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2025 Vol.2, Issue 3

Research Article

Study on a Predictive Model for Ground Vibrations Following Explosions in Underground Hydrogen Storage Facilities

지하 수소저장시설의 폭발에 따른 지반 진동치 예측 모델 연구

Hyun-Jun Choi1
,
Hyung-Koo Yoon2
,
Gyu-Hyun Go3
,
YoungSeok Kim4*
최 현준1
,
윤 형구2
,
고 규현3
,
김 영석4*
1Senior Researcher, Korean Peninsula Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang, 10223, Rep. of Korea
2Professor, Department of Construction and Disaster Prevention Engineering, Daejeon University, Daejeon, 34520, Rep. of Korea
3Associate Professor, Department of Civil Engineering, Kumoh National Institute of Technology, Gumi, 39177, Rep. of Korea
4Senior Research Fellow, Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang, 10223, Rep. of Korea
1한국건설기술연구원 한반도인프라연구센터 수석연구원
2대전대학교 재난안전공학과 교수
3국립금오공과대학교 토목공학과 부교수
4한국건설기술연구원 지반연구본부 선임연구위원
*Corresponding Author
20 August 2025. pp. 133-144
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Abstract

With the increasing demand for large-scale hydrogen storage facilities, hydrogen energy has emerged as a promising eco-friendly energy source. Underground hydrogen storage facilities offer enhanced safety against external shocks; however, research on the impact of explosions on the surrounding ground and structures remains limited. In this study, the nonlinear finite element analysis (FEA) method was employed to investigate the dynamic behavior of the ground following an explosion in an underground hydrogen storage facility. The Coupled Eulerian-Lagrangian (CEL) and Mohr-Coulomb models were applied to evaluate ground deformation and vibration characteristics under explosion loads. Additionally, parametric analyses were conducted to assess variations in hydrogen concentration, overburden thickness, and ground conditions, and a database (DB) of peak ground vibration values at different separation distances was established. To enhance prediction accuracy, an artificial intelligence (AI)-based deep neural network (DNN) model was developed to predict ground vibrations. Data augmentation and outlier removal techniques were incorporated to optimize model performance. The results demonstrated that increasing the data augmentation factor led to a reduction in training and validation loss, with the lowest test loss and mean squared error (MSE) observed in the dataset augmented by a factor of 100. This study is expected to contribute to the quantitative prediction of ground vibrations caused by underground hydrogen storage facility explosions.

Keywords
Underground facility
hydrogen explosion
ground vibration
numerical model
predictive model

최근 수소 에너지가 친환경 에너지원으로 주목받으며, 대규모 저장시설의 필요성이 증가하고 있다. 특히, 지하 수소저장시설은 외부 충격에 대한 안전성이 높으나, 폭발 발생 시 지반 및 주변 구조물에 미치는 영향에 대한 연구는 부족한 실정이다. 본 연구에서는 비선형 유한요소해석(FEA) 기법을 활용하여 지하 수소저장시설 폭발 시 지반의 동적 거동을 해석하고, Coupled Eulerian-Lagrangian(CEL) 및 Mohr-Coulomb 모델을 적용하여 폭발 하중에 따른 지반 변형과 진동 특성을 평가하였다. 또한, 수소 농도, 토피고, 지반 조건 등의 매개변수 변화를 분석하고, 이격거리별 최대 진동치 데이터베이스(DB)를 구축하였다. 인공지능(AI) 기반 심층 신경망(DNN) 모델을 개발하여 지반 진동을 예측하고, 데이터 증폭 및 이상치 제거 기법을 적용하여 모델 성능을 최적화하였다. 그 결과, 데이터 증폭 배수가 증가할수록 학습 및 검증 손실이 감소하였으며, 특히 100배 증폭된 데이터셋에서 가장 낮은 테스트 손실과 평균제곱오차(MSE)를 기록하였다. 본 연구는 지하 수소저장시설 폭발로 인한 지반 진동을 정량적으로 예측하는 데 기여할 것으로 기대된다.

키워드
지하시설
수소가스 폭발
지반 진동
수치해석 모델
예측 모델
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Information
  • Publisher :Korea Protective Facility Institute
  • Publisher(Ko) :한국방호시설학회
  • Journal Title :Protective Facility
  • Journal Title(Ko) :방호시설
  • Volume : 2
  • No :3
  • Pages :133-144
  • Received Date : 2025-02-05
  • Accepted Date : 2025-03-14
  • DOI :https://doi.org/10.23310/PF.2025.2.3.133
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