Sang Min Lee

Ph.D. Candidate in Artificial Intelligence
Seoul National University

201612445@snu.ac.kr
concrete-sangminlee.github.io
Google Scholar
Sang Min Lee
ORCID
0000-0002-6822-5252
GitHub
github.com/concrete-sangminlee

Education

  1. 2023–2027 (expected)

    Ph.D. in Artificial Intelligence

    Interdisciplinary Program in Artificial Intelligence, Seoul National University

    • Research: AI for resilient infrastructure and wind engineering
    • Advisor: Prof. Thomas H.-K. Kang
  2. 2021–2023

    M.S. in Structural Engineering

    Department of Architecture and Architectural Engineering, Seoul National University

    • Thesis: Machine Learning Application to Automated Impact-Echo Test for Concrete Structures
    • Advisor: Prof. Thomas H.-K. Kang
  3. 2016–2021

    B.S. in Architectural Engineering

    Department of Architecture and Architectural Engineering, Seoul National University

Research and Professional Experience

  1. Mar 2021 – present

    Graduate Research Assistant

    Hi-performance Structural Engineering Lab, Seoul National University

    • Advisor: Prof. Thomas H.-K. Kang
  2. Oct–Nov 2025

    Visiting Researcher

    National Weather Center, University of Oklahoma, Norman, OK, USA

    • Hydrometeorology and Remote Sensing Lab, hosted by Prof. Yang Hong

Selected Publications6 journal articles · 20 conference papers

Journal Articles

  1. J6Lee, S. M., Hong, J., Choi, H., & Kang, T. H.-K. (2025). Machine learning assisted method for automated impact-echo testing of concrete structures. Journal of Nondestructive Evaluation, 44(4), 121. SCIE doi:10.1007/s10921-025-01260-3
  2. J5Lee, S. M., Choi, H. S., Kim, C., & Kang, T. H.-K. (2025). Lightweight alternative machine learning model for automating concrete crack image classification. ACI Structural Journal, 122(5). SCIE
  3. J4Lee, Y. I., Lee, S. M., & Kang, T. H.-K. (2025). Multiclass deep support vector data description for structural health monitoring. Journal of Structural Engineering, 151(12). SCIE doi:10.1061/JSENDH.STENG-14823
  4. J3Ahn, B., Lee, D., Lee, S. M., & Kang, T. H.-K. (2023). A methodology for predicting expected wind speed from satellite images using artificial neural networks (in Korean). Journal of the Wind Engineering Institute of Korea, 27(4), 135–142. KCI
  5. J2Lee, S. M., Choi, H., & Kang, T. H.-K. (2023). Machine learning applications for smart impact-echo testing (in Korean). Review of Architecture and Building Science (Architectural Institute of Korea), 67(3), 47–50.
  6. J1Lee, S. M., Sung, H. S., & Kang, T. H.-K. (2022). Comparison of performance for predicting compressive strength of concrete using machine learning. Journal of the Korea Concrete Institute, 34(5), 505–513. KCI

International Conference Papers

  1. C8Lee, S. M., & Kang, T. H.-K. (2026). CoMuRAG: Condition-mutated hard negatives for safety-sensitive Korean building-code retrieval. ACM/IEEE Joint Conference on Digital Libraries (JCDL 2026).
  2. C7Park, H. G., Lee, S. M., & Kang, T. H.-K. (2026). Modified impact-echo testing of liner-plate-backed concrete in nuclear containment buildings. ACEM26 / Structures26.
  3. C6Lee, S. M., & Kang, T. H.-K. (2025). Retrieval augmented generation for assistance of using wind load design codes. The 10th Asia-Pacific Conference on Wind Engineering (APCWE10).
  4. C5Lee, S. M., Lee, Y. I., & Kang, T. H.-K. (2024). Multi-sphere deep SVDD for system identification of building structure. The 2024 World Congress on Advances in Civil, Environmental, and Materials Research (ACEM24).
  5. C4Lee, S. M., Choi, H. S., Kim, C., & Kang, T. H.-K. (2024). Representation learning for classification of concrete crack images. The 2024 Structures Congress, Seoul, Korea.
  6. C3Lee, S. M., & Kang, T. H.-K. (2022). Clustering wind pressure tap using dynamic time warping. 8th European-African Conference on Wind Engineering (EACWE2022), Bucharest, Romania.
  7. C2Lee, S. M., & Kang, T. H.-K. (2022). Shear strength prediction of prestressed concrete based on machine learning. The 2022 Structures Congress, Seoul, Korea.
  8. C1Lee, S. M., & Kang, T. H.-K. (2021). Prediction of wind pressure coefficients on high-rise building façade using LSTM RNN model for sensor reduction. The 2021 World Congress on Advances in Structural Engineering and Mechanics (ASEM21).

Also 12 papers at Korean domestic conferences and 3 theses. Full CV: concrete-sangminlee.github.io/cv/

Patents

  1. 2023

    AI-based defect detection system inside concrete members

    Lee, S. M., & Kang, T. H.-K. Korean Patent 10-2611457, registered

  2. 2022

    Wind load estimation system based on artificial intelligence

    Lee, S. M., & Kang, T. H.-K. Korean Patent 10-2480382, registered

Teaching Experience

  1. 2021–2024

    Teaching assistant for 8 courses, Seoul National University

Honors and Awards, Certifications

  1. 2025–2026

    SNU AI Fellowship, Interdisciplinary Program in Artificial Intelligence, SNU

  2. 2025

    Excellence Award, SNU AI Paper Competition, Interdisciplinary Program in Artificial Intelligence, SNU

  3. 2023–2024

    SNU AI Graduate School Scholarship, Interdisciplinary Program in Artificial Intelligence, SNU

  4. 2022

    Outstanding Teaching Assistant Award, College of Engineering, SNU

  5. 2022

    Outstanding Presentation Award, Korea Concrete Institute

  6. 2025

    Big Data Analysis Engineer, Korea Data Agency

Professional Service

  1. 2024–present

    Reviewer, Wind and Structures

  2. 2025

    Organizing committee member, 28th WEIK Annual Conference

  3. Memberships

    KCI, ACI, AIK, KSMI, WEIK, ASCE

Skills

Programming: Python, MATLAB, C · Embedded and IoT: Arduino UNO, ESP32, Teensy, STM boards · Engineering tools: ECO2, AutoCAD, Microsoft Excel