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May 22, 2026Structural Concrete0 citations

AI-Powered Development of Sustainable Lightweight Partition Wall Systems

AI ‐powered development of sustainable lightweight partition wall systems using neural networks

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Authors

FMFayez MoutassemMKMohamad KharsehMFMaissa Farhat

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Overview

Randomized trial demonstrates improved compressive strength of EPS concrete using neural networks, highlighting sustainability benefits.

Key Points

  • The aim is to develop an artificial neural network model to predict the compressive strength of lightweight expanded polystyrene concrete.
  • Developed an ANN model to predict EPS concrete strength, validated through 30 mix experiments.
  • Parameters included water content, EPS content, ordinary Portland cement, and air content.
  • Conducted a comprehensive experimental program to calibrate the ANN model.
  • ANN model exhibited high accuracy with R² > 0.98, RMSE ≈ 0.12 MPa, and MSE ≈ 0.014 MPa.
  • Proposed precast EPS sandwich panels achieved low U-values of approximately 0.66 W/m²K (100 mm panel) and 0.42 W/m²K (150 mm panel).
  • Highlights potential to enhance energy efficiency and sustainability within the construction industry.

Cite This Study

Moutassem et al. (2026) studied this question.

synapsesocial.com/papers/6a0ff3ffd674f7c03778cf99https://doi.org/10.1002/suco.70648
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