Mr. Jae Min Lee | Architectural Engineering | Best Researcher Award

Mr. Jae Min Lee | Ph.D student | Chungbuk National University | South Korea

Academic Background

Jae Min Lee has established a strong academic foundation in Architectural Engineering with a focus on concrete mechanics and computational modeling. His scholarly record reflects measurable research engagement, with Scopus indexing multiple scholarly outputs, Google Scholar citations indicating growing influence, and an h-index demonstrating early-career research impact. His academic journey combines experimental material science and data-driven modeling, positioning him at the intersection of civil engineering and artificial intelligence.

Research Focus

His research centers on predicting and characterizing the behavior of concrete through machine learning and data-informed techniques. He integrates artificial neural networks and physics-informed neural networks to study thermal, mechanical, and moisture-related characteristics in complex concrete systems.

Work Experience

He has contributed to academic research environments through active involvement in laboratory-based investigations and computational analysis. His role includes developing data-driven methodologies for understanding heterogeneous concrete behavior and bridging experimental findings with predictive modeling. He has also participated in collaborative research that links advanced simulations with material characterization, enhancing interdisciplinary insight into structural performance.

Key Contributions

His contributions significantly advance the understanding of thermal and mechanical behavior in large-scale concrete structures. By implementing inverse estimation approaches using neural network frameworks, he has improved the accuracy of predicting internal temperature rise and moisture diffusion in mass concrete. His work introduces efficient methods for quantifying behavioral parameters even when physical observations are limited or affected by noise, reducing experimental dependency. These developments support sustainable and intelligent engineering practices and promote cost-efficient evaluation of material properties through computational innovation.

Awards & Recognition

His academic achievements and growing research influence have led to nomination for the Best Researcher Award. His work has drawn attention for combining civil engineering principles with artificial intelligence to solve emerging challenges in structural materials research.

Professional Roles & Memberships

He is an active member of major technical organizations, including the Korea Concrete Institute and the Korea Institute for Structural Maintenance and Inspection. His involvement reflects commitment to professional development and knowledge dissemination within the concrete engineering community. He also participates in collaborative initiatives involving machine learning applications in material sciences, contributing to interdisciplinary research networks.

Publication Profile

Scopus

Featured Publications

Lee, J. M., & Lee, C. J. Inverse estimation of moisture diffusion model for concrete using artificial neural network.

Lee, J. M., Zhang, W., Lee, D., & Lee, C. Residual strength of concrete subjected to fatigue based on a machine learning technique.

Impact Statement / Vision

His long-term vision is to develop intelligent frameworks that enhance predictive accuracy and reduce experimental burden in concrete engineering. By combining deep learning, physics-based modeling, and structural material science, his work aspires to advance next-generation concrete technologies. He aims to contribute solutions that support sustainability, efficiency, and innovation in civil and structural engineering research.