Abstract

Energy efficiency in buildings drives the development of sustainable materials, with Phase Change Materials standing out for their contribution to the construction sector. Phase Change Materials, integrated into materials like cement or concrete, regulate indoor temperatures by absorbing heat during the day and releasing it at night.  Accurate thermal property assessment is crucial for optimizing these materials, yet conventional experimental methods are time-consuming, costly, and require specialized labor. While automation and machine learning streamline the process, they do not eliminate the need for expertise but rather shift the focus toward data-driven material innovation, complementing rather than replacing traditional roles. To enhance efficiency, our study integrates deep neural networks. A Generative Adversarial Network first augments the dataset, and a Multilayer Perceptron then predicts the properties of cementitious composites enriched with Phase Change Material and nano-silica aerogel. Using inputs such as mass composition and density, the model outputs compressive strength and thermal conductivity. Training with synthetic data yields high predictive accuracy, highlighting the potential of data augmentation in domains with limited datasets. This approach enhances the precision and efficiency of assessing thermal performance in innovative construction materials while supporting the evolving role of experts in the field.