This thrust focuses on methods for detecting, localizing, and interpreting damage in civil infrastructure systems. The goal is to develop damage diagnosis approaches that are connected to structural response, deformation patterns, stiffness changes, vibration behaviour, and engineering interpretation.
Rather than relying only on black-box classification, the work emphasizes physically meaningful indicators, interpretable models, and decision-support methods for infrastructure condition assessment.
What We Study#
Damage-Sensitive Structural Response
Methods for using measured or simulated structural response to identify abnormal behaviour, stiffness changes, local deterioration, and possible damage-sensitive patterns.
Condition Assessment
Approaches for interpreting sensing, vibration, visual, and computational data to support assessment of the current condition of civil infrastructure systems.
Physics-Guided Diagnosis
Damage diagnosis methods that use mechanics, structural dynamics, and engineering knowledge to improve reliability and interpretability.
AI-Assisted Damage Interpretation
Machine learning methods for identifying structural condition states while maintaining connection to measurable response features and engineering meaning.
Methods and Tools#
Research in this thrust may involve structural analysis, structural dynamics, finite element simulations, signal processing, image-based inspection, machine learning, and experimental measurements.
The emphasis is on developing methods that can support reliable damage detection, localization, condition assessment, and infrastructure health monitoring under realistic uncertainty and measurement limitations.
Student Background#
Students interested in this thrust may benefit from background in structural analysis, structural dynamics, finite element modelling, signal processing, machine learning, experimental mechanics, or infrastructure inspection.
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 damage detection, condition assessment, structural health monitoring, or AI-assisted infrastructure diagnosis 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.