Machine and deep learning methods for concrete strength Prediction: A bibliometric and content analysis review of research trends and future directions

Abstract

This review paper provides a detailed evaluation of the existing landscape and future trends in applying machine learning and deep learning approaches for predicting concrete strength in construction engineering. The study contextualizes the investigation of machine learning and deep learning in concrete strength prediction, emphasizing the need for precise strength forecasting in construction. This hybrid review uses quantitative analysis of an extensive collection of 1005 research publications from the Scopus database (2010−2023) to identify clusters, hotspots, and gaps in this area, giving a systematic way to analyze the field's dynamics. This review reveals major research clusters such as concrete characteristics, sustainability, error analysis, and optimization. It identifies research hotspots like compressive strength prediction, reinforced concrete, and neural networks. The review illuminates future research paths, ethical concerns, and environmental implications. It emphasizes the relevance of fairness, bias reduction, and sustainability in developing and deploying machine and deep learning models in the construction sector and the necessity for specialized models in forecasting concrete durability, sustainable concrete strength, and shear strength.