Foresight-Driven Smart Project Management for Sustainable Coastal Infrastructure in the UAE: An Innovative Integration of Digital Twin and SHM Technologies

As coastal cities grapple with intensifying climate threats, conventional project management frameworks—rooted in periodic inspections and reactive maintenance—are proving inadequate for ensuring infrastructure longevity. This study addresses a critical research gap by introducing a foresight-driven smart project management framework that integrates Digital Twin (DT) and Structural Health Monitoring (SHM) technologies for sustainable coastal infrastructure in the United Arab Emirates (UAE). Leveraging real-time sensor data, AI-based forecasting, and dynamic simulation, the framework was applied to a ۶۰۰-meter seawall segment on Al Hudayriat Island in Abu Dhabi. Under three climate scenarios—۲۰۲۴ baseline, ۲۰۵۰ (RCP۴.۵), and ۲۱۰۰ (RCP۸.۵)—the system achieved over ۹۲% predictive accuracy in forecasting strain, displacement, and corrosion rates using LSTM neural networks. Results showed a ۳۴% reduction in projected maintenance costs over ۱۰ years, a ۲۹% improvement in early fault detection, and system responsiveness under ۴ seconds, with ۹۹.۴% operational uptime. Stress and displacement levels were projected to increase by ۷۱% and ۱۰۶%, respectively, under end-century conditions, with failure probabilities tripling in the absence of intervention. The findings underscore the urgent need for predictive, digitally integrated asset management strategies in coastal infrastructure. The proposed framework offers a scalable, data-informed solution for climate resilience that can be replicated across other high-risk coastal zones globally.

کلیدواژه ها:

Smart Project Management ، Digital Twin ، Structural Health Monitoring (SHM) ، Climate Resilience ، Coastal Infrastructure ، United Arab Emirates (UAE) ، Predictive Maintenance ، AI Forecasting ، Sea-Level Rise ، Foresight Engineering