Pruning Long Short-Term Memory: A Model for Predicting the Stress–Strain Relationship of Normal and Lightweight Aggregate Concrete at Finite Temperature

Abstract

While normal weight aggregate concrete (NWAC) can experience significant strength loss and spalling at high temperatures, lightweight aggregate concrete (LWAC) can maintain its structural integrity. Stress–strain relationship of concrete is an important test to perform during designing phase of concrete infrastructures. Therefore, this study focuses on exploring the stress–strain behavior of NWAC and LWAC under uniaxial compression at temperatures ranging from 20 to 750°C. In addition, pruning long short-term memory (P-LSTM) networks to create a predictive model for the stress–strain relationship of NWAC and LWAC is also utilized. Concrete mixture designs containing ordinary Portland cement, silica fume, and lightweight expanded clay aggregate, were first optimized to reduce the number of experiments using the response surface method. Subsequently, 30 mixture designs were fabricated and subjected to compression tests, following exposure to varying temperatures that ranged from 20 to 750°C, to evaluate their stress–strain relationship and determine associated mechanical properties. Experimental results were then utilized to develop a P-LSTM model used to forecast the stress–strain relationship of concrete at varying temperatures. The P-LSTM model developed in this study improved the prediction accuracy and stability beyond conventional LSTM model, which would be useful in the design and optimization of NWAC and LWAC structures. Additionally, the P-LSTM model has a lower computational cost and less likelihood of over-fitting as compared to typical LSTM networks.