Papers
1
Total Citations
5
H-Index
1
About
Radwa Eissa is a rising scholar at the intersection of construction engineering, data science, and strategic foresight. Her research focuses on harnessing machine learning and social network analysis to map and predict the evolution of knowledge within the construction engineering and management (CEM) domain. In her most-cited work, "Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis" (2024, 5 citations), Eissa pioneers a predictive scientometric approach that moves beyond retrospective impact assessment. By analyzing publication networks and applying ML algorithms, she identifies emerging subdisciplines and anticipates future research trajectories, offering a powerful tool for researchers, funding agencies, and industry leaders to allocate resources strategically. This work marks a significant methodological contribution, bridging traditional bibliometrics with computational trend forecasting. Though early in her career, Eissa’s innovative fusion of engineering management and artificial intelligence positions her as a forward-thinking voice in shaping the next generation of construction research. Her ability to translate complex data into actionable foresight underscores her potential to influence both academic inquiry and practical decision-making in the built environment.
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Top Papers
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