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2024 Vol.40, Issue 2 Preview Page
Time Series Analysis for Predicting Deformation of Earth Retaining Walls

시계열 분석을 이용한 흙막이 벽체 변형 예측

Seunghwan Seo1
,
Moonkyung Chung2*
서 승환1
,
정 문경2*
1Member, Senior Researcher, Korea Institute of Civil Engr. and Building Tech.
2Member, Senior Research Fellow, Korea Institute of Civil Engr. and Building Tech.
1정회원, 한국건설기술연구원 지반연구본부 수석연구원
2정회원, 한국건설기술연구원 지반연구본부 선임연구위원
*Corresponding Author
30 April 2024. pp. 65-79
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Abstract

This study employs traditional statistical auto-regressive integrated moving average (ARIMA) and deep learning-based long short-term memory (LSTM) models to predict the deformation of earth retaining walls using inclinometer data from excavation sites. It compares the predictive capabilities of both models. The ARIMA model excels in analyzing linear patterns as time progresses, while the LSTM model is adept at handling complex nonlinear patterns and long-term dependencies in the data. This research includes preprocessing of inclinometer measurement data, performance evaluation across various data lengths and input conditions, and demonstrates that the LSTM model provides statistically significant improvements in prediction accuracy over the ARIMA model. The findings suggest that LSTM models can effectively assess the stability of retaining walls at excavation sites. Additionally, this study is expected to contribute to the development of safety monitoring systems at excavation sites and the advancement of time series prediction models.

Keywords
ARIMA
Deep learning
Earth retaining wall
Excavation
LSTM

본 연구는 전통적인 통계기반 ARIMA(Auto-Regressive Integrated Moving Average) 모델과 딥러닝 기반 LSTM(Long Short-Term Memory) 모델을 활용하여 굴착 현장의 지중경사계 데이터를 통한 흙막이 벽체 변형을 예측하고, 두 모델의 예측 성능을 비교 분석하였다. ARIMA 모델은 시간의 흐름에 따른 시계열 데이터의 선형적 패턴을 분석하는 데 강점을 보이는 반면, LSTM은 데이터의 복잡한 비선형 패턴과 장기 의존성을 포착하는 데 우수한 능력을 보여주었다. 본 연구는 흙막이 벽체 변형 예측을 위해 지중경사계 계측 데이터에 대한 전처리, 다양한 시계열 데이터 길이 및 입력변수 조건 등에 따른 성능 평가를 포함하였으며, LSTM 모델이 ARIMA 모델에 비해 통계적으로 유의미한 예측 성능 향상을 확인하였다. 본 연구의 결과는 굴착 현장에서의 지중경사계 데이터를 활용한 흙막이 벽체의 안정성 평가에 LSTM 모델을 효과적으로 적용할 수 있음을 보여준다. 또한 이를 바탕으로 향후 굴착 현장 전체에 대한 안전 모니터링 시스템 구축과 시계열 예측 모델 발전에 기여할 것으로 기대된다.

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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 :2
  • Pages :65-79
  • Received Date : 2024-03-08
  • Revised Date : 2024-03-28
  • Accepted Date : 2024-03-28
  • DOI :https://doi.org/10.7843/kgs.2024.40.2.65
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