Hybrid deep learning model for concrete incorporating microencapsulated phase change materials

Abstract

The inclusion of microencapsulated phase change materials (MPCMs) in concrete promotes thermal energy storage, thus enhancing sustainable design. Notwithstanding this advantage, the compressive strength of concrete dramatically decreases upon MPCM addition. While several experimental studies have explored the origin of this compressive strength reduction, a reliable and practical framework for the prediction of the compressive strength of MPCM-integrated concrete is yet to be developed. The current research proposes a deep learning approach to estimate the compressive strength of MPCM-integrated cementitious composites based on its mixture proportions and the thermophysical properties of PCM. Extreme learning machines (ELMs), autoencoders, hybrid ELM-autoencoder, and extreme gradient boosting (XGBoost) models were purposefully developed using the largest pertinent experimental dataset available to date encompassing 244 mixture design examples retrieved from the open literature. The results demonstrate the capability of the hybrid deep learning and XGBoost models in accurately modeling the compressive strength of PCM integrated concrete with favorably low prediction error. Furthermore, a sensitivity analysis identified the most influential parameters on the compressive strength development to assist the mixture design of concrete incorporating MPCM.