Louay A. Aboul-Nour

Expanded Polystyrene (EPS) offers a viable pathway for balancing the fresh and hardened properties of infrastructure with environmental sustainability. This study evaluated six concrete mixes with varying EPS Styrofoam aggregate ratios and three water-to-binder (w/b) ratios, all of which incorporated silica fume and a superplasticizer. Eight machine learning (ML) algorithms (SVMs, GPR, ANNs, etc.) and a deep-learning LSTM model were utilized to preliminarily predict trends in EPS concrete properties. The experimental results indicated that the fine aggregate replacement outperformed the coarse aggregate replacement, retaining approximately 76% of the density of the control mixture, along with other property reductions. The fine aggregate replacement resulted in a compressive strength reduction of up to 46.4%, with losses in tensile strength of 20.9% and an improvement in workability of 3.4%. Finally, various Artificial intelligence (AI) models identified trends in the predictions of EPS properties based on the mixing ratio within a limited experimental dataset. In addition, explainable AI with SHAP analysis, quantifying feature contributions, ensured that the coarse aggregate replacement exerted a more significant negative impact on the mechanical properties and density than the fine aggregate replacement. Although these mixtures offer significant weight reduction, their use in structural applications requires further verification, as the reduction of nearly half of the compressive strength is significant. These findings provide strategies and a framework to facilitate the precise practical application of EPS concrete in nonstructural or lightly loaded applications.

Expanded Polystyrene (EPS) offers a viable pathway for balancing the fresh and hardened properties of infrastructure with environmental sustainability. This study evaluated six concrete mixes with varying EPS Styrofoam aggregate ratios and three water-to-binder (w/b) ratios, all of which incorporated silica fume and a superplasticizer. Eight ML algorithms (SVM, GPR, ANN, etc.) and a deep-learning LSTM model were utilized to preliminarily predict trends in EPS concrete properties, with SHAP analysis quantifying feature contributions. The experimental results indicated that the fine aggregate replacement outperformed the coarse aggregate replacement, retaining approximately 76% of the density of the control mixture, along with other property reductions. The fine aggregate replacement resulted in a compressive strength reduction of up to 46.4%, with losses in tensile strength of 20.9% and an improvement in workability of 3.4%. Finally, various AI models identified trends in the predictions of EPS properties based on the mixing ratio within a limited experimental dataset. In addition, SHAP analysis revealed that the coarse aggregate replacement exerted a more significant negative impact on the mechanical properties and density than the fine aggregate replacement. Although these mixtures offer significant weight reduction, their use in structural applications requires further verification, as the reduction of nearly half of the compressive strength is significant. These findings provide strategies and a framework to facilitate the precise practical application of EPS concrete in nonstructural or lightly loaded applications.