Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures

Abstract

In this study, the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised. Silica fume was used at concentrations of 0%, 5%, 10%, and 20%. Cube specimens (100 mm x 100 mm x 100 mm) were prepared for testing the compressive strength and ultrasonic pulse velocity. They were cured at 20 degrees C +/- 2 degrees C in a standard cure for 7, 28, and 90 d. After curing, they were subjected to temperatures of 20 degrees C, 200 degrees C, 400 degrees C, 600 degrees C, and 800 degrees C. Two well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM) networks, were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures. The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectively. Various statistical measures were used to validate the prediction performances of both the approaches. This study found that the LSTM network achieved better results than the stacked autoencoders. In addition, this study found that deep learning, which has a very good prediction ability with little experimental data, was a convenient method for civil engineering.