Doctoral Defense by Chen Xu

Chen Xu presented his doctoral theses with the title "Physics Meets Data: Machine Learning Frameworks for Structural Engineering" on 31st March 2026 at 1:30PM.


Dear Chen, well done, and congratulations! We wish you all the best for your future, both professionally and personally.


Abstract:
Structural engineering increasingly requires models that are both data-driven and physically consistent, especially when real-world measurements are sparse, noisy, and heterogeneous. This thesis develops physics-informed machine learning (PIML) frameworks that combine physical knowledge with observations to address three key challenges. First, it enables the identification of unknown loads in tunnel linings from limited displacement measurements. Second, it supports real-time prediction of tunnelinginduced ground settlement by integrating simulations with monitoring data. Third, it improves concrete damage classification in structural health monitoring by transferring knowledge from synthetic data to real experiments. Across these applications, the proposed approaches enhance accuracy, efficiency, and robustness, demonstrating the potential of PIML for more reliable structural analysis, monitoring, and maintenance.



Promotionsaushang_Chen_Xu.pdf