Machine Learning Applications in Sustainable Construction Materials: A Scientometrics Review of Global Trends, Themes, and Future Directions

Ephrem Melaku Getachew, Woubishet Zewdu Taffese (), Leonardo Espinosa-Leal and Mitiku Damtie Yehualaw
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Ephrem Melaku Getachew: School of Civil, Water Resource Engineering and Architecture, College of Engineering, Kombolcha Institute of Technology, Wollo University, Kombolcha P.O. Box 208, Ethiopia
Woubishet Zewdu Taffese: Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA
Leonardo Espinosa-Leal: Graduate School and Research, Arcada University of Applied Sciences, 00560 Helsinki, Finland
Mitiku Damtie Yehualaw: Faculty of Civil and Water Resource Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar P.O. Box 26, Ethiopia

Sustainability, 2025, vol. 17, issue 18, 1-34

Abstract: The integration of machine learning (ML) into sustainable construction materials research, particularly focusing on construction and demolition waste (CDW), has accelerated in recent years, driven by the dual need for digital innovation and environmental responsibility. This study presents a comprehensive scientometric analysis of the global research landscape on ML applications for predicting the performance of sustainable construction materials. A total of 542 publications (2007–2025) were retrieved from Scopus and analyzed using VOSviewer (V1.6.20) and Biblioshiny (Bibliometrix R-package, V5.1.1) to map publication trends, leading sources, key authors, keyword co-occurrence, and emerging thematic clusters. The results reveal a sharp rise in publications after 2018, peaking in 2024, in parallel with the growing global emphasis on the circular economy and the UN Sustainable Development Goals. Leading journals such as Construction and Building Materials , the Journal of Building Engineering , and Materials have emerged as key publication venues. Keyword analysis identified core research areas, including compressive strength prediction, recycled aggregates, and ML algorithm development, with recent trends showing increasing use of ensemble and deep learning methods. The findings highlight three thematic pillars—Performance Characterization, Algorithmic Modeling, and Sustainability Practices—underscoring the interdisciplinary nature of the field. This study also highlights regional disparities in research output and collaboration, underscoring the need for more inclusive and diverse global partnerships. Overall, this study provides a comprehensive and insightful view of the rapidly evolving ML-CDW research landscape, offering valuable guidance for researchers, practitioners, and policymakers in advancing data-driven, sustainable solutions for the future of construction.

Keywords: machine learning; scientometric analysis; bibliometric mapping; artificial intelligence; sustainability; predictive modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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