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An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第11期9卷 1514-1523页

作者： Okan KARAHAN harun tanyildizi Cengiz D. ATIS Civil Engineering Department Erciyes University Construction Education Department Fιrat University

In this study, an artificial neural network (ANN) model for studying the strength properties of steel fiber reinforced concrete (SFRC) containing fly ash was devised. The mixtures were prepared with 0 wt%, 15 wt%, and 30 wt% of fly ash, at 0 vol.%, 0.5 vol.%, 1.0 vol.% and 1.5 vol.% of fiber, respectively. After being cured under the standard conditions for 7, 28, 90 and 365 d, the specimens of each mixture were tested to determine the corresponding compressive and flexural strengths. The pa- rameters such as the amounts of cement, fly ash replacement, sand, gravel, steel fiber, and the age of samples were selected as input variables, while the compressive and flexural strengths of the concrete were chosen as the output variables. The back propagation learning algorithm with three different variants, namely the Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Fletcher-Powell conjugate gradient (CGF) algorithms were used in the network so that the best approach can be found. The results obtained from the model and the experiments were compared, and it was found that the suitable algorithm is the LM algorithm. Furthermore, the analysis of variance (ANOVA) method was used to determine how importantly the experimental parameters affect the strength of these mixtures.

关键词：飞尘钢纤维强度范围人工神经网络 ANOVA

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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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温度、炭纤维和硅土粉对轻量混凝土力学性能的影响

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新型炭材料 2008年第4期23卷 339-344页

作者： harun tanyildizi Department of Construction Education Firat University Elazig TURKEY

研究了温度、炭纤维和硅土粉含量对轻量混凝土力学性能的影响。制备了含硅土粉(质量分数0%和10%)及炭纤维(质量分数0%,0.5%,1%,和2%)的混合物,测定了高温(400℃,600℃和800℃)暴烤后轻量混凝土的压缩强度和弯曲强度。采用三个控制因素(... 详细信息

研究了温度、炭纤维和硅土粉含量对轻量混凝土力学性能的影响。制备了含硅土粉(质量分数0%和10%)及炭纤维(质量分数0%,0.5%,1%,和2%)的混合物,测定了高温(400℃,600℃和800℃)暴烤后轻量混凝土的压缩强度和弯曲强度。采用三个控制因素(硅土粉含量、炭纤维含量及处理温度),应用Taguchi法确定最佳条件并减少实验次数,采用方差(Anova)法确定主要实验参数与轻量混凝土力学性能的基准。结果表明:轻量混凝土力学性能最实效的参数是加热程度,而抗拉强度与抗弯强度的最佳参数各不相同。

关键词：轻量混凝土炭纤维力学性能变异(Anova)分析 Taguchi法

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