A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prediction of mechanical properties of waste polypropylene/waste ground rubber tire powder blends using artificial neural networks
AbstractRecycling represents a valid alternative to the disposal of post-consumer materials if it is possible to obtain new materials with good properties. In this work, waste polypropylene (WPP)/waste ground rubber tire (WGRT) powder blends were studied with respect to the effect of bitumen and maleic anhydride-grafted styrene–ethylene–butylene–styrene (SEBS-g-MA) content by using the design of experiments (DOE) approach, whereby the effect of the four polymers content on the final mechanical properties were predicted. Uniform design method was especially adopted for its advantages. Optimization was done using hybrid artificial neural network–genetic algorithm (ANN–GA) technique. The results indicated that the blends show fairly good ductibility provided that it had a relatively higher concentration of bitumen and SEBS-g-MA under the studied condition. A quantitative relationship was presented between the material concentration and the mechanical properties as a set of contour plots, which were confirmed experimentally by testing the optimum ratio.
Prediction of mechanical properties of waste polypropylene/waste ground rubber tire powder blends using artificial neural networks
AbstractRecycling represents a valid alternative to the disposal of post-consumer materials if it is possible to obtain new materials with good properties. In this work, waste polypropylene (WPP)/waste ground rubber tire (WGRT) powder blends were studied with respect to the effect of bitumen and maleic anhydride-grafted styrene–ethylene–butylene–styrene (SEBS-g-MA) content by using the design of experiments (DOE) approach, whereby the effect of the four polymers content on the final mechanical properties were predicted. Uniform design method was especially adopted for its advantages. Optimization was done using hybrid artificial neural network–genetic algorithm (ANN–GA) technique. The results indicated that the blends show fairly good ductibility provided that it had a relatively higher concentration of bitumen and SEBS-g-MA under the studied condition. A quantitative relationship was presented between the material concentration and the mechanical properties as a set of contour plots, which were confirmed experimentally by testing the optimum ratio.
Prediction of mechanical properties of waste polypropylene/waste ground rubber tire powder blends using artificial neural networks
Zhang, Shu Ling (author) / Zhang, Zhen Xiu (author) / Pal, Kaushik (author) / Xin, Zhen Xiang (author) / Suh, Joshua (author) / Kim, Jin Kuk (author)
2010-02-22
6 pages
Article (Journal)
Electronic Resource
English
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