Machine Learning-Driven Insights for Concrete Compressive Strength Prediction: A bibliometric Analysis | ||||
| International Journal of Engineering & Artificial Intelligence Art Design | ||||
| Volume 1, Issue 1, August 2025, Page 1-22 PDF (682.99 K) | ||||
| Document Type: Review article | ||||
| DOI: 10.21608/ijeaid.2025.396403.1000 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ahmed M. Gomaa   1; Khaled Samy2; Marina O. Karam2; Youssef A. Abd Elsatar2; Mohamed Z. Mohamed2; Mohamed S. Elnemr2; Mohab W. Mosbah2; Mohamed E. Mosbah2; Kamal Hafez3
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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, The Higher Institute of Engineering and Technology Fifth Settlement, Egypt | ||||
| 3Department of Civil Engineering, Faculty of Engineering, Suez Canal University, Ismailia, Egypt | ||||
| Abstract | ||||
| Predicting concrete's compressive strength through in-depth analysis is essential for improving safety in practical applications and optimizing construction processes. Numerous studies have explored methods for forecasting the mechanical properties of concrete, particularly its compressive strength. Summarizing key insights from these studies can help guide future research directions. This paper conducts a bibliometric analysis of research utilizing machine learning (ML) algorithms for predicting concrete's compressive strength. It assesses the effectiveness of these models and offers insights into developing more efficient solutions. Additionally, it provides researchers with a comprehensive overview of key themes, emerging trends, and research gaps in this domain. To accomplish this, 1,805 articles published between 2014 and March 8, 2025, were retrieved from the Scopus Database and analyzed using VOSviewer software. The findings highlight the widespread application of ML models for this purpose, evaluating their advantages and limitations, particularly in managing complex datasets. By offering a detailed assessment of ML techniques and their practical implications, this study distinguishes itself from previous research. A major contribution of this study is its identification of leading institutions, influential authors, key countries, and major publication sources in this field. It integrates data to highlight crucial research areas, gaps, and evolving trends. Ultimately, the study establishes a solid foundation for advancing ML-driven, reliable, and sustainable structural systems in civil engineering, construction materials, and the concrete industry | ||||
| Keywords | ||||
| Concrete Strength Prediction; Machine Learning (ML) Applications; Bibliometric Study; Concrete Mechanical Characteristics; Forecasting Models | ||||
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