Digital Twin Integration for Predictive Maintenance of Steel Structures in Harsh Environments

Steel structures operating in harsh environments—such as coastal, arctic, and industrial zones—face accelerated deterioration due to corrosion, thermal cycling, and mechanical fatigue. Traditional inspection-based maintenance approaches often fail to provide timely interventions, resulting in increased safety risks and operational costs. This study presents a novel digital twin framework designed to enable predictive maintenance of steel structures under extreme environmental conditions. The proposed system integrates real-time sensor data, finite element modeling, and machine learning algorithms to establish a dynamic, continuously updating virtual replica of the physical asset. By leveraging data-driven analytics and structural health monitoring (SHM), the digital twin forecasts failure-prone zones and maintenance schedules with high accuracy. A simulation-based case study on a steel truss bridge located in a coastal region demonstrates the model's capability to identify early-stage degradation, optimize inspection routines, and reduce unplanned downtimes. The findings underscore the potential of digital twin technology as a transformative tool for enhancing the resilience, safety, and cost-efficiency of steel infrastructure in challenging environments.

کلیدواژه ها:

Digital Twin ، Predictive Maintenance ، Steel Structures ، Harsh Environments ، Structural Health Monitoring (SHM) ، Corrosion Detection ، Machine Learning ، Finite Element Modeling ، Infrastructure Resilience ، Real-Time Monitoring