IoT-Enabled Structural Health Monitoring of Civil Infrastructure: Sensor Architectures, Data Pipelines, and Deployment Challenges
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Abstract
Civil infrastructure worldwide is ageing faster than inspection regimes can track. Structural health monitoring (SHM) — the continuous, sensor-based assessment of structural condition — has long promised to replace episodic visual inspection with data-driven condition awareness, and the Internet of Things (IoT) has finally made the promise economically plausible: low-cost MEMS sensors, low-power wide-area networking, edge computing, and cloud analytics together enable instrumented bridges, buildings, dams, and towers at a fraction of the cost of first-generation wired systems. Yet the gap between pilot deployments and sustained operational practice remains wide. This paper provides a systems-level review of IoT-enabled SHM organised around three layers. First, sensor architectures: we survey the principal sensing modalities — vibration, strain, displacement, inclination, acoustic emission, environmental, and vision-based sensing — and their placement logic, power characteristics, and data profiles, consolidated in a reference table. Second, data pipelines: we trace the end-to-end path from on-node signal conditioning and edge feature extraction through network transport, time-synchronisation, cloud storage, and damage-identification analytics, highlighting where the classical SHM literature’s statistical pattern-recognition paradigm meets modern streaming architectures. Third, deployment challenges: we analyse the obstacles that dominate real projects — energy autonomy, harsh-environment reliability, synchronisation accuracy, data volume economics, environmental and operational variability confounding damage signals, model validation without damage labels, and institutional ownership of long-lived monitoring assets. We close with a deployment roadmap and research agenda spanning self-powered sensing, edge intelligence, digital-twin integration, and standardisation.
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