Author(s): Lukas Reinhardt, Emilia Vogt and Henrik Weiss

Abstract: Aging concrete bridges are increasingly susceptible to structural deterioration due to corrosion, fatigue, alkali-silica reaction, creep, and environmental stressors, necessitating efficient, continuous monitoring systems to ensure long-term safety and serviceability. Traditional inspection methods—largely visual and manual—are limited by subjectivity, periodicity, and the inability to detect subsurface or early-stage damage. This study presents a comparative analysis of Internet-of-Things (IoT)-enabled Structural Health Monitoring (SHM) configurations employing Micro-Electro-Mechanical System (MEMS) accelerometers, Fiber Bragg Grating (FBG) strain sensors, and Acoustic Emission (AE) transducers, both individually and in hybrid combinations, for aging concrete bridges. Field experiments conducted on representative bridge structure spans under cyclic loading conditions assessed system performance based on accuracy, false-positive rate, signal-to-noise ratio (SNR), latency, packet delivery ratio (PDR), and energy efficiency. Statistical evaluation using one-way ANOVA revealed that the hybrid multimodal configuration achieved the highest detection accuracy (approximately 95%), lowest false-positive rate (approximately 3.2%), and superior SNR (approximately 25.5 dB) compared to single-modality systems, while maintaining comparable power consumption. The results demonstrate that combining strain, vibration, and acoustic sensing modalities enhances sensitivity to both static and dynamic deterioration processes. The hybrid IoT-SHM system also exhibited high communication reliability (PDR approximately 97%) and low latency (approximately 210 ms), validating its suitability for real-time, energy-efficient bridge structure monitoring. Overall, the study confirms that the hybrid multimodal Internet of Things (IoT) frameworks offer the most effective balance between diagnostic precision, operational sustainability, and economic feasibility for long-term structural monitoring of aging concrete bridges. The findings advocate a paradigm shift toward continuous, data-driven, and predictive infrastructure maintenance strategies, facilitating safer and more cost-efficient bridge structure asset management.

Pages: 12-18 | Views: 19 | Downloads: 11

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