AI in structural engineering

Machine learning and physics-informed AI are increasingly used to design and assess  structural response, but purely data-driven models can be opaque, poorly generalisable outside their training data, and disconnected from the underlying mechanics.

We work to combine data-driven methods with the mechanics-first principles at the core of our research programme — developing interpretable models that respect known physical constraints, and physics-informed architectures that use mechanics to guide learning rather than replace it.

Selected publications

• Shen, S. and Málaga‐Chuquitaype, C. "Learning Rocking Dynamics From Sparse Shake‐Table Data With Interpretable Physics‐Informed Neural Networks." Earthquake Engineering & Structural Dynamics (2026).
• Junda, E., Málaga-Chuquitaype, C. and Chawgien, K. "Interpretable machine learning models for the estimation of seismic drifts in CLT buildings." Journal of Building Engineering (2023): 106365.
• Málaga-Chuquitaype, C., "Machine learning in structural design: an opinionated review," Frontiers in Built Environment,  (2022).