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
Traditional structural health monitoring methods often rely on manual inspection, which is not only inefficient but also suffers from data collection delays, subjective error sensitivity, and the inability to continuously track subtle structural changes in complex buildings over time. Therefore, this study innovatively designed a structural health monitoring system based on Internet of Things (IoT) and Micro Electro Mechanical Systems (MEMS) sensors. The system has a unique three-layer architecture, including a perception layer for collecting data through MEMS sensors, a network layer for low-power wireless data transmission, and an application layer for cloud based data analysis and visualization. Wavelet transform is also used for signal denoising to reduce external interference. Performance testing shows that the self-made system exhibits excellent performance in bridge vibration monitoring, with time-domain and frequency-domain analysis verifying the accuracy of vibration data. The relative error in frequency identification is only 1.38%. In actual testing, the frequency error of each measuring point is controlled within 3%, and the average relative error is 1.4–1.6%. The research designed building structural health monitoring models have low cost, high accuracy, and reliability, and have broad application prospects in vibration monitoring of building bridges.
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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