Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Mechanical Properties of High Performance Concrete using Industrial Byproducts
This paper examines the possibilities of using the combination of byproducts like 20% Bottom ash (BA) as fine aggregate and ordinary Portland cement with optimum replacement levels of 20% Fly ash (FA), 10% Silica fume (SF), 10%Metakaolin (MK) by adopting water cement ratio of 0.45. Conplast SP430 was used as a superplasticizer for better workability for high performance concrete. The mechanical properties Such as Compressive strength (Cubes and cylinders), flexural strength and Modulus of elasticity were determined. The result of this investigation explains the strength characteristics of all mixes. Based on the results obtained the replacement of combination of mixes which is superior for all the mixes and also results were compared with control mix. The data obtained from experimental tests were used to train an Artificial Neural Network (ANN) which can predict the experimental results were compared in the testing data set. As a result, compressive strength, flexural strength and modulus of elasticity can be predicted in a quite short period of time with tiny error rates by using the multilayer feed-forward neural network models than regression techniques.Â
Mechanical Properties of High Performance Concrete using Industrial Byproducts
This paper examines the possibilities of using the combination of byproducts like 20% Bottom ash (BA) as fine aggregate and ordinary Portland cement with optimum replacement levels of 20% Fly ash (FA), 10% Silica fume (SF), 10%Metakaolin (MK) by adopting water cement ratio of 0.45. Conplast SP430 was used as a superplasticizer for better workability for high performance concrete. The mechanical properties Such as Compressive strength (Cubes and cylinders), flexural strength and Modulus of elasticity were determined. The result of this investigation explains the strength characteristics of all mixes. Based on the results obtained the replacement of combination of mixes which is superior for all the mixes and also results were compared with control mix. The data obtained from experimental tests were used to train an Artificial Neural Network (ANN) which can predict the experimental results were compared in the testing data set. As a result, compressive strength, flexural strength and modulus of elasticity can be predicted in a quite short period of time with tiny error rates by using the multilayer feed-forward neural network models than regression techniques.Â
Mechanical Properties of High Performance Concrete using Industrial Byproducts
Jayaranjini, A. (Autor:in) / Vidivelli, B. (Autor:in)
25.10.2015
Asian Journal of Applied Sciences; Vol. 3 No. 5 (2015): October 2015 ; 2321-0893
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
Geopolymer concrete using industrial byproducts
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