Optimized intelligent predictive system for enhancing mechanical properties of geopolymer concrete under elevated temperatures

Green buildings are built using concrete made with by-products obtained from industries. They include Fly ash attained as by-product from coal power plants and GGBS from iron furnaces. For obtaining high concrete strength in green buildings, the type of concrete preferred is Geopolymer Concrete (GPC). In GPC these by-product pozzolanics plays a crucial role along with the alkali activators. In this research, GPC is made with fly ash, GGBS, fine aggregate, coarse aggregate, sodium silicate, and sodium hydroxide. 1:1.5:3 is the mix ratio of GPC in terms of binder, fine aggregate and coarse aggregate. For 28 days the GPC samples are cured in ambient temperature. The compressive strength, flexural strength, and split tensile strength of the sample are then experimentally determined. The experimental results in the form of temperature vs. strength are plotted graphically, then using curve fitting technique the equations are generated in the system. These equations acts as fitness function in the novel Ant lion Boltzmann Predictive System (ABPS) to determine the strength parameters of GPC with considering to the elevated temperature. The elevated temperature is considered as input in the system, for the particular elevated temperature the residual strength in percentage left in the GPC is predicted. The reference strength and temperature considered in the novel technique is 100% strength at 25 degrees C. The compression strength left in the GPC was 41.77%, flexural strength left was 37.9%, and split tensile strength left was 29.89%, which is quite better than the compared models.