 J. The proposed method achieved accurate identification of the LVI location and energy for FRP.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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