BridgeGuard AI: A Self-Calibrating Edge-AI Prototype for Low-Cost Bridge Condition Monitoring and Digital-Twin Visualization

Bridge deterioration is gradual and difficult to detect with periodic visual inspections, while professional electronic monitoring remains too expensive for small authorities and secondary structures. This paper presents a low-cost, self-calibrating structural condition monitoring prototype integrating sensing, on-device intelligence, continuous calibration, data fusion, degradation modelling, and digital-twin visualization in a single pipeline. An ESP32 edge node acquires vibration, tilt, strain, temperature, and humidity from an MPU6050 inertial sensor, an HX711-based strain gauge, and a DHT11, filters every channel with a Kalman filter, and runs a 5-3-5 TinyML autoencoder whose reconstruction error maps to a risk probability and a six-class event label. A host service resolves the drift-versus-load ambiguity with a multi-hypothesis Bayesian calibrator and a two-state Kalman decomposition, then fuses the corrected channels into an explainable Structural Health Index, timber-decay and fatigue-based remaining-useful-life estimates, and an Euler–Bernoulli digital twin streamed to a dashboard at 20 Hz. Anomaly-gated transmission reduces uploaded records by more than 99%. Bench hardware and physics-based simulator verification across five operating scenarios plus an injected-drift test demonstrate end-to-end behaviour on a prototype hardware budget of about USD 12.60.