Using fuzzy system as AI tool to minimize thermal bridges in design of external walls

Buildings significantly contribute to climate change, accounting for approximately one-third of global energy consumption and a quarter of CO ₂ emissions. Therefore, all actions aimed at increasing building energy efficiency are of great importance. This study explores the application of fuzzy system – an artificial intelligence (AI) tool – for optimizing external wall designs, specifically focusing on minimizing thermal bridges at the window-to-wall connection. To achieve this, traditional thermal bridge analysis using the TRISCO program to generate training sets was employed. The data collected from thermal analysis served as input for machine learning. The fuzzy system was then utilized to estimate linear heat transmittance coefficients, which quantify heat loss through thermal bridges. The proposed AI approach demonstrates excellent performance, generating precise linear heat transmittance coefficient values. Importantly, due to its ability to generalize knowledge, the trained system accurately determines the value of the Ψ coefficient for cases not included in the training data – those for which traditional analysis using the TRISCO program had not been previously performed. By leveraging this approach for thermal bridge analysis, it becomes possible to reduce the need for classical analyses, which often involve time-consuming calculations, expensive experiments, and extensive designer expertise in selecting optimal solution.