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2026 Vol.42, Issue 2 Preview Page
Prediction of Settlement of Caisson-type Quay Walls Using Deep Learning During Earthquakes

지진 시 딥러닝을 활용한 케이슨 안벽의 침하량 예측

Hyeonsu Yun1
,
Seong-Kyu Yun2
,
Sungchul Lee3
,
Gichun Kang4*
윤 현수1
,
윤 성규2
,
이 성철3
,
강 기천4*
1Member, Ph.D Candidate, Department of Civil Engineering, College of Engineering, Gyeongsang National Univ.
2Member, Ph.D, Department of Civil Engineering, College of Engineering, Gyeongsang National Univ.
3Member, Executive Director, Daeyang Englneering
4Member, Associate Prof., Department of Civil Engineering, College of Engineering, Gyeongsang National Univ.
1정회원, 경상국립대학교 공과대학 토목공학과 박사과정
2정회원, 경상국립대학교 공과대학 토목공학과 공학박사
3정회원, ㈜대양엔지니어링 상무
4정회원, 경상국립대학교 공과대학 토목공학과 부교수
*Corresponding Author
30 April 2026. pp. 89-99
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Abstract

The 2017 Pohang earthquake heightened interest in soil liquefaction in Korea and underscored the need for seismic stability in port structures such as caisson-type quay walls. Conventional numerical analytical methods for predicting damage require significant computational time for repeated simulations, whereas large-scale physical model tests are often limited in their ability to replicate diverse conditions. This study constructed datasets on settlement and horizontal displacement using the FLIP numerical analysis program, exploring different ground conditions (SPT N-values) and ground motion intensities ranging from 0.1 to 2.0 m/s2. Based on the generated time-series response data, we assessed the predictive performance of deep learning models. Different algorithms were compared and evaluated using root mean square error and the coefficient of determination (R2). The findings confirm that a deep learning approach is practically applicable for predicting liquefaction-induced settlement in caisson-type quay walls.

Keywords
Deep learning
Caisson-type quay wall
Earthquake
Liquefaction

2017년 포항지진을 계기로 국내에서 액상화 현상에 대한 관심이 증가하였으며, 케이슨식 안벽과 같은 항만 구조물의 내진 안정성 확보가 중요한 과제로 대두되고 있다. 기존 수치해석 기반의 피해 예측은 반복해석에서 많은 시간이 소요되며 대형 모형시험 또한 다양한 조건에 대한 반복수행이 어렵다는 한계를 가진다. 이에 본 연구에서는 FLIP 수치해석을 통해 다양한 지반 조건(N치), 지진동 세기(0.1~2.0m/s2)에 따른 침하량 및 수평변위 데이터를 구축하고 이를 기반으로 딥러닝 모델의 예측성능을 검토하였다. 구축된 시계열 응답 데이터를 대상으로 여러 알고리즘의 성능을 비교·분석하였으며 RMSE, 결정계수(R2)을 이용하여 모델 성능을 평가하였다. 분석결과 딥러닝 기반 접근법은 케이슨식 안벽의 액상화 피해 예측에 있어 적용 가능성이 있음을 확인하였다.

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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 : 42
  • No :2
  • Pages :89-99
  • Received Date : 2026-03-05
  • Revised Date : 2026-04-06
  • Accepted Date : 2026-04-08
  • DOI :https://doi.org/10.7843/kgs.2026.42.2.89
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