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2024 Vol.40, Issue 5 Preview Page
Assessment of Risk Levels in Cut-Slope Using Dimensionality Reduction and Clustering Analysis

차원축소와 클러스터링 분석을 활용한 도로비탈면 위험등급 산정

Seunghwan Seo1
,
Gunwoong Kim2
,
Younghoon Woo3
,
Byungsuk Park1
,
Juhyong Kim4
,
Seung-Hyun Kim5
,
Moonkyung Chung6*
서 승환1
,
김 건웅2
,
우 용훈3
,
박 병석1
,
김 주형4
,
김 승현5
,
정 문경6*
1Member, Senior Researcher, Korea Institute of Civil Engr. and Building Tech.
2Member, Postdoctoral Researcher, Korea Institute of Civil Engr. and Building Tech.
3Member, Research Specialist, Korea Institute of Civil Engr. and Building Tech.
4Member, Senior Research Fellow, Korea Institute of Civil Engr. and Building Tech.
5Member, Research Fellow, Korea Institute of Civil Engr. and Building Tech.
6Member, Senior Research Fellow, Korea Institute of Civil Engr. and Building Tech.
1정회원, 한국건설기술연구원 지반연구본부 수석연구원
2정회원, 한국건설기술연구원 지반연구본부 박사후연구원
3정회원, 한국건설기술연구원 지반연구본부 전임연구원
4정회원, 한국건설기술연구원 지반연구본부 선임연구위원
5정회원, 한국건설기술연구원 지반연구본부 연구위원
6정회원, 한국건설기술연구원 지반연구본부 선임연구위원
*Corresponding Author
31 October 2024. pp. 113-129
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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.

Keywords
CSMS
Cut-slope
Dimensionality reduction
K-means clustering
Risk level

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.

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Information
  • Publisher :The Korean Geotechnical Society
  • Publisher(Ko) :한국지반공학회
  • Journal Title :Journal of the Korean Geotechnical Society
  • Journal Title(Ko) :한국지반공학회 논문집
  • Volume : 40
  • No :5
  • Pages :113-129
  • Received Date : 2024-09-30
  • Revised Date : 2024-10-09
  • Accepted Date : 2024-10-10
  • DOI :https://doi.org/10.7843/kgs.2024.40.5.113
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