PREDICTIVE MAINTENANCE OF INFRASTRUCTURE USING DIGITAL TWIN MODELING AND ANALYTICS

Abstract

Civil infrastructure such as bridges, highways, and buildings requires continuous monitoring and timely maintenance to ensure safety, reliability, and long service life. Traditional maintenance strategies are often reactive or schedule-based, leading to inefficient resource utilization and unexpected failures. This paper presents a digital twin–enabled predictive maintenance framework for civil infrastructure systems. The proposed approach integrates realtime sensor data, structural models, and datadriven analytics to create a virtual representation of physical assets. Machine learning algorithms are employed within the digital twin to predict deterioration trends and identify potential failures in advance. The framework enables condition-based maintenance decisions and improves asset management efficiency. Experimental evaluation using simulated infrastructure data demonstrates improved fault prediction accuracy and reduced maintenance costs compared to conventional methods. The results highlight the potential of digital twins in enhancing infrastructure resilience and sustainability.