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Enclosed airspaces (EAs) are a common component of different energy efficient technologies in building envelopes which have intrinsic adaptive behavior under various climatic conditions. The need for comprehensive accurate numerical models without restrictions for different applications still exists. Thus, for the first time, machine learning and regression-based techniques (Ordinary least squares Linear Regression (OLR), Support Vector Regression (SVR), Kernel Ridge Regression (KRR), k-Nearest Neighbors (kNN), r-Nearest Neighbors (rNN), and Artificial Neural Network (ANN)) were applied to develop precise models derived from the most credible experimental data with stochastic logic of testing and training in several runs. It was found that application of KRR, SVR, and ANN leads to the most desirable outcomes with highest R 2  (0.97–0.99), and lowest errors, however OLR does not provide satisfactory results (R 2  &lt;0.6). Moreover, major deviations are observed for calculations by OLR, kNN, and rNN in horizontal EAs (thicknesses = 40mm) with downward heat flow direction.

Data-driven numerical models for the prediction of the thermal resistance value of the Enclosed Airspaces (EAs) in building envelopes