In this paper, two models in gene expression programming (GEP) approach for predicting compressive strength of concretes containing rice husk ash have been developed at the age of 1, 3, 7, 14, 28, 56 and 90 days.
The results showed that ANN can be an alternative approach for predicting the compressive strength of the material.
Overall, the results show that M5P model tree can be a better alternative approach for prediction of the compressive strength of NC and HPC using the amount of constituents of concrete as input parameters.
This article presents a compressive sensing approach for reducing data acquisition time in cardiac cine magnetic resonance imaging (MRI).
This work has shown the excellent capability of artificial neural network approach for the simulation of the compressive properties of closed-cell aluminum foam.
The results showed that ANN can be an alternative approach for the predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives.
Moreover, the proposed MSBTD method is not necessarily the optimal compressive sensing approach for denoising SDOCT images.
Compressive sensing (CS) is an emerging approach for acquisition of sparse or compressible signals.
Nevertheless, in accordance with recent reports that a hierarchical structure outperforms a flat one, this study proposes a hierarchical classification and regression (HCR) approach for improving performance in predicting HPC compressive strength.
