확률론적 접근을 통한 터널 위험도 평가 모델
Tunnel risk assessment model using a probabilistic approach
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초록
본 연구에서는 터널 붕괴 위험도를 체계적으로 평가하고 관리할 수 있는 새로운 평가 모델을 제안 하였다. 터널 공사는 도로, 철도, 지하철 등의 인프라 구축에 필수적이지만, 붕괴 위험 관리가 중요하다. 이를 위해 14개의 주요 영향 인자를 선정하고, AHP (analytichierarchy process) 기법을 활용하여 각 인자의 상대적 중요도를 평가하고 가중치를 산정하였다. 몬테카를로 시뮬레이션(Monte Carlo method)을 통해 각 인자의 확률분포를 반영한 위험도 평가를 수행하여터널 붕괴 위험도 지수를 도출하였다. 연구 결과, 절리면 상태, 지하수 유입량, 보조공법, 절리면 간격, 지보패턴수준, 지반 등급 등이 터널 붕괴 위험도에 큰 영향을 미치는 것으로 나타났다. ◯◯ 터널에서 발생한 붕괴 사고 데이터를 활용하여 제안된 모델의 유효성을 평가한 결과, 사례 데이터의 위험도 지수와 실제 붕괴 현황이 잘 부합하는 것으로 나타나 본평가 모델의 적용 가능성을 확인하였다. 본 연구의 결과는 터널 공사의 안전성을 높이고, 인명 피해와 경제적 손실을 최소화하는 데 기여할 수 있을 것으로 기대된다. 향후 다양한 터널 공사 현장에서 본 모델을 적용하여 추가적인 평가와 개선을 통해 보다 정교한 위험도 평가 모델을 개발하는 것이 필요하다.
Abstract
This study proposes a new model for the systematic assessment and management oftunnel collapse risks. Tunnel construction is essential for developing infrastructure,including roads, railways, and subways, but managing collapse risks remains critical. To address this, 14 key influencing factors were identified, and their relative importance and weights were determined using the analytic hierarchy process (AHP) method. Monte Carlo method was applied to incorporate the probability distribution of each factor, resulting in a comprehensive risk assessment and the development of a tunnel collapse risk index. The findings indicate that factors such as Joint face conditions, groundwater inflow, auxiliary methods, joint face spacing, support pattern levels, and ground grade significantly affect tunnel collapse risks. The proposed model was validated using collapse accident data from the ◯◯ Tunnel, demonstrating a strong correlation between the calculated risk index and actual collapse incidents, thereby confirming the model’s applicability. The results of this study are expected to enhance the safety of tunnel construction and contribute to minimizing human casualties and economic losses. Future studies should focus on applying this model across diverse tunnel construction sites, conducting further evaluations, and refining theframework to develop a more sophisticated and precise risk assessment model.
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