MUMBAI, India, March 13 -- Intellectual Property India has published a patent application (202641025382 A) filed by Madhankumar C; Mr Sathish J; Ms Suguna P; Mr Parthiban L; Mr Saravanan N; Mr Arunvaratharaj T; and Ms Shakila Banu M, Pollachi, Tamil Nadu, on March 3, for 'ai-driven structural health monitoring system for real-time bridge and building safety prediction.'
Inventor(s) include Mr Sathish J; Ms Suguna P; Mr Parthiban L; Mr Saravanan N; Mr Arunvaratharaj T; and Ms Shakila Banu M.
The application for the patent was published on March 13, under issue no. 11/2026.
According to the abstract released by the Intellectual Property India: "AI-Driven Structural Health Monitoring System for Real-Time Bridge and Building Safety Prediction Abstract The increasing complexity and aging of civil infrastructure demand intelligent monitoring solutions capable of predicting structural failures before catastrophic events occur. Traditional structural health monitoring (SHM) approaches rely on periodic inspections and static threshold-based alarms, which are often inadequate for detecting progressive deterioration, hidden micro-cracks, and dynamic load-induced stress variations. This paper proposes an AI-Driven Structural Health Monitoring System (AI-SHMS) designed to provide real-time safety prediction for bridges and buildings through continuous sensing, intelligent analytics, and predictive modeling. The proposed system integrates a distributed network of IoT-enabled sensors, including accelerometers, strain gauges, vibration sensors, displacement transducers, acoustic emission sensors, and environmental sensors to capture parameters such as stress distribution, modal frequencies, tilt, crack propagation, temperature, and humidity. Data acquired from these sensors is transmitted to an edge computing layer for preprocessing, noise filtering, and feature extraction before being analyzed by a cloud-based AI engine. A hybrid deep learning framework combining Long Short-Term Memory (LSTM) networks for temporal behavior modeling and Graph Neural Networks (GNN) for structural connectivity analysis is employed to detect anomalies, localize damage, and estimate the Remaining Useful Life (RUL) of structural components. The system dynamically generates a Structural Risk Index (SRI) that categorizes infrastructure safety into multiple levels, enabling automated early warning alerts and predictive maintenance planning. Unlike conventional SHM systems, the proposed framework continuously adapts to environmental changes, traffic load patterns, seismic activity, and material fatigue progression, thereby reducing false alarms and improving prediction reliability. Simulation and experimental validation demonstrate enhanced detection accuracy, improved early damage identification, and efficient risk forecasting. The system is scalable for deployment in smart cities, transportation networks, high-rise buildings, and disaster-prone regions. By integrating artificial intelligence with real-time structural sensing, the proposed solution offers a proactive, adaptive, and data driven approach to infrastructure safety management, significantly enhancing resilience and public safety."
Disclaimer: Curated by HT Syndication.
