Effect of Molarity of Sodium Hydroxide on the Strength Behavior of Fiber-Reinforced Geopolymer Concrete Exposed to Elevated Temperature

Ordinary concrete production is highly energy intensive and caused to greenhouse gas emission responsible for global warming. Geopolymer mixtures are the eco-friendly alternative for to protect the CO۲ emission in concrete industry. In this study, the post-fire behavior of fiber reinforced geopolymer concrete (FRGPC) was investigated based on molarity changing approach. To do so, supplementary cementitious materials such as fly ash, metakaolin and zeolite are used to provide binary and ternary FRGPC mixtures. For this aim, FRGPC exposed to elevated temperature at the ۲۰۰, ۵۰۰, ۸۰۰ °C. In addition, three molarity (۱۲, ۱۴, ۱۶) of solution is studied for better strength performance. The result of this study presented that the ratio of the post-fire residual strength of the sample of Z۱۰MK۲۰ increased by ۸.۱% at ۲۰۰ °C, ۱۴.۱% at ۵۰۰ °C, and decreased by ۵.۲% at ۸۰۰ °C. The ۲۸-day sample resistance, with ۲۰% replacement of metakaolin, was measured at ۴۵.۸ MPa after adding fibers (۲% constant volume of ۱-۳% polypropylene fibers). Also, with increasing the molarity of FRGPC mixtures from ۱۲ to ۱۶, the heat resistance behavior in FRGPC had an increase about ۶%. Increasing the volume of polypropylene (PP) fibers up to ۳% by volume did not have much effect on the heat resistance behavior of FRGPC. Beside, post-fire strength of FRGPC was predicted using artificial neural network (ANN) and support vector machines (SVM) with the integration of water cycle algorithm (WCA). Based on the coefficient of determination obtained in the training and testing stages, ANN-WCA model had an acceptable performance in predicting the post-fire residual strength of FRGPC. Additionally, the sensitivity analysis manifested that the molarity of the FRGPC mixtures and the exposed temperature had the greatest effect and PP fibers had the least effect on post-fire residual strength of FRGPC.

کلیدواژه ها:

Strength behavior ، Ternary blended GPC ، Molarity ، Elevated temperature ، Artificial intelligence