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

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Frontiers of Structural and Civil Engineering 2020年第6期14卷 1316-1330页

作者： Harun TANYILDIZI Abdulkadir SENGUR Yaman AKBULUT murat sahtna Faculty of Technology Department of Civil EngineeringFirat UniversityElazig 23100Turkey Faculty of Technology Department of Electrical-Electronics EngineeringFir at UniversityElazig 23100Turkey

In this study,the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were *** 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 *** were cured at 20℃zb2℃ in a standard cure for 7,28,and 90 *** curing,they were subjected to temperatures of 20℃,200℃,400℃,600℃,and 800℃.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 *** forecasting experiments were carried out using MATLAB deep learning and neural network tools,*** statistical measures were used to validate the prediction performances of both the *** study found that the LSTM network achieved better results than the stacked *** 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.

关键词： concrete high temperature strength properties deep learning stacked auto-encoders LSTM network

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