Thermal Performance Prediction for Alkali-Activated Concrete Using GGBFS, NaOH and Sodium Silicate

Document Type : Technical Notes

Authors

1 Assistant Professor, Department of Civil Engineering, Mohan Babu University (SVEC), Tirupati, Andhra Pradesh, India.

2 Research Scholar, Department of Civil Engineering, National Institute of Technology, Jamshedpur, Jharkhand, India.

3 Assistant Professor, Department of Civil Engineering, IIMT University, Meerut, Uttar Pradesh, India.

Abstract

In fire safety, understanding the behaviour of concrete exposed to high temperatures is essential. This study experimentally explored the mechanical properties of Alkali-Activated Concrete (AAC) and utilized Recurrent Neural Network (RNN)-based Long Short-Term Memory (LSTM) techniques to predict the mechanical properties of AAC at elevated temperatures. The LSTM models accurately predicted compressive, flexural, and split tensile strengths, with coefficients of determination (R²) exceeding 0.9 for training and testing datasets. Specifically, R² values were 0.9838 and 0.9134 for compressive strength, 0.9965 and 0.9861 for flexural strength, and 0.9743 and 0.9852 for split tensile strength in training and testing, respectively. The models also yielded low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, further underscoring their predictive reliability. Error analysis across all mechanical properties affirmed the LSTM models' robustness in capturing AAC's complex behaviour under thermal stress. These results suggest that LSTM networks are highly effective tools for predicting material properties crucial for structural fire safety and sustainable construction, offering a promising approach for improving the resilience and safety of AAC structures in extreme conditions.

Keywords

References

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Civil Engineering Infrastructures Journal
Volume 59, Issue 1
June 2026
Pages 241-253

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History

  • Receive Date: 15 December 2023
  • Revise Date: 28 August 2024
  • Accept Date: 01 September 2024

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