Welcome to the Structural Engineering and Health Monitoring (SEHM) Lab at LMU
The high occupancy of urban multistory buildings, the aging of critical infrastructure, and evolving safety and sustainability expectations all demand new performance objectives for civil structures. Research at the Structural Engineering and Health Monitoring (SEHM) Lab at Loyola Marymount University focuses on developing resilient, damage-limiting structural systems and data-informed methodologies for buildings and bridges subjected to earthquakes and other natural hazards.
SEHM Lab research integrates analytical modeling, computational simulation, and experimental validation, with emphasis on:
- Rocking and self-centering structural systems for damage-limiting seismic response and retrofit of buildings and bridges
- Physics-based and probabilistic digital twins for real-time structural health monitoring and condition assessment
- High-fidelity finite element and reduced-order modeling to enable efficient simulation, model updating, and uncertainty quantification
Current work in the SEHM Lab combines numerical studies with laboratory-scale and system-level investigations to better understand structural response, improve predictive capabilities, and support performance-based engineering decisions.
For more details on ongoing research activities and projects, please visit the lab’s Research page.
Featured Research
Structural Health Monitoring & Digital Twins
Experimental and computational methods for vibration-based structural health monitoring, physics-informed machine learning, and uncertainty-aware digital twins for infrastructure condition assessment.
Earthquake Engineering & Resilient Systems
Rocking, self-centering, and damage-limiting structural systems designed to reduce seismic damage and improve post-earthquake functionality.
Computational Modeling & Simulation
High-fidelity finite element modeling, reduced-order methods, model updating, and uncertainty quantification for efficient and reliable prediction of structural response.