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

Abstract

This study aims 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 (25oC), mobile dryer (50oC), heating cabinet type 1 (80oC), and heating cabinet type 2 (100oC) were adopted. Additionally, three curing times of 8h, 16h and 24h, 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 the 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, the concrete age was identified as the most influential variable affecting the final compressive strength of GPC.