ANUBHAV: Contextual Baseline Learning Engine
Autonomous 14-to-30-day baseline learning engine. Employs Physics-Informed Neural Networks (PINNs) to isolate normal diurnal thermal movements and operational traffic vibration from true non-linear structural degradation.
Physics-Informed AI
Neural networks constrained by the differential equations of structural elastodynamics, preventing unphysical predictions.
Thermal Decoupling
Multi-order environmental transfer functions decouple reversible daily temperature expansion ($\Delta T$) from permanent plastic rebar strain.
Zero False Alarms
Mahalanobis distance outlier detection filters routine rush-hour traffic vibrations and heavy wind gusts, eliminating nuisance municipal false alarms.
Autonomous Retraining
Continuous adaptive learning automatically recalibrates baselines post-monsoon or following verified minor seismic tremors.
Interactive Baseline Learning Sandbox
Simulate 14-to-30-day baseline learning progression and see residual variance converge