Journal of the Korea Institute of Building Construction (한국건축시공학회지)

The Korean Institute of Building Construction (한국건축시공학회)
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A Foundational Study on Deep Learning for Assessing Building Damage Due to Natural Disasters

자연재해로 인한 건물의 피해 평가를 위한 딥러닝 기초 연구

  • Kim, Ji-Myong (Department of Architectural Engineering, Mokpo National University) ;
  • Yun, Gyeong-Cheol (Department of Railway Management, Songwon University)
  • 김지명 ;
  • 윤경철
  • Received : 2024.04.17
  • Accepted : 2024.05.21
  • Published : 2024.06.20
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Abstract

The escalating frequency and intensity of natural disasters and extreme weather events due to climate change have caused increasingly severe damage to societal infrastructure and buildings. Government agencies and private companies are actively working to evaluate these damages, but existing technologies and methodologies often fall short of meeting the practical demands for accurate assessment and prediction. This study proposes a novel approach to assess building damage resulting from natural disasters, focusing on typhoons-one of the most devastating natural hazards experienced in the country. The methodology leverages deep learning algorithms to evaluate typhoon-related damage, providing a comprehensive framework for assessment. The framework and outcomes of this research can provide foundational data for the evaluation of natural disaster-induced damage over the entire life cycle of buildings and can be applied in various other industries and research areas for assessing risk of damage.

기후 변화에 따른 자연재해와 이상기상의 빈도 및 심도가 날로 증가하면서, 사회기반시설과 건축물에 미치는 영향도 점차 커지고 있다. 이러한 문제에 대응하기 위해 다양한 정부 기관과 민간 부문에서는 이로인한 피해를 정확히 평가하려는 노력을 기울이고 있지만, 현실에 부합하는 정밀한 피해 예측과 평가는 여전히 도전적인 과제로 남아 있으며, 현재의 기술 수준으로는 부족함이 많다. 이러한 배경 하에, 본 연구는 우리나라에서 발생하는 주요 자연재해 중 하나인 태풍에 의한 건축물 피해를 분석하여, 해당 피해를 정확히 평가할 수 있는 방법론을 제시하고자 한다. 딥러닝 알고리즘을 활용한 평가 방식과 프레임워크를 도입하여, 태풍으로 인한 건물 피해 평가에 관한 연구를 진행하였다. 본 연구의 결과는 건물의 수명주기 전반에 걸친 자연재해 피해 평가에 필요한 기본 데이터를 제공하고, 다양한 산업 및 연구 영역에서 위험 평가에 활용이 가능하다.

Keywords

Acknowledgement

This research was funded by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2022R1F1A106314113) and supported by research fund from Songwon University2024(A2024-20).

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