A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete

The construction industry's growing emphasis on sustainability has driven the development of eco-friendly concrete alternatives, such as self-compacting concrete (SCC) and geopolymer concrete (GPC). These materials, incorporating diverse ingredients like fly ash, silica fumes, and recycled aggregates, aim to reduce the environmental impact of traditional concrete production. However, the complex behavior of these materials poses challenges in accurately predicting their properties. Machine learning (ML) and deep learning (DL) techniques offer promising solutions to this problem. This systematic literature review analyzes the application of ML and DL methods for predicting the properties of sustainable concrete, including SCC, GPC, and other variations incorporating recycled aggregates, supplementary cementitious materials, and high-performance concrete. The review encompasses linear models, support vector machines (SVM), k-nearest neighbors (KNN), bagging, boosting, artificial neural networks (ANN), evolutionary algorithms, and nature-inspired algorithms. The analysis reveals a dominance of ANNs in predicting GPC properties, while bagging, boosting, and ANNs perform well for SCC (60 %). For other sustainable concrete types, boosting methods are prominent. The review highlights the potential of AI to optimize concrete mix designs, enhance sustainability, and reduce reliance on costly and time-consuming experimental testing. Additionally, it identifies key challenges and knowledge gaps, paving the way for future research in this rapidly evolving field.