Amir Hasanzade-Inallu - Department of Earthquake Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Pouya Hassanzadeh Inallou - Department of Transportation Engineering, Islamic Azad University, Tehran South Branch, Tehran, Iran.
Babak Eskandarinezhad - Department of Civil Engineering, Islamic Azad University of Tabriz, Tabriz, Iran.

Increasing scarcity of natural sand sources around the world and stricter environmental policies in recent years have led researchers to investigate substitute aggregates for natural sand in concrete. A green solution to this problem is the manufactured sand. In this study, we aimed to model the compressive strength of concrete with manufactured sand using an artificial neural network trained using bat algorithm. The comparison of this model with a multiple regression model developed showed the superiority of neural network model. Applying sensitivity analysis techniques, the relative importance of the explanatory variables on compressive strength of concrete with manufactured sand was concluded to be water-to-cement ratio, compressive strength of cement, stone powder content, sand-to-total aggregate ratio, and slump respectively

Concrete with Manufactured Sand (MSC),Artificial Neural Networks (ANNs),Bat Optimization Algorithm,Compressive Strength,