New insight into the prediction of strength properties of cementitious mortar containing nano- and micro-silica based on porosity using hybrid artificial intelligence techniques

Nowadays, the accurate prediction of strength properties of cementitious materials containing nano- and micro-silica (NS-MS) remains an open question because of the highly nonlinear function of its constituents on the porosity. In the present study, a combined framework is developed by integrating ant colony optimization (ACO), particle swarm optimization (PSO), and biogeography-based optimization (BBO) with the artificial neural network (ANN) to predict compressive and flexural strengths of cement mortar in two different forms of ignoring (ANN(II)) and considering (ANN(III)) the porosity as an input parameter. This procedure is accomplished considering the porosity effect on the strengths and implementing an experimental program containing 32 mixes (960 specimens) with different NS-MS contents at various ages. Macro- and micro-structural analyses showed that NS-MS caused more decreased pore structure, and thus this situation increases strength properties compared to their separate use. Also, MBBO-MOANN(III) results indicated an improvement in convergence speed and model accuracy compared to other models. This improvement is because of considering the porosity.