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| 1 | Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures显示文摘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. | Harun TANYILDIZI Abdulkadir SENGUR Yaman AKBULUT Murat SAHtNa | 2020 | Frontiers of Structural and Civil Engineering2020，14，6： | 1 |
| 2 | 玄武岩纤维再生混凝土路用性能研究显示文摘为研究废旧水泥路面再生骨料对混凝土路用性能影响,基于试验探究再生骨料取代率对混凝土强度及耐磨性能影响。根据再生混凝土初步试验结果,进一步掺加玄武岩纤维,探讨玄武岩纤维掺量对再生混凝土抗压、抗折强度及抗裂性能影响。结果表明,随着再生骨料取代率的增加,再生混凝土抗压、抗折强度及耐磨性能均不断降低。在混凝土中掺加适量的玄武岩纤维有利于提高再生混凝土抗压强度、抗折强度,极大程度改善再生混凝土抗裂性能。 | 杨军 | 2016 | 公路交通科技(应用技术版)2016，12，4： | 1 |
| 3 | Compressive behavior of hybrid steel-polyvinyl alcohol fiber-reinforced concrete containing fly ash and slag powder:experiments and an artificial neural network model显示文摘Understanding the mechanical behavior of hybrid fiber-reinforced concrete(HFRC),a composite material,is crucial for the design of HFRC and HFRC structures.In this study,a series of compression experiments were performed on hybrid steelpolyvinyl alcohol(PVA)fiber-reinforced concrete containing fly ash and slag powder,with a focus on the fiber content/ratio effect on its compressive behavior;a new approach was built to model the compression behavior of HFRC by using an artificial neural network(ANN)method.The proposed ANN model incorporated two new developments:the prediction of the compressive stress-strain curve and consideration of 23 features of components of HFRC.To build a database for the ANN model,relevant published data were also collected.Three indices were used to train and evaluate the ANN model.To highlight the performance of the ANN model,it was compared with a traditional equation-based model.The results revealed that the relative errors of the predicted compressive strength and strain corresponding to compressive strength of the ANN model were close to 0,while the corresponding values from the equation-based model were higher.Therefore,the ANN model is better able to consider the effect of different components on the compressive behavior of HFRC in terms of compressive strength,the strain corresponding to compressive strength,and the compressive stress-strain curve.Such an ANN model could also be a good tool to predict the mechanical behavior of other composite materials. | Fang-yu LIU Wen-qi DING Ya-fei QIAO Lin-bing WANG Qi-yang CHEN | 2021 | Journal of Zhejiang University-Science A(Applied Physics & Engineering)2021，22，9： | 0 |
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