67 / 2025-11-23 22:06:04

Digital twin modeling for a tunnel freezing restoration project

Understanding how the temperature field develops and changes is essential for freezing restoration works. However, traditional analytical or numerical approaches are often hindered by limited monitoring data and overly idealized model settings. To improve temperature estimation, a PSO-driven digital twin model was constructed and applied to a tunnel freezing restoration case in Bangkok. Through the integration of real-time temperature measurements, the model updates parameters dynamically and enhances predictive reliability. Single-parameter tuning converges quickly and is suitable for early-stage adjustments, while multi-parameter optimization shows advantages in stratified or highly variable geological environments. When incorporating several observation points, the optimization may become trapped in local minima; thus, a GA-PSO hybrid scheme was introduced to address the challenge. Overall, the results validate the model’s capability and provide a practical solution for real-time thermal control in complex freezing operations.