Journal of the Computational Structural Engineering Institute of Korea (한국전산구조공학회논문집)

Computational Structural Engineering Institute of Korea (한국전산구조공학회)
권호 표지
DOI QR코드

DOI QR Code

Fire Fragility Analysis of Steel Moment Frame using Machine Learning Algorithms

머신러닝 기법을 활용한 철골 모멘트 골조의 화재 취약도 분석

  • Xingyue Piao (Department of Civil and Environmental Engineering, Hanyang University) ;
  • Robin Eunju Kim (Department of Architecture and Architectural Engineering, Seoul National University)
  • 박성월 (한양대학교 건설환경공학과) ;
  • 김은주 (서울대학교 건축학과)
  • Received : 2023.11.27
  • Accepted : 2023.12.14
  • Published : 2024.02.29
Download PDF
⟨ Previous Next ⟩

Abstract

In a fire-resistant structure, uncertainties arise in factors such as ventilation, material elasticity modulus, yield strength, coefficient of thermal expansion, external forces, and fire location. The ventilation uncertainty affects thefactor contributes to uncertainties in fire temperature, subsequently impacting the structural temperature. These temperatures, combined with material properties, give rise to uncertain structural responses. Given the nonlinear behavior of structures under fire conditions, calculating fire fragility traditionally involves time-consuming Monte Carlo simulations. To address this, recent studies have explored leveraging machine learning algorithms to predict fire fragility, aiming to enhance efficiency while maintaining accuracy. This study focuses on predicting the fire fragility of a steel moment frame building, accounting for uncertainties in fire size, location, and structural material properties. The fragility curve, derived from nonlinear structural behavior under fire, follows a log-normal distribution. The results demonstrate that the proposed method accurately and efficiently predicts fire fragility, showcasing its effectiveness in streamlining the analysis process.

내화 구조물에서는 환기 계수, 재료 탄성 계수, 항복 강도, 열팽창 계수, 외력 및 화재 위치에서 불확실성이 관찰된다. 환기 불확실성은 화재 온도에 영향을 미치고, 이는 다시 구조물 온도에 영향을 미친다. 이러한 온도는 재료 특성과 함께 불확실한 구조적 응답으로 이어지고 있다. 화재 시 구조적 비선형 거동으로 인해 몬테카를로 시뮬레이션을 사용하여 화재 취약성을 계산하는데, 이는 시간이 많이 소요된다. 따라서 머신러닝 알고리즘을 활용해 화재 취약성 분석을 예측함으로써 효율성을 높이고 정확성을 확보하려는 연구가 진행되고 있다. 이 연구에서는 화재 크기, 위치, 구조 재료 특성의 불확실성을 고려하여 철골 모멘트 골조 건물의 화재 취약성을 예측했다. 화재 시 비선형 구조 거동 결과를 기반으로 한 취약성 곡선은 로그 정규 분포를 따른다. 마지막으로 제안한 방법이 화재 취약성을 정확하고 효율적으로 예측할 수 있음을 보여주었다.

