Integration of Digital Twin and AI for Real-Time Prediction and Management of Concrete Deterioration in Structures

Abstract

This paper presents a simplified yet practical AI-integrated Digital Twin framework for predicting concrete deterioration in reinforced concrete structures. The proposed methodology combines Indian Standard (IS) code-based material and exposure parameters with manually fed and simulated sensor data representing strain, temperature, humidity, crack width, ultrasonic pulse velocity, rebound hammer values, and corrosion potential. A virtual structural model acts as a Digital Twin, while a machine learning-based prediction module processes the combined inputs to assess deterioration risk levels and provide maintenance-oriented recommendations. The results demonstrate that the proposed framework is capable of identifying early-stage deterioration trends, including micro-crack initiation and corrosion risk, which are often overlooked by traditional inspection approaches. The study highlights the advantages of AI-based Digital Twins in terms of early warning capability, data-driven interpretation, and long-term maintenance planning. The proposed framework offers a scalable foundation for future real-time implementations and contributes toward the adoption of intelligent monitoring systems for reinforced concrete infrastructure.