Tuned Mass Dampers (TMDs) are widely used to mitigate structural vibrations in buildings and bridges. However, conventional optimization methods often struggle to achieve optimal performance due to the complexity of structural dynamics. This study proposes the NN-L-BFGS-B algorithm, which combines Artificial Neural Networks (ANNs) for global exploration and L-BFGS-B for local exploitation to efficiently optimize TMD parameters. A ten-story shear-building model with a TMD is used for validation. The proposed method achieves the lowest H₂ norm compared to previous studies, demonstrating improved optimization performance. Additionally, NN-L-BFGS-B effectively balances computational efficiency and accuracy, making it adaptable to various engineering optimization problems.

Abstract
1. 서론
2. NN-L-BFGS-B 알고리즘
    2.1 Step 1. 신경망을 활용한 초기 최적화
    2.2 Step 2. L-BFGS-B 기반 최적화
3. 수치 모델
4. 결과 비교 및 분석
5. 결론
감사의 글
References

• 이승재(한국기술교육대학교 디자인·건축공학부, 교수) | Lee Seung-Jae (School of Industrial Design & Architectural Engineering, KOREATECH)
• 김승구(한국기술교육대학교 디자인·건축공학부, 연구원) | Kim Seung-Goo (School of Industrial Design & Architectural Engineering, KOREATECH)
• 이돈우(한국기술교육대학교 디자인·건축공 학부, 박사후연구원) | Lee Don-Woo (School of Industrial Design & Architectural Engineering, KOREATECH) Corresponding author