MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641126188 A) filed by Dr Md. Samiul Haque; Mr Sunil Sk; Mrs Gowthami R; and Dr Shivaprasanna G S on October 22, 2026, for Modular Mathematical Framework For Structural Health Monitoring, Reliability Assessment And Predictive Maintenance Of Civil Infrastructure.
Inventors include Dr Md. Samiul Haque; Mr Sunil Sk; Mrs Gowthami R; and Dr Shivaprasanna G S.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: The present invention relates to a modular mathematical framework (100) for structural health monitoring, reliability assessment and predictive maintenance of civil infrastructure. The framework comprises a data acquisition and preprocessing module (200) configured to acquire and process structural response, inspection, historical, environmental, loading, material and geometric data; a structural health assessment module (300) configured to determine a quantitative structural health or damage index; a deterioration and reliability assessment module (400) configured to model structural degradation and determine reliability measures and probability of failure; a predictive deterioration and remaining service-life estimation module (500) configured to predict future structural condition and remaining useful service life; a predictive maintenance and decision-support module (600) configured to determine maintenance requirements, priority and suitable maintenance intervention; and an adaptive model-updating module (700) configured to incorporate newly acquired monitoring and inspection data for updating the mathematical models and revising structural health, deterioration, reliability and service-life predictions. An exemplary implementation module (800) integrates the outputs of the foregoing modules to provide structural condition, reliability, risk, remaining service life and maintenance recommendations for a civil infrastructure component. The framework enables continuous updating of mathematical models and supports risk-informed predictive maintenance decisions.
Disclaimer: Curated by HT Syndication.