Keywords

References

  1. Baker, J.W. (2015) Efficient Analytical Fragility Function Fitting using Dynamic Structural Analysis, Earthq. Spectra, 31(1), pp.579~599.
  2. Chaboki, M., Heshmati, M., Aghakouchak, A.A. (2021) Investigating the behaviour of Steel Framed-Tube and Moment-Resisting Frame Systems Exposed to Fire, Struct. 33, pp. 1802~1818.
  3. Chaudhary, R.K., Roy, T., Matsagar, V. (2020) Framework for Fragility Assessment of Reinforced Concrete Portal Frame Subjected to Elevated Temperature, Struct., 28, pp.2785~2800.
  4. Eurocode 1 (2002) Eurocode 1: Actions on Structures - Part 1-2: General Actions - Actions on Structures Exposed to Fire, EN 1991-1-2. CEN, Brussels.
  5. Eurocode 3 (2005) Eurocode 3: Design of Steel Structures - Part 1-2: General Rules - Structural Fire Design, EN 1993-1-2. CEN, Brussels.
  6. Gernay, T., Khorasani, N.E., Garlock, M. (2019a) Fire Fragility Functions for Steel Frame Buildings: Sensitivity Analysis and Reliability Framework. Fire Technol., 55(4), pp.1175~1210. https://doi.org/10.1007/s10694-018-0764-5
  7. Gernay, T., Khorasani, N.E., Garlock, M. (2016) Fire Fragility Curves for Steel Buildings in a Community Context: A Methodology, Eng. Struct., 113, pp.259~276. https://doi.org/10.1016/j.engstruct.2016.01.043
  8. Gernay, T., Van Coile, R., Khorasani, N.E., Hopkin, D. (2019b) Efficient Uncertainty Quantification Method Applied to Structural Fire Engineering Computations, Eng. Struct., 183, pp.1~17. https://doi.org/10.1016/j.engstruct.2019.01.002
  9. Guo, Q., Jeffers, A.E. (2015) Finite-Element Reliability Analysis of Structures Subjected to Fire, J. Struct. Eng., 141(4), p.04014129.
  10. Heshmati, M., Aghakouchak, A.A. (2020) Collapse Analysis of Regular and Irregular Tall Steel Moment Frames under Fire Loading, Struct. Des. Tall & Spec. Build., 29(3), p.e1696.
  11. Hwang, J.Y., Kwak, H.G. (2015) A Numerical Model of Reinforced Concrete Members Exposed to Fire and After-Cooling Analysis, J. Comput. Struct. Eng. Inst. Korea, 28(1), pp.101~113.
  12. Izzuddin, B.A., Song, L., Elnashai, A.S., Dowling, P.J. (2000) An Integrated Adaptive Environment for Fire and Explosion Analysis of Steel Frames - Part II:: Verification and Application, J. Constr. Steel Res., 53, pp.87~111.
  13. JCSS (2001) JCSS Probabilistic Model Code - Part 2: Load Models, Joint Committee on Structural Safety, ISBN 978-3-909386-79-6.
  14. Jiang, J., Li, G.Q., Usmani, A. (2014) Progressive Collapse Mechanisms of Steel Frames Exposed to Fire, Adv. Structural Eng., 17(3), pp.381~398.
  15. Jiang, B., Li, G.Q., Usmani, A. (2015) Progressive Collapse Mechanisms Investigation of Planar Steel Moment Frames under Localized Fire, J. Constr. Steel Res., 115, pp.160~168. https://doi.org/10.1016/j.jcsr.2015.08.015
  16. Jovanovic, B., Van Coile, R., Hopkin, D., Khorasani, N.E., Lange, D., Gernay, T. (2021) Review of Current Practice in Probabilistic Structural Fire Engineering: Permanent and Live Load Modelling, Fire Technol., 57, pp.1~30. https://doi.org/10.1007/s10694-020-01005-w
  17. Kang, J.W., Kang, M.S., Yoon, H. (2023) Structural Fire Analysis of a Composite Beam Protected by Fire-Resistant Materials, J. Comput. Struct. Eng. Inst. Korea, 36(2), pp.137~145. https://doi.org/10.7734/COSEIK.2023.36.2.137
  18. Khorasani, N.E., Gardoni, P., Garlock, M. (2015) Probabilistic Fire Analysis: Material Models and Evaluation of Steel Structural Members, J. Struct. Eng., 141(12), p.04015050.
  19. Khorasani, N.E., Garlock, M., Gardoni, P. (2014) Fire Load: Survey Data, Recent Standards, and Probabilistic Models for Office Buildings, Eng. Struct., 58, pp.152~165.
  20. Lange, D., Devaney, S., Usmani, A. (2014) An Application of the PEER Performance Based Earthquake Engineering Framework to Structures in Fire, Eng. Struct., 66, pp.100~115.
  21. Memari, M., Mahmoud, H. (2014) Performance of Steel Moment Resisting Frames with RBS Connections under Fire Loading, Eng. Struct., 75, pp.126~138.
  22. Memari, M., Mahmoud, H. (2018) Multi-Resolution Analysis of the SAC Steel Frames with RBS Connections under Fire, Fire Saf. J., 98, pp.90~108.
  23. Ni, S., Gernay, T. (2021) A Framework for Probabilistic Fire Loss Estimation in Concrete Building Structures, Struct. Saf., 88, p.102029.
  24. Qin, C., Mahmoud, H. (2019) Collapse Performance of Composite Steel Frames under Fire, Eng. Struct., 183, pp.662~676.
  25. Qureshi, R., Van Coile, R., Hopkin, D., Thomas, G. (2022) Reliability Assessment of the US Prescriptive Standard for Steel Columns under Fire, Struct., 40, pp.711~724.
  26. Rubert, A., Schaumann, P. (1986) Structural Steel and Plane Frame Assemblies under Fire Action, Fire Saf. J., 10, pp. 173~184.
  27. Shi, K., Guo, Q., Jeffers, A. (2013) Stochastic Analysis of Structures in Fire by Monte Carlo Simulation, J. Struct. Fire Eng., 4(1), pp.37~46. https://doi.org/10.1260/2040-2317.4.1.37
  28. Shrivastava, M., Abu, A., Dhakal, R, Moss, P. (2019) State-of-the-Art of Probabilistic Performance Based Structural Fire Engineering, J. Struct. Fire Eng., 10(2), pp.175~192. https://doi.org/10.1108/JSFE-02-2018-0005
  29. Sun, R., Huang, Z., Burgess, I.W. (2012) Progressive Collapse Analysis of Steel Structures under Fire Conditions, Eng. Struct., 34, pp.400~413.
  30. Vaidogas, E., Juocevicius, V. (2008) Reliability of a Timber Structure Exposed to Fire: Estimation using Fragility Function, J. Mech., 73(5), pp.35~42.
  31. Van Coile, R., Hopkin, D., Khorasani, N.E., Gernay, T. (2021) Demonstrating Adequate Safety for a Concrete Column Exposed to Fire, using Probabilistic Methods, Fire & Mater., 45(7), pp.918~928.
  32. Zhu, Z., Quiel, S.E., Khorasani, N.E. (2023) Bivariate Structural-Fire Fragility Curves for Simple-Span Overpass Bridges with Composite Steel Plate Girders, Struct. Saf., 100, p.102294.