This thrust focuses on uncertainty-aware methods for structural health monitoring, damage diagnosis, model updating, prognosis, and decision support.
The goal is to move from deterministic monitoring outputs toward confidence-aware interpretation, where engineers can understand not only what a model predicts, but also how reliable that prediction may be.
What We Study#
Uncertainty-Aware Monitoring
Methods for understanding how measurement noise, modelling assumptions, environmental variability, and limited data affect infrastructure monitoring outcomes.
Probabilistic Diagnosis
Approaches for interpreting structural condition with confidence estimates, rather than relying only on deterministic damage indicators or classifications.
Confidence-Aware Prediction
Methods that support infrastructure prognosis and decision-making by accounting for uncertainty in data, models, and future structural behaviour.
Risk-Informed Decision Support
Frameworks that connect monitoring outputs with inspection, maintenance, and rehabilitation decisions under uncertainty.
Methods and Tools#
Research in this thrust may involve probability, statistics, Bayesian reasoning, inverse problems, structural reliability, machine learning, digital twins, and structural health monitoring.
The emphasis is on developing monitoring and prediction methods that are not only accurate, but also calibrated, interpretable, and useful for engineering decisions.
Student Background#
Students interested in this thrust may benefit from background in probability, statistics, Bayesian inference, structural reliability, inverse problems, machine learning, or structural health monitoring.
It is not necessary to have expertise in all areas. Specific topics are shaped based on the student’s background, interests, and expected time commitment.
Interested Students#
Students interested in uncertainty-aware monitoring, probabilistic diagnosis, infrastructure prognosis, Bayesian methods, or decision support are encouraged to read the broader Research page and contact me through the Join the Group page.
Specific project topics are discussed individually after understanding the student’s background, interests, and available research opportunities.