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

Kumar, Pramod; Sharma, Sanjay; Kumar, P Siva; Yuvaraj, M. S.; Purushotham, D V; Kumar, Saurabh; Vashistha, Kapil Kumar; Kumar, Amit; Gogineni, Abhilash

doi:10.22059/ceij.2024.369661.1996

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

Articles in Press

Document Type : Technical Notes

Authors

¹ Assistant Professor, Department of Civil Engineering, Mohan Babu University (SVEC), Tirupati, Andhra Pradesh, India- 517102

² Research Scholar, Department of Civil Engineering, National Institute of Technology, Jamshedpur, Jharkhand-831013

³ Assistant Professor, Department of Civil Engineering, IIMT University, Meerut, Uttar Pradesh, India-250001

⁴ National Institute of Technology Jamshedpur

10.22059/ceij.2024.369661.1996

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 alkali-activated concrete (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.

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