Seismic Prediction and Structural Health Management of the Bay Bridge Using Digital Twin Technology

This study investigates the application of digital twin technology for predicting and managing earthquake impacts on steel and concrete structures, with a specific focus on the Bay Bridge in San Francisco. Using real-time structural health monitoring (SHM) data and finite element analysis (FEA), a comprehensive digital twin model of the bridge was developed to simulate its response to seismic events. The model was validated against historical data from the ۱۹۸۹ Loma Prieta earthquake (۶.۹ magnitude), showing a close alignment between simulated and recorded displacement values (۰.۴۵ meters vs. ۰.۴۲ meters). Two seismic scenarios were analyzed: the Loma Prieta earthquake and a simulated Hayward Fault event (۷.۰ magnitude). Results revealed that the maximum horizontal displacement reached ۰.۶۲ meters, with significant stress concentrations of ۳۲۰ MPa at the base of the steel towers—close to the yield strength of structural steel. Additionally, suspension cables experienced tension levels reaching ۹۰% of their design capacity during the Hayward Fault scenario, indicating a risk of fatigue failure. The study identifies critical zones prone to damage and proposes risk mitigation strategies, including reinforcement of steel towers, continuous cable tension monitoring, and preemptive deck repairs. The digital twin provided real-time insights into the structural health of the Bay Bridge, offering a proactive approach for seismic preparedness and disaster management. These findings demonstrate the transformative potential of digital twins in enhancing the resilience and safety of large-scale infrastructure.

کلیدواژه ها:

Digital Twin ، Seismic Prediction ، Structural Health Monitoring ، Bay Bridge ، Earthquake Management ، Finite Element Analysis ، Real-Time Monitoring ، Risk Mitigation