Fire Fragility Analysis of Steel Moment Frame using Machine Learning Algorithms

Xingyue Piao; Robin Eunju Kim

doi:10.7734/COSEIK.2024.37.1.57

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2024 Vol.37, Issue 1 Preview Page Next Page

Research Paper

Fire Fragility Analysis of Steel Moment Frame using Machine Learning Algorithms 머신러닝 기법을 활용한 철골 모멘트 골조의 화재 취약도 분석

29 February 2024. pp. 57-65

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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.

Keywords

steel moment frame

fire hazard

fragility analysis

machine learning algorithm

decision tree

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.

키워드

철골 모멘트 골조

화재재난

취약도 분석

머신러닝 기법

결정 트리

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Information

Publisher :Computational Structural Engineering Institute of Korea
Publisher(Ko) :한국전산구조공학회
Journal Title :Journal of the Computational Structural Engineering Institute of Korea
Journal Title(Ko) :한국전산구조공학회 논문집
Volume : 37
No :1
Pages :57-65
Received Date : 2023-11-27
Revised Date : 2023-12-13
Accepted Date : 2023-12-14
DOI :https://doi.org/10.7734/COSEIK.2024.37.1.57

Journal of the Computational Structural Engineering Institute of Korea ISSN:1229-3059(Print) 2287-2302(Online) 한국전산구조공학회 논문집

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