Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures
Harun TANYILDIZI1(), Abdulkadir ŞENGÜR2, Yaman AKBULUT2, Murat ŞAHİN1
1. Faculty of Technology, Department of Civil Engineering, Firat University, Elazig 23100, Turkey 2. Faculty of Technology, Department of Electrical-Electronics Engineering, Firat University, Elazig 23100, Turkey
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 × 100 mm × 100 mm) were prepared for testing the compressive strength and ultrasonic pulse velocity. They were cured at 20°C±2°C in a standard cure for 7, 28, and 90 d. After curing, they were subjected to temperatures of 20°C, 200°C, 400°C, 600°C, and 800°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.
. [J]. Frontiers of Structural and Civil Engineering, 2020, 14(6): 1316-1330.
Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN. Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures. Front. Struct. Civ. Eng., 2020, 14(6): 1316-1330.
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