MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202641111043 A) filed by Meenakshi Sundararajan Engineering College on September 16, 2026, for Iot-Enabled Smart Pavement System Integrating Predictive Analytics For Real-Time Structural Health Monitoring And Failure Prevention.
Inventors include Mr. Vishnuvardhan. S; Mrs. Nirmalamabal. U; Mrs. Saranya. P; Mr. Saravanan. S; Mr. Ravikumar. N; Dr Ponni. M; Mrs. Jothilakshmi M; Ms Peganrranjith S; Ms. Keerthi Priya N; and Ms. Subhashini I.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: IoT-Enabled Smart Pavement System Integrating Predictive Analytics for Real-Time Structural Health Monitoring and Failure Prevention The present invention relates to an Internet of Things (IoT)-enabled smart pavement system configured for continuous structural health monitoring, condition assessment, predictive deterioration analysis, and early intervention of pavement infrastructure. The system comprises a plurality of distributed sensing nodes positioned at selected locations and/or depths within a pavement structure, wherein the sensing nodes are configured to acquire real-time measurements associated with structural and environmental conditions, including strain, stress or load response, vibration, displacement, temperature, moisture, and other pavement condition parameters. Each sensing node includes a sensing interface, a processing unit, a communication module, and a power supply or energy-harvesting arrangement, and is configured to preprocess, time-stamp, and transmit acquired measurements to one or more gateway devices or a remote computing platform through a wireless communication network. The remote computing platform receives and stores the sensor measurements together with historical monitoring data and, in an embodiment, supplementary parameters including traffic loading, climatic conditions, pavement material characteristics, construction information, and maintenance history. A predictive analytics module processes the combined time- series data using statistical, machine-learning, or other predictive algorithms to identify deviations from established structural behavior, determine deterioration trends, estimate pavement condition and remaining performance, and generate an early warning when monitored parameters indicate an increased probability of distress or structural failure. The system may employ data fusion, anomaly detection, threshold evaluation, trend analysis, and predictive modeling to distinguish transient operating variations from persistent deterioration and to reduce false alerts. A decision-support module can classify pavement sections according to detected severity and predicted risk and can generate maintenance recommendations or prioritized intervention alerts for responsible road authorities or infrastructure operators. In selected embodiments, the sensing nodes may operate with low-power electronics and energy harvesting from pavement-induced mechanical, vibrational, thermal, or other available environmental energy, thereby supporting extended monitoring with reduced dependence on periodic battery replacement. The system further provides a digital representation of pavement health through a user interface or dashboard, enabling visualization of current measurements, historical trends, detected anomalies, predicted deterioration, and alert status for individual pavement sections. By integrating distributed sensing, wireless communication, data storage, and predictive analytics into a unified pavement-monitoring architecture, the invention provides a proactive mechanism for detecting developing structural abnormalities before they become visually apparent or significantly compromise pavement serviceability, thereby supporting condition-based maintenance, improved resource allocation, reduced unexpected pavement deterioration, and enhanced infrastructure service life and operational safety while supporting scalable deployment across diverse road networks.
Disclaimer: Curated by HT Syndication.
