Experimental and machine learning based study of compressive strength of geopolymer concrete

Abstract

In this study, the aim is to investigate and predict the compressive strength of geopolymer concrete (GPC). The effects of curing method, curing time and concrete age on the compressive strength of GPC were evaluated experimentally. Four curing methods, namely room temperature (25°C), mobile dryer (50°C), heating cabinet type 1 (80°C) and heating cabinet type 2 (100°C) were adopted. Additionally, three curing times, of 8 h, 16 h and 24 h, as well as three concrete ages, of 7 days, 14 days and 28 days, were considered. To predict the compressive strength of GPC, 679 test results were collected to develop various machine learning models. The test results indicated that increasing the curing temperature, curing time and concrete age all led to improvements in the compressive strength of GPC. The mobile dryer showed promise as a curing method for cast-in-place GPC. The proposed machine learning models demonstrated good predictive capacity for the compressive strength of GPC with relatively high accuracy. Through sensitivity analysis, concrete age was identified as the most influential variable affecting the final compressive strength of GPC.