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

作　　者：Harun TANYILDIZI Abdulkadir SENGUR Yaman AKBULUT Murat SAHtNa 

机构地区：[1]Faculty of Technology,Department of Civil Engineering,Firat University,Elazig 23100,Turkey [2]Faculty of Technology,Department of Electrical-Electronics Engineering,Fir at University,Elazig 23100,Turkey

出　　处：《Frontiers of Structural and Civil Engineering》2020年第6期1316-1330,共15页结构与土木工程前沿（英文版）

基　　金：The experimental part of this study was supported by the Firat University BAPYB(Project No.TEF.12.04);he authors gratefully acknowledge the Firat University of BAPYB.

摘　　要：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℃zb2℃ in a standard cure for 7,28,and 90 d.After 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 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.

关 键 词：concrete high temperature strength properties deep learning stacked auto-encoders LSTM network 

分 类 号：TU528[建筑科学—建筑技术科学]