Statistical investigation on physical–mechanical properties of base and polymer modified bitumen using Artificial Neural Network
作者： Karim Golzar , Azam Jalali-Arani , Mahsa Nematollahi
DOI: 10.1016/J.CONBUILDMAT.2012.08.011
关键词: Composite material 、 Process engineering 、 Artificial neural network 、 Ethylene-vinyl acetate 、 Asphalt 、 Polymer modified 、 MATLAB 、 Butyl acrylate 、 Materials science 、 Experimental data
摘要: Abstract Improving bitumen properties by using polymeric materials such as ethylene butyl acrylate, styrene–ethylene–butylene–styrene, vinyl acetate and styrene–butadiene–styrene has been reported the researchers. To avoid practical measuring difficulties, Artificial Neural Network employed to predict properties. The presented study, employing data obtained researchers MATLAB program, suggested models physical–mechanical of modified bitumen. In process, an appropriate architecture each desirable try error method. Comparing model results with experimental showed high acceptability models.
sci-hub.se 参考文章(47)
G Airey, Rheological properties of styrene butadiene styrene polymer modified road bitumens Fuel. ,vol. 82, pp. 1709- 1719 ,(2003) , 10.1016/S0016-2361(03)00146-7
Nadjet Dehouche, Mustapha Kaci, Khadija Ait Mokhtar, Influence of thermo-oxidative aging on chemical composition and physical properties of polymer modified bitumens Construction and Building Materials. ,vol. 26, pp. 350- 356 ,(2012) , 10.1016/J.CONBUILDMAT.2011.06.033
Y PAN, J JIANG, Z WANG, Quantitative structure–property relationship studies for predicting flash points of alkanes using group bond contribution method with back-propagation neural network Journal of Hazardous Materials. ,vol. 147, pp. 424- 430 ,(2007) , 10.1016/J.JHAZMAT.2007.01.025
Xiaohu Lu, Ulf Isacsson, Laboratory study on the low temperature physical hardening of conventional and polymer modified bitumens Construction and Building Materials. ,vol. 14, pp. 79- 88 ,(2000) , 10.1016/S0950-0618(00)00012-X
Serdal Terzi, Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks Construction and Building Materials. ,vol. 21, pp. 590- 593 ,(2007) , 10.1016/J.CONBUILDMAT.2005.11.001
Jyh-Shyong Chang, Bo-Chang Hung, Optimization of Batch Polymerization Reactors Using Neural-Network Rate-Function Models Industrial & Engineering Chemistry Research. ,vol. 41, pp. 2716- 2727 ,(2002) , 10.1021/IE0100075
Motoyuki Sugano, Yuusuke Iwabuchi, Tohru Watanabe, Jun Kajita, Kohji Iwata, Katsumi Hirano, Relations between thermal degradations of SBS copolymer and asphalt substrate in polymer modified asphalt Clean Technologies and Environmental Policy. ,vol. 12, pp. 653- 659 ,(2010) , 10.1007/S10098-010-0301-9
Ridha B. Gharbi, Adel M. Elsharkawy, Mansour Karkoub, Universal neural-network-based model for estimating the PVT properties of crude oil systems Energy & Fuels. ,vol. 13, pp. 454- 458 ,(1999) , 10.1021/EF980143V
Abid A. Shah, Saleh H. Alsayed, H. Abbas, Yousef A. Al-Salloum, Predicting residual strength of non-linear ultrasonically evaluated damaged concrete using artificial neural network Construction and Building Materials. ,vol. 29, pp. 42- 50 ,(2012) , 10.1016/J.CONBUILDMAT.2011.10.038
Luciano Pivoto Specht, Oleg Khatchatourian, Lélio Antônio Teixeira Brito, Jorge Augusto Pereira Ceratti, None, Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks Materials Research-ibero-american Journal of Materials. ,vol. 10, pp. 69- 74 ,(2007) , 10.1590/S1516-14392007000100015