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<p>Geopolymer concrete (GPC) has emerged as a sustainable alternative to ordinary Portland cement due to its reduced carbon footprint and efficient utilization of industrial and waste-derived materials. However, predicting its compressive strength remains challenging because of complex nonlinear interactions among binder composition, alkaline activator chemistry, aggregate proportions, and curing conditions. This study proposes a Long Short-Term Memory (LSTM)-based deep learning model for accurate prediction of geopolymer concrete compressive strength. A comprehensive experimental dataset incorporating fly ash, eggshell powder replacement, SiO₂/Na₂O ratio, fine and coarse aggregate contents, reaction liquid dosage, and curing age was utilized. Exploratory data analysis, including histogram distributions, kernel density estimation, correlation matrix evaluation, and multivariate pair plots, revealed structured experimental variation and strong interdependency among mix parameters. The LSTM model was trained and validated using optimized hyperparameters, and its performance was evaluated through MSE, RMSE, and R² metrics. Training and validation loss curves demonstrated stable convergence without overfitting. The strong agreement between predicted and experimental values, along with normally distributed residuals centered around zero, confirms the robustness and generalization capability of the proposed framework. The study highlights the potential of deep learning techniques for intelligent, data-driven design and optimization of sustainable geopolymer concrete systems.</p>

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Utilizing long short-term memory deep learning models to predict the compressive strength of geopolymer concrete

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Konnoju Saikumar Chary,
  • Vemu Venkata Praveen Kumar,
  • Lingamallu Raghu Kumar,
  • N. L. N. Kiran Kumar,
  • Vasu Namala,
  • G. Srikanth

摘要

Geopolymer concrete (GPC) has emerged as a sustainable alternative to ordinary Portland cement due to its reduced carbon footprint and efficient utilization of industrial and waste-derived materials. However, predicting its compressive strength remains challenging because of complex nonlinear interactions among binder composition, alkaline activator chemistry, aggregate proportions, and curing conditions. This study proposes a Long Short-Term Memory (LSTM)-based deep learning model for accurate prediction of geopolymer concrete compressive strength. A comprehensive experimental dataset incorporating fly ash, eggshell powder replacement, SiO₂/Na₂O ratio, fine and coarse aggregate contents, reaction liquid dosage, and curing age was utilized. Exploratory data analysis, including histogram distributions, kernel density estimation, correlation matrix evaluation, and multivariate pair plots, revealed structured experimental variation and strong interdependency among mix parameters. The LSTM model was trained and validated using optimized hyperparameters, and its performance was evaluated through MSE, RMSE, and R² metrics. Training and validation loss curves demonstrated stable convergence without overfitting. The strong agreement between predicted and experimental values, along with normally distributed residuals centered around zero, confirms the robustness and generalization capability of the proposed framework. The study highlights the potential of deep learning techniques for intelligent, data-driven design and optimization of sustainable geopolymer concrete systems.