Paper Title:
Prediction of Compressive Strength of Aerated Lightweight Aggregate Concrete by Artificial Neural Network
  Abstract

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
Chapter
Dynamics, Vibration and Time-Dependent Deformation
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, J. Hu, S. J. Lee, B. J. Broughton, "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
$32.00
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