Abstract / Description of output
Lightweight aggregate is commonly used to reduce the self-weight of concrete elements which can efficiently mitigate the environmental impact. This study investigates the mechanical performance of normal weight aggregate concrete (NWAC) and Lightweight aggregate concrete (LWAC) exposed to high temperature, focusing on developing a predictive model for the stress-strain relationship of the LWAC based on the property of cement, silica fume, light weight aggregate and water/cement ratio. Mechanical properties of 30 different mixtures, including uniaxial compressive stress–strain relationship, compressive strength, modulus of elasticity, and failure strain are determined before and after high temperature exposures at 250, 500, and 750 °C. Results show that the modulus of elasticity and compressive strength of LWAC increased with exposure temperature. In particular, mixture S4 was found to outperform other mixtures, which retained 96, 75, and 46 % of compressive strength following exposures to 250, 500, and 750 °C, respectively. At all the temperatures investigated, the specimens of this mixture were able to achieve satisfactory modulus of elasticity and maximum strain. The specimens prepared with mixtures containing 75 % LECA (S23) showed a higher retention compressive strength at 750 °C. This study also utilizes the long short-term memory (LSTM) neural network to predict the stress-strain relationship of NWAC and LWAC mixtures after exposure to high temperatures. The results show that the LSTM model could adequately predict the stress-strain relationship of both LWAC and NWAC mixtures in terms of compressive strength, modulus of elasticity, and failure strain at elevated temperatures.
Keywords / Materials (for Non-textual outputs)
- Lightweight Concrete
- Artificial Intelligence
- Deep Learning
- Long Short-Term Memory
- Fire resistance