This study presents a low-cost edge-IoT-based structural health monitoring (SHM) architecture for crack monitoring in operational underground tunnels. The system integrates crack-displacement sensing, temperature measurement, ESP32-based edge processing, LoRa/MQTT communication, AES/ECDSA-based data protection, and cloud-based data management. The architecture was validated through a 30-day field campaign at two representative cracks in the Hai Van Tunnel, with measurements acquired at 60 s sampling intervals. The results show that edge-based wavelet–Kalman processing improved measurement stability and reduced high-frequency noise, while the implemented security mechanism introduced only minor latency relative to the monitoring cycle. The two monitored cracks exhibited small micrometer-scale fluctuations associated with temperature variation and showed no cumulative widening trend during the observation period. This study demonstrates the feasibility of campaign-based tunnel crack monitoring using a trustworthy edge-sensing architecture, while longer deployments with more sensing nodes are needed to fully evaluate scalability and operational durability.

Tạp chí Khoa học Giao thông vận tải - Trường Đại học Giao thông vận tải;Transport and Communications Science Journal under University of Transport and Communications was established in 2002 and launched online version in 2019.

The condition of concrete structures’ surfaces, most importantly the condition of cracks on the surface and their development over time, is an important and common criterion used to diagnose health conditions and determine their service life. Rapid collection, identification, and monitoring of concrete structures’ surfaces to assess the condition of bridge structures requires a fast-acting system that can meet the actual speed of the inspection process. This article introduces a YOLOv8 quantization model for rapid crack detection in concrete structures, leveraging GPU acceleration suitable for real-time video analysis during bridge inspections. The method incorporates histogram equalization and image enhancement to mitigate lighting issues and improve crack visibility. INT8 quantization reduces model size and accelerates processing while maintaining accuracy through dataset calibration. Converted to TensorRT and integrated into the inference pipeline for optimized GPU and memory management, the YOLOv8 quantization model achieves at least 30 FPS using full HD video footage. Field tests with NVIDIA GPUs demonstrated a 5x reduction in processing time, a 5x FPS increase, and a 6x improvement in GPU utilization, all while maintaining similar RAM usage. The quantum YOLOv8 model is optimized for NVIDIA GPUs, achieving a balance between accuracy and processing speed, allowing workers to analyze full HD videos in real-time at a rate of at least 30 FPS during field inspections