Prediction of Compressive Strength of Aerated Lightweight Aggregate Concrete by Artificial Neural Network

Abstract:

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This paper presents artificial neural network techniques for predicting the compressive strength of Aerated Lightweight Aggregate Concrete (ALAC) based on the effects of the concrete mix parameters. The compressive strength of sixty different concretes with densities ranging from 551 to 1948 kg/m3 was used and trained. The primary mix design variables studied included amount of cement, water, coarse aggregate, fine aggregate, surfactant, the volume percentage of air in the matrix (A/M), and the volume percentage of matrix of the total mix (M/T). The training and testing results indicate that the model explains 0.984 and 0.979 of the variability in compressive strength for the single aggregate used in the study, respectively.

Info:

Periodical:

Edited by:

Aimin Yang, Jingguo Qu and Xilong Qu

Pages:

177-182

DOI:

10.4028/www.scientific.net/AMM.84-85.177

Citation:

Y. J. Kim et al., "Prediction of Compressive Strength of Aerated Lightweight Aggregate Concrete by Artificial Neural Network", Applied Mechanics and Materials, Vols. 84-85, pp. 177-182, 2011

Online since:

August 2011

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Price:

$35.00

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