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Indian Journal of Engineering and Materials Sciences (IJEMS) >
IJEMS Vol.17 [2010] >
IJEMS Vol.17(3) [June 2010] >
| Title: | Modeling slump of concrete using the group method data handling algorithm |
| Authors: | Chen, Li |
| Keywords: | Group method data handling
Slump
High-performance concrete
Regression analysis |
| Issue Date: | Jun-2010 |
| Publisher: | CSIR |
| Abstract: | This paper proposes the group method data handling
(GMDH) algorithm and applies it to estimate the slump of high-performance concrete
(HPC). It is known that HPC is a highly complex material whose behaviour
is difficult to model,
especially for slump. To estimate the
slump, it is a nonlinear
function of the content of all concrete ingredients, including
cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and
fine aggregate.
Therefore, slump estimation is
set as a function of the content of these seven
concrete ingredients
and additional four important ratios. The GMDH algorithm presented in
this paper has the advantage of a heuristic self-organized and gradually complicated model for the complicated multi-variable
HPC slump estimation.
The model establishes the input-output relationship of a complex system using a
multilayered perception-type structure that is similar to a feed-forward
multilayer artificial neural network (ANN), but it expresses relationships using more explicit functions than
ANN. Moreover, the GMDH has the ability to select significant variables and
combine them properly and automatically. The results show that GMDH obtains a more accurate
mathematical equation through
learning procedures which outperforms the traditional multiple linear regression
analysis (RA) and ANN, with lower estimating errors for predicting the HPC
slump.
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| Page(s): | 179-185 |
| ISSN: | 0975-1017 (Online); 0971-4588 (Print) |
| Source: | IJEMS Vol.17(3) [June 2010]
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