Journal of the Korean Geotechnical Society (한국지반공학회논문집)

Korean Geotechnical Society (한국지반공학회)
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Assessment of Risk Levels in Cut-Slope Using Dimensionality Reduction and Clustering Analysis

차원축소와 클러스터링 분석을 활용한 도로비탈면 위험등급 산정

  • Seo, Seunghwan (Korea Institute of Civil Engr. and Building Tech.) ;
  • Kim, Gunwoong (Korea Institute of Civil Engr. and Building Tech.) ;
  • Woo, Younghoon (Korea Institute of Civil Engr. and Building Tech.) ;
  • Park, Byungsuk (Korea Institute of Civil Engr. and Building Tech.) ;
  • Kim, Juhyong (Korea Institute of Civil Engr. and Building Tech.) ;
  • Kim, Seung-Hyun (Korea Institute of Civil Engr. and Building Tech.) ;
  • Chung, Moonkyung (Korea Institute of Civil Engr. and Building Tech.)
  • 서승환 (한국건설기술연구원 지반연구본부) ;
  • 김건웅 (한국건설기술연구원 지반연구본부) ;
  • 우용훈 (한국건설기술연구원 지반연구본부) ;
  • 박병석 (한국건설기술연구원 지반연구본부) ;
  • 김주형 (한국건설기술연구원 지반연구본부) ;
  • 김승현 (한국건설기술연구원 지반연구본부) ;
  • 정문경 (한국건설기술연구원 지반연구본부)
  • Received : 2024.09.30
  • Accepted : 2024.10.10
  • Published : 2024.10.31
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Abstract

This study reclassifies the risk levels of cut-slopes and addresses the limitations inherent in existing evaluation methods using road slope maintenance data. Conventional risk assessment predominantly relies on subjective expert judgment, resulting in issues of consistency and reliability. To mitigate these limitations, this study applies dimensionality reduction techniques, specifically Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), followed by K-means clustering, to classify new risk levels. The clustering results using PCA demonstrated more distinct cluster separation compared to LDA, and also showed superior performance in terms of the silhouette coefficient and other clustering metrics. This suggests that the existing risk level labels may not adequately capture the underlying data structure. Furthermore, the inconsistency observed between LDA-based clustering results and current risk labels indicates potential reliability issues in the present labeling approach. To resolve this, new risk levels were assigned using PCA and K-means clustering, with cluster risk levels evaluated based on risk scores. A quantitative analysis of key risk factors was also conducted to establish criteria for risk classification and assess the impact of each variable on the different risk levels. This study proposes a data-driven, objective, and quantitative approach to risk level evaluation, aiming to improve the efficiency and reliability of road slope management.

본 연구는 도로 비탈면 유지관리 데이터를 활용하여 위험등급을 재분류하고, 기존 위험등급 평가 방식의 한계를 분석하였다. 기존의 위험등급 평가는 주로 전문가의 주관적 판단에 의존하여 평가의 일관성에 한계가 있었다. 이를 개선하기 위해 본 연구에서는 차원 축소 기법인 주성분 분석(PCA)과 선형 판별 분석(LDA)을 적용하여 데이터를 축소하고, K-means 클러스터링을 통해 새로운 위험등급을 분류하였다. PCA를 사용한 클러스터링이 LDA에 비해 더 명확한 클러스터 분리를 보였으며, 실루엣 계수 등 성능 지표에서도 우수한 결과를 나타냈다. 이는 기존 위험등급 레이블이 데이터의 실제 구조를 충분히 반영하지 못함을 시사한다. 또한, LDA를 기반으로 한 클러스터링 결과와 기존 위험등급 레이블 간의 일치도가 낮아, 기존 레이블의 신뢰성에 한계가 있음을 확인하였다. 이를 해결하기 위해, PCA와 K-means 클러스터링을 이용하여 새로운 위험등급을 부여하고, 위험점수를 기반으로 각 클러스터의 등급을 분류하였다. 주요 위험 요인에 대한 정량적 분석을 통해 등급 부여의 기준을 설정하고, 각 등급별 주요 변수의 영향을 평가하였다. 본 연구는 데이터 중심의 객관적이고 정량적인 위험등급 평가 방법을 제시하여 도로 비탈면 관리의 효율성과 신뢰성을 향상시키는 데 기여하고자 한다.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 한국건설기술연구원 주요사업으로 수행되었습니다(과제번호 20240133-001, 지반분야 재난재해 대응과 미래 건설산업 신성장을 위한 지반 기술 연구).

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