The accuracy of soft computing techniques was used in this research to estimate the unconfined compressive strength according to series of unconfined compressive tests for multiple mixtures of cockle shell, cement and sand under different... more
The accuracy of soft computing techniques was used in this research to estimate the unconfined compressive strength according to series of unconfined compressive tests for multiple mixtures of cockle shell, cement and sand under different curing periods. We developed a process for simulating the unconfined compressive strength through two techniques of soft computing, the support vector regression (SVR) and the adaptive neuro-fuzzy inference (ANFIS). The developed SVR and ANFIS networks have one neuron (UCS) in the output layer and four neurons in the input layer. The inputs were percentage of cockle shell, cement and sand content in the mixtures, and age (in days). First, the ANFIS network was used to select the most effective parameters on the UCS. The linear, polynomial, and radial basis functions were employed as the SVR's kernel function. The simulation results proved the performance of proposed optimizers. Additionally, the results of SVR and ANFIS were compared through the Pearson correlation coefficient and the root-mean-square error. The findings show that the predictive accuracy and capability of generalization can be an improved by the ANFIS approach in comparison to the SVR estimation. The simulation results confirmed the effectiveness of the proposed optimization strategies. (C)2015 Elsevier Ltd. All rights reserved.
