Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Computers and Concrete Volume 10, Number 6, December 2012 , pages 557-573 DOI: https://doi.org/10.12989/cac.2012.10.6.557 |
|
|
|
Predicting concrete properties using neural networks (NN) with principal component analysis (PCA) technique |
||
B. Boukhatem, S. Kenai, A.T. Hamou, Dj. Ziou and M. Ghrici
|
||
| Abstract | ||
| This paper discusses the combined application of two different techniques, Neural Networks (NN) and Principal Component Analysis (PCA), for improved prediction of concrete properties. The combination of these approaches allowed the development of six neural networks models for predicting slump and compressive strength of concrete with mineral additives such as blast furnace slag, fly ash and silica fume. The Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian regularization was used in all these models. They are produced to implement the complex nonlinear relationship between the inputs and the output of the network. They are also established through the incorporation of a huge experimental database on concrete organized in the form Mix-Property. Thus, the data comprising the concrete mixtures are much correlated to each others. The PCA is proposed for the compression and the elimination of the correlation between these data. After applying the PCA, the uncorrelated data were used to train the six models. The predictive results of these models were compared with the actual experimental trials. The results showed that the elimination of the correlation between the input parameters using PCA improved the predictive generalisation performance models with smaller architectures and dimensionality reduction. This study showed also that using the developed models for numerical investigations on the parameters affecting the properties of concrete is promising. | ||
| Key Words | ||
| neural networks; principal component analysis; correlation; prediction; concrete; additives. | ||
| Address | ||
| B. Boukhatem and S. Kenai: Geometrical Laboratory, Civil Engineering Department, University of Blida, Algeria; A.T. Hamou: Civil Engineering Department, University of Sherbrooke, Canada; Dj. Ziou: Department of Informatics, University of Sherbrooke, Canada; M. Ghrici: Civil Engineering Departments, University of Chlef, Algeria | ||