Exploratory literature review and scientometric analysis of artificial intelligence applied to geopolymeric materials

Abstract

The goal of this work is to present and analyze studies that link artificial intelligence (AI) and geopolymer composites through an exploratory literature review. The systematic search comprised articles in the Scopus database, from 2000 to 2023. The results from 48 articles show that AI has been applied mainly to predict compressive strength and slump. Comparing the AI techniques to their R², no clear trend was observed, i.e., there was no overall best algorithm; and their performance was not directly related to database size. Still, Random Forest obtained superior results in articles that compared multiple techniques. Some of the main gaps identified are the lack of studies with geopolymers synthesized in acidic environments or with waste-based or nanomaterials; and that AI has not been largely applied to properties besides compressive strength (e.g., fire resistance, durability, porosity, and thermal properties). Regarding transparency, authors have seldom publicized their codes, impairing proper review of the models. In conclusion, if properly and ethically adopted, AI is a promising tool for the development of geopolymer composites, which can optimize time and employee resources; and many applications remain unexplored.