INTELLIGENT FRP–CONCRETE COMPOSITE SYSTEMS INTEGRATED WITH AI-DRIVEN STRUCTURAL MONITORING

Abstract

Fiber-Reinforced Polymer (FRP)–concrete composite systems have emerged as a high-performance solution for enhancing structural resilience, durability, and load-bearing capabilities in modern civil engineering. However, conventional monitoring techniques fail to detect early-stage degradation such as micro-cracks, delamination, and moisture-induced deterioration. This study proposes an intelligent FRP–concrete composite framework integrated with AI-driven structural health monitoring (SHM) using deep learning models and sensor-fusion analytics. Real-time strain, vibration, and environmental data are captured using embedded sensors and processed through hybrid CNN-LSTM models for anomaly detection and predictive maintenance. Experimental validation demonstrates improved detection accuracy and reduced false positives under dynamic loading. The results indicate that integrating AI with FRP–concrete composites significantly enhances structural safety, lifecycle prediction, and proactive decision-making.