Vision and Scope
The Building Physics research group is dedicated to advancing the scientific understanding and optimisation of energy performance and moisture safety in buildings. Our work addresses the urgent need for climate-resilient, energy-efficient, and healthy indoor environments ensuring optimal occupant comfort. By integrating advanced physical modelling with AI, we bridge fundamental research with practical engineering solutions to enhance the performance and longevity of the built environment.
Key Research Themes
- Moisture Safety and Durability — We employ advanced stochastic and AI-based metamodeling techniques to capture and quantify uncertainties in coupled heat and moisture transport processes. These probabilistic methods enable risk-informed envelope design, moving beyond traditional deterministic safety margins toward a reliability-based engineering framework. Our research develops innovative strategies to mitigate moisture-related risks in wood-based, lightweight, and high-performance façade and roof systems, ensuring durability under both current and future climate conditions.
- Energy Performance Analysis — The interaction between thermal and moisture dynamics plays a pivotal role in overall building performance. We investigate the coupled energy–moisture behaviour of building envelopes to optimise energy efficiency, minimise failure risks, and extend service life. Particular emphasis is placed on future climate scenarios to ensure long-term building resilience and occupant well-being.
Ongoing Projects
- AI-driven Control of Energy and Moisture Dynamics — Integration of smart systems and AI to predict, control, and manage the energy performance and moisture safety of residential buildings.
- Moisture Risks and Occupant Health — Investigation of the links between hygrothermal conditions and health-related outcomes, including mould growth, indoor air quality, and long-term exposure risks.
Recommended Publications
- Robust probabilistic modelling of mould growth in building envelopes using random forests machine learning algorithm
- Mould growth criteria and design avoidance approaches in wood-based materials – A systematic review
- A literature review of probabilistic hygrothermal assessment for building envelopes
- Hygrothermal assessment of timber frame walls using a convolutional neural network
- Impact of extruded mortar joints on the hygrothermal performance of brick veneer walls: A probabilistic study
- Canadian initial-condition climate ensemble: Hygrothermal simulation on wood-stud and retrofitted historical masonry
- A multi-criteria decision analysis for building envelope design: Bridging moisture safety and health risks