Critical review on the application of artificial intelligence techniques in the production of geopolymer-concrete

Abstract

AbstractThe need to employ technology that replaces traditional engineering methods which generate gases that worsen our environment has emerged in an era of dwindling ecosystem owing to global warming has a negative influence on the earth system’s ozone layer. In this study, the exact method of using artificial intelligence (AI) approaches in sustainable structural materials optimization was investigated to ensure that concrete construction projects for buildings have no negative environmental effects. Since they are used in the forecasting/predicting of an agro-waste-based green geopolymer concrete system, the intelligent learning algorithms of Fuzzy Logic, ANFIS, ANN, GEP and other nature-inspired algorithms were reviewed. A systematic literature search was conducted to identify relevant studies published in various databases. The included studies were critically reviewed to analyze the types of AI techniques used, the research methodologies employed, and the main findings reported. To meticulously sort the crucial components of aluminosilicate precursors and alkaline activators blend and to optimize its engineering behavior, laboratory methods must be carried out through the mixture experiment design and raw materials selection. Such experimental activities often fall short of the standards set by civil engineering design guidelines for sustainable construction purposes. At some instances, specific shortcomings in the design of experiments or human error may degrade measurement correctness and cause unforeseen discharge of pollutants. Most errors in repetitive experimental tests have been eliminated by using adaptive AI learning techniques. Though, as an extensive guideline for upcoming investigators in this cutting-edge and developing field of AI, the pertinent smart intelligent modelling tools used at various times, under varying experimental testing methodologies, and leveraging different source materials were addressed in this study review. The findings of this review study demonstrate the benefits, challenges and growing interest in utilizing AI techniques for optimizing geopolymer-concrete production. The review identified a range of AI techniques, including machine learning algorithms, optimization models, and performance evaluation measures. These techniques were used to optimize various aspects of geopolymer-concrete production, such as mix design, curing conditions, and material selection.