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International Journal of Advanced Engineering Application

ISSN: 3048-6807

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Physics-Informed Neural Networks (PINNs) for Real-Time Structural Health Monitoring of Aging Urban Bridges

Author(s):Arash V., Sameera T., Leo G.

Affiliation: Department of Structural Mechanics, National Institute of Infrastructure Technology, Surat, Gujarat

Page No: 11-16

Volume issue & Publishing Year: Volume 3, Issue 3, 2026/03/04

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI: https://doi.org/10.5281/zenodo.19342835

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Abstract:
The rapid aging of urban transportation infrastructure presents a significant safety risk and financial burden for municipal authorities. Traditional Structural Health Monitoring (SHM) techniques often rely on periodic manual inspections or sensor-heavy data streams that lack the context of physical structural laws. This paper introduces a "Physics-Informed Neural Network" (PINN) framework that integrates real-time IoT sensor data with fundamental structural mechanics (Euler-Bernoulli beam theory and Navier equations) to monitor bridge integrity. Unlike standard "black-box" AI models, our PINN approach ensures that predictions adhere to the laws of physics, such as mass conservation and material stiffness constraints. Using a simulated multi-span highway bridge, we demonstrate the model’s ability to detect sub-surface fatigue cracking and load-bearing anomalies with 95% accuracy, even with sparse sensor coverage. The framework allows for the creation of a "Dynamic Digital Twin" that evolves with the structure's wear, enabling a shift from reactive to proactive maintenance. Our results show that PINN-driven monitoring can extend the service life of aging bridges by up to 15 years while reducing inspection costs by 40%.

Keywords: Physics-Informed Neural Networks (PINNs), Structural Health Monitoring (SHM), Digital Twins, Predictive Maintenance, Aging Infrastructure, Finite Element Analysis (FEA), IoT Sensors, Deep Learning, Urban Engineering, Bridge Safety

Reference:

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