|
Title |
Study on the Development Direction of IoT-Based Structural Health Monitoring System for Erosion Control Facilities
|
|
Authors |
부윤섭(Yoonseob Boo) ; 신상민(Sangmin Shin) |
|
DOI |
https://doi.org/10.11112/jksmi.2026.30.4.50 |
|
Keywords |
사방시설물; 구조건전성 모니터링; IoT; 틸트 센서; 엣지 AI; 이상탐지; 저전력 광역 네트워크 Erosion control facility; Structural health monitoring; IoT; Tilt sensor; Edge AI; Anomaly detection; LPWA |
|
Abstract |
Korea has 12,343 erosion control dams in landslide and debris flow vulnerable areas, with concentrated distribution in Gangwon (2,899) and Gyeongbuk (2,727) provinces accounting for approximately 46% of the total. However, only 19.6% are officially designated for intensive management, leaving most facilities outside systematic maintenance frameworks. The current system relies primarily on periodic field inspections, making it difficult to detect structural anomalies in advance, while the dispersed nature of facilities in remote mountain areas imposes substantial burdens on inspection personnel and costs. Furthermore, prior small-scale model experiments have shown that debris-flow impact loads on impermeable check dams can reach up to 4.14 kN at the lower section, providing quantitative evidence for the necessity of real-time structural behavior monitoring. To address these challenges, this study systematically reviews domestic and international research trends in IoT and edge AI-based Structural Health Monitoring (SHM) technologies and proposes a development direction for a real-time anomaly detection monitoring system tailored to erosion control facilities. The proposed three-layer architecture (Sensing?Communication?Service) integrates a multi-sensor node configuration of MEMS tilt sensors, accelerometers, and environmental sensors with Low-Power Wide-Area Network (LPWA) communications, and runs edge AI anomaly detection directly on field sensor nodes to reduce communication costs and battery consumption. A prior simulation study reported that an anomaly-score-based selective transmission strategy can extend battery life by approximately 59% while maintaining damage detection performance (Arnaiz et al., 2023). A phased adoption strategy?pilot, optimization, and advancement?complementing the current periodic inspection system is also discussed, along with key technical and institutional challenges. The findings serve as foundational data for subsequent empirical research on smart maintenance of erosion control facilities.
|