Hygrothermal modeling in mass timber constructions: Recent advances and machine learning prospects
Sina Akhavan Shams, Hua Ge, Lin Wang
Abstract
This review paper provides a comprehensive overview of hygrothermal modeling with a focus on its application in mass timber construction, a sustainable and innovative construction approach. The first part of the review investigates the recent advancements in modeling heat and moisture transfer in wood-based materials, focusing on the key factors that influence moisture transport in wood. This section also covers the recent advances related to other bio-based building materials. The second part of the review discusses the latest findings in hygrothermal modeling applied to mass timber constructions, presenting various modeling approaches, tools, and simulation techniques. The discussion addresses how findings on heat and moisture transfer can enhance hygrothermal modeling of mass timber constructions, thereby improving the accuracy of simulations. Finally, the paper presents a thorough literature review on the application of machine learning in hygrothermal modeling. The emerging field of machine learning and its potential to enhance prediction accuracy for building assemblies is investigated. Key research gaps are identified with recommendations for future studies aimed at developing “white-box” simulation models tailored specifically for assessing hygrothermal performance of mass timber constructions. Such models could provide more reliable datasets, particularly useful for training black-box machine learning models. Additionally, the review highlights the potential of machine learning algorithms to accurately simulate complex heat and moisture transfers. It suggests further research to conduct a comparative analysis of algorithm performance and to explore integrating these “black-box” models with physics-based numerical models. • Review of state-of-the-art in hygrothermal modeling of mass timber structures. • Importance of hysteresis and liquid transport in accurate hygrothermal simulations of mass timber structures. • Deep learning algorithms show promises in enhancing prediction accuracy in hygrothermal simulations. • Research gaps and future research trends in enhancing hygrothermal modeling reliability identified.