Dr. Vasso Papadimitriou | Cost Estimation Models | Best Researcher Award
Researcher | Aristotle University of Thessaloniki | Greece
Dr. Vasso Papadimitriou is a forward-thinking researcher specializing in artificial intelligence applications in construction engineering, with a focus on cost estimation, digital construction, and sustainable project management. Her research bridges engineering design, machine learning, and construction technology, developing intelligent tools to enhance decision-making accuracy in building renovation and infrastructure projects.
Affiliated with the Aristotle University of Thessaloniki and the University of Macedonia, Dr. Papadimitriou’s work contributes to advancing AI-driven predictive models that improve efficiency and reduce uncertainty in project budgeting and execution. She applies methods such as Artificial Neural Networks (ANNs), Radial Basis Functions (RBFs), and TOPSIS to optimize renovation cost forecasting, aligning with the United Nations Sustainable Development Goals (SDG 9 and SDG 17).
Her research has been published in high-impact journals indexed in Scopus, Web of Science (SCI-Expanded, ESCI), and Google Scholar, with growing recognition for her innovative use of computational models in sustainable engineering and design. Through her comparative analyses and model development, Dr. Papadimitriou contributes to the digital transformation of the construction industry, shaping the future of AI-powered engineering solutions.
Profiles: Scopus | Orcid | Google Scholar | Web of Science
Featured Publications
Papadimitriou, V. E., & Aretoulis, G. N. (2024). A final cost estimating model for building renovation projects. Buildings, 14(4), 1072. Citation count: 8.
Papadimitriou, V. E., Aretoulis, G. N., & Papathanasiou, J. (2024). Radial Basis Function (RBF) and Multilayer Perceptron (MLP) comparative analysis on building renovation cost estimation: The case of Greece. Algorithms, 17(9), 390. Citation count: 4.
Papadimitriou, V. E., & Aretoulis, G. N. (2025). An innovative approach regarding efficient and expedited early building renovation cost estimation utilizing ANNs and the TOPSIS methodology. Algorithms, 18(11), 696. Citation count: —.
Papadimitriou, V., & Aretoulis, G. (2023). Neural network models as a cost prediction tool to prevent building construction projects from a failure—A literature review. Proceedings of the Erasmus+ PROSPER Project International Scientific Conference. Citation count: 1.