Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4913119 | Construction and Building Materials | 2017 | 12 Pages |
Abstract
Many researchers are interested in predicting the concrete compressive strength, resulting in quite a few linear and nonlinear regression equations. Alternatively, other models have been developed to produce more sophisticated systems by applying soft computing techniques, the majority of which have rarely been used beyond classic problems, such as function optimization or approximation by genetic algorithms (GAs), or neural networks (NNs). Our study proposes an evolutionary structure with a more complex NN in order to achieve the full potential of these techniques, which the genetics of neural systems promises to do. It consists of integrating a GA to optimize the connection weights for each neuron of an NN developed previously. The idea behind this combination is to develop an NNGA model prediction of the compressive strength of concrete containing natural pozzolan. Model learning and testing were first performed based on the back-propagation algorithm. Then, the model was optimized using the proposed evolutionary structure based upon GA. More than 400 experimental data collected from past studies were used in building this model. The hybrid NNGA model was compared with NN model using the same architecture, show that the NNGA is more performant and better than NN alone. The proposed hybrid model was also experimentally validated, very acceptable results with a high correlation coefficient R2 equal to 0.93, yielding comparable results to those obtained by the ACI 209-08 and CEB-FIP models with R2 values equal to 0.95 and 0.96, respectively. However, it can help to predict the compressive strength of a specified concrete mix at any age without knowing in prior the 28Â days' compressive strength of this given concrete as it is the case in ACI 208-09 and CEB-FIB Codes. The main feature of this system is its flexibility to reduce significantly the scale of the experiment using a system graphical user interface.
Related Topics
Physical Sciences and Engineering
Engineering
Civil and Structural Engineering
Authors
Redouane Rebouh, Bakhta Boukhatem, Mohamed Ghrici, Arezki Tagnit-Hamou,