Compressive strength prediction for concrete modified with nanomaterials

The evolution of nanotechnology introduced novel materials that can be used to enhance the mechanical behaviour of concrete and other materials. The enhancement ratio depends on the type and percentage of the nanomaterial. A prediction model that can estimate the compressive strength of concrete made with nanomaterials is still lacking. Such models are considerably needed for the design and analysis of reinforced concrete structures made with nanomaterials. Gene expression programming (GEP) was used in this research to develop a prediction model that can estimate the compressive strength of concrete modified with carbon nanotubes (CNTs), nanosilica (NS), nano clay (NC), and nano aluminum (NA). A total of 94 data points were collected from several tests found in the literature to develop the GEP model. Two GEP models were developed where the first one neglects the effect of NC and NA while the second GEP model considers their effect. The models were then verified using statistical evaluation. The GEP models have high R2 values of 94 % and 92.5 % and low mean absolute error of 4.6 % and 2.9 % of all data for the first and second GEP model, respectively. A parametric study is then performed to further validate the GEP models by investigating the sensitivity of their parameters to the compressive strength of nano concrete. The trends of the GEP model are in agreement with the overall trends of the experimental results available in the literature. The compressive strength of concrete predicted using the GEP model increases with the addition of CNTs, NS, and NC, while it decreases with NA. This confirms the accuracy of the GEP models.