Structural Health Monitoring (SHM) is vital for ensuring the safety and longevity of bridges, utilizing various sensor-based techniques to detect damage, assess performance, and monitor long-term deterioration. Traditional methods, such as visual inspections, lack precision and are prone to human error, whereas more advanced techniques like vibration-based monitoring, acoustic emission, strain gauges, and GNSS offer real-time damage detection and millimeter-level precision but often require complex planning and high costs. The presented 6D sensor, developed for infrastructure monitoring, accurately measures complex displacements and rotations, offering enhanced precision through a combination of machine learning and mathematical algorithms. When paired with low-cost, high-precision GNSS systems, it provides comprehensive real-time data on both localized and large-scale structural movements, improving insights into bridge behavior under various environmental conditions and loads.This paper explores the integration of 6D sensors with GNSS technology, discussing the advantages of real-time monitoring for predictive maintenance and presenting insights from ongoing project results.
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Structural Health Monitoring (SHM) is vital for ensuring the safety and longevity of bridges, utilizing various sensor-based techniques to detect damage, assess performance, and monitor long-term deterioration. Traditional methods, such as visual inspections, lack precision and are prone to human error, whereas more advanced techniques like vibration-based monitoring, acoustic emission, strain gauges, and GNSS offer real-time damage detection and millimeter-level precision but often require comp...
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