A novel framework for strength prediction of geopolymer mortar: Renovative precursor effect

Abstract

Concrete is the most used building material today as it has many advantages due to its structure. Geopolymer composites could potentially replace concrete in the future due to the demands of the construction industry. The studies related to the geopolymers have challenges due to the lack of a linear relationship between compressive strength (CS) and flexural strength (FS) caused by factors such as a binder, mixing parameters, limited available data, and time-consuming trial and error methods. Novel prediction models need to be developed as the reliability of the prediction model currently being used requires improvement. This study attempts to develop an optimum prediction technique for compressive strength and flexural strength of geopolymer by using Machine Learning (ML) algorithms to meet the mentioned research need. For this purpose, a new database was created with mechanical properties (156 (CS) and (FS) obtained from geopolymer mortar samples with a comprehensive laboratory process using materials such as obsidian (OB), Ground granulated blast furnace slag (GGBS), and metakaolin (MT) as binders. Five machine-learning algorithms were applied to the dataset. Feature statistics and two different feature ranking methods were also performed to regulate each ML algorithm. Among the algorithms, the best R square performance results were given by Random Forest as a value of 0.983. In the sensitivity analysis that examined the effects of inputs on outputs, obsidian was shown to be the most important input.