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2026 Vol.25, Issue 2 Preview Page

Research Article

Prediction of Slope Safety Factors from Seepage Characteristics in River Embankments

하천 제방의 침투 안전율을 이용한 비탈면 안전율 예측

Kwang-Wu Lee1
,
Jeongjun Park2
,
Deokhee Won3
,
Gigwon Hong3
,
Kicheol Lee4*
이 광우1
,
박 정준2
,
원 덕희3
,
홍 기권3
,
이 기철4*
1Member, Research Fellow, Department of Geotechnical Engineering Research, 283 Goyang-daero, Ilsanseo-gu, Goyang-si, Gyeonggi-do 10223, Republic of Korea
2Member, Principal Researcher, Incheon Disaster Prevention Research Center, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
3Member, Associate Professor, Department of Urban InfraEngineering, Halla University, 28 Halladae-gil, Wonju, 26404, Republic of Korea
4Member, Research Professor, RISE Project Group, Halla University, 28 Halladae-gil, Wonju 26404, Republic of Korea
*Corresponding Author
30 June 2026. pp. 11-24
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Abstract

This study aims to propose a method for predicting slope safety factors using hydraulic safety factors derived from the seepage analysis of river embankment. Accordingly, seepage analyses and slope stability analyses were performed on levee cross-sections of the Tamjin river and Hampyeoncheon, national rivers in Jeollanam-do, and a database was constructed comprising the hydraulic safety factors based on the critical hydraulic gradient and critical velocity, the water-level time conditions, and the slope safety factors. The statistical correlation analysis revealed that, although a partially significant correlation was found between the hydraulic safety factor and the slope safety factor, its magnitude remained marginal. Accordingly, four machine learning models (SVR, KRR, RF, and XGB) were applied to predict the nonlinear data, but sufficient predictive performance could not be achieved owing to the lack of training data. To address this limitation, a Physics-Informed Neural Network (PINN), which incorporates physical laws into the learning process, was used to augment the data, and retraining was conducted by combining the augmented data with the original data. As a result of the retraining, the coefficient of determination improved markedly across all models, thereby confirming the potential to rapidly predict slope safety factors using seepage analysis results alone.

Keywords
Embankment
Seepage analysis
Slope safety factor
Machine learning
PINN
Data augmentation

본 연구는 하천 제방의 침투해석으로부터 산출되는 수리학적 안전율을 이용하여 비탈면 안전율을 예측하는 방법을 제시하고자 한다. 이에 따라 전라남도 국가하천인 탐진강과 함평천의 제방 단면을 대상으로 침투해석 및 비탈면 안정해석을 수행하였고, 한계동수경사 및 한계유속에 의한 수리학적 안전율과 수위 시간 조건, 비탈면 안전율로 구성된 데이터베이스를 구축하였다. 통계적 상관분석 결과, 수리학적 안전율과 비탈면 안전율 사이에는 부분적으로 유의한 상관관계가 확인되었으나 그 정도는 미미한 수준에 머물렀다. 이에 네 가지 머신러닝 모델(SVR, KRR, RF, XGB)을 적용하여 비선형적인 데이터를 예측하고자 하였으나, 학습 데이터 부족으로 충분한 예측 성능을 확보하지 못하였다. 이같은 한계를 보완하고자 물리 법칙을 학습에 반영하는 PINN(Physics-Informed Neural Network)으로 데이터를 증강시켰고, 원본 데이터와 결합하여 재학습을 수행하였다. 재학습 결과, 모든 모델에서 결정계수가 크게 향상되었으며, 이를 통해 침투해석 결과만으로 비탈면 안전율을 신속하게 예측할 수 있는 가능성을 확인하였다.

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Information
  • Publisher :Korean Geosythetics Society
  • Publisher(Ko) :한국지반신소재학회
  • Journal Title :Journal of the Korean Geosynthetics Society
  • Journal Title(Ko) :한국지반신소재학회 논문집
  • Volume : 25
  • No :2
  • Pages :11-24
  • Received Date : 2026-06-01
  • Revised Date : 2026-06-09
  • Accepted Date : 2026-06-14
  • DOI :https://doi.org/10.12814/jkgss.2026.25.2.011
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