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Thermal Performance Prediction for Alkali-Activated Concrete Using GGBFS, NaOH, and Sodium Silicate | ||
| Civil Engineering Infrastructures Journal | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 18 مهر 1403 اصل مقاله (1.46 M) | ||
| نوع مقاله: Technical Notes | ||
| شناسه دیجیتال (DOI): 10.22059/ceij.2024.369661.1996 | ||
| نویسندگان | ||
| Pramod Kumar1؛ Sanjay Sharma2؛ P Siva Kumar1؛ M. S. Yuvaraj1؛ D V Purushotham1؛ Saurabh Kumar1؛ Kapil Kumar Vashistha3؛ Amit Kumar* 3؛ Abhilash Gogineni4 | ||
| 1Assistant Professor, Department of Civil Engineering, Mohan Babu University (SVEC), Tirupati, Andhra Pradesh, India- 517102 | ||
| 2Research Scholar, Department of Civil Engineering, National Institute of Technology, Jamshedpur, Jharkhand-831013 | ||
| 3Assistant Professor, Department of Civil Engineering, IIMT University, Meerut, Uttar Pradesh, India-250001 | ||
| 4National Institute of Technology Jamshedpur | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Alkali-activated concrete؛ Elevated temperature؛ LSTM؛ Mechanical properties؛ Prediction of strengths | ||
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آمار تعداد مشاهده مقاله: 80 تعداد دریافت فایل اصل مقاله: 102 |
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