The Role of Steel Slag Powder in Enhancing Concrete Strength: A Bibliometric and AI-Based Analysis of Research Trends | ||||
| International Journal of Engineering & Artificial Intelligence Art Design | ||||
| Volume 1, Issue 1, August 2025, Page 23-40 PDF (623.92 K) | ||||
| Document Type: Review article | ||||
| DOI: 10.21608/ijeaid.2025.396422.1001 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ahmed M. Gomaa   1; Kamal Hafez2; Mohamed Shaita3; Mohamed Ragb3; Armia Adel Said3; Keroles Elkes3; Abdalla Nasif3; Khaled Samy3
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| 1Assistant Professor at Department of Construction and Building Engineering, Faculty of Engineering and Technology, Egyptian Chinese University, Cairo, Egypt | ||||
| 2Department of Civil Engineering, Faculty of Engineering, Suez Canal University, Ismailia, Egypt | ||||
| 3Department of Civil Engineering, The Higher Institute of Engineering and Technology Fifth Settlement, Egypt | ||||
| Abstract | ||||
| Steel slag powder (SSP) is gaining attention as an effective supplementary cementitious material in concrete, offering improved mechanical performance and promoting sustainable construction. This study conducts a bibliometric analysis of global research on SSP in concrete, with a specific focus on its role in enhancing compressive strength. A total of 836 articles published between 2000 and April 2025 were analyzed using VOSviewer and related tools to map key research patterns, including co-authorship networks, keyword co-occurrence, and institutional productivity. The analysis highlights the primary applications of SSP, notably in cement replacement, durability improvement, and environmental impact reduction. Influential countries, institutions, and authors are identified, alongside trending themes such as green concrete, industrial waste utilization, and microstructural engineering. Beyond bibliometric mapping, the study incorporates artificial intelligence (AI) tools especially machine learning (ML) and artificial neural networks (ANNs)which are increasingly employed to model and predict the performance of SSP-based concrete. These AI approaches aid in mixture optimization, strength prediction, and long-term durability assessments. Key challenges include the lack of standardized practices, variability in slag composition, and the scalability of SSP in large-scale applications. The study also suggests future directions such as multi-objective optimization, AI-based mix design systems, and enhanced interdisciplinary research. By offering a data-driven overview of current trends and technological integration, this research provides valuable insights for engineers, academics, and policymakers, highlighting the synergy between SSP innovation and AI in advancing sustainable concrete technologies | ||||
| Keywords | ||||
| Steel Slag Powder; Compressive Strength; Machine Learning Applications; Bibliometric Analysis; Sustainable Concrete | ||||
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