Journal of the Korean Geosynthetics Society (한국지반신소재학회논문집)

Korean Geosynthetics Society (한국지반신소재학회)
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Short-Term Crack in Sewer Forecasting Method Based on CNN-LSTM Hybrid Neural Network Model

CNN-LSTM 합성모델에 의한 하수관거 균열 예측모델

  • Jang, Seung-Ju (Civil Eng. Office 1, Seoul Metro) ;
  • Jang, Seung-Yup (Dept. of Transportation System Engineering, Graduate School of Transportation)
  • Received : 2022.03.25
  • Accepted : 2022.06.07
  • Published : 2022.06.30
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Abstract

In this paper, we propose a GoogleNet transfer learning and CNN-LSTM combination method to improve the time-series prediction performance for crack detection using crack data captured inside the sewer pipes. LSTM can solve the long-term dependency problem of CNN, so spatial and temporal characteristics can be considered at the same time. The predictive performance of the proposed method is excellent in all test variables as a result of comparing the RMSE(Root Mean Square Error) for time series sections using the crack data inside the sewer pipe. In addition, as a result of examining the prediction performance at the time of data generation, the proposed method was verified that it is effective in predicting crack detection by comparing with the existing CNN-only model. If the proposed method and experimental results obtained through this study are utilized, it can be applied in various fields such as the environment and humanities where time series data occurs frequently as well as crack data of concrete structures.

본 연구에서는 하수관거 내부에서 촬영된 균열 데이터를 활용하여 균열검출에 대한 시계열 예측 성능을 개선하기 위해 GoogleNet의 전이학습과 CNN- LSTM(Long Short-Term Memory) 결합 방법을 제안하였다. LSTM은 합성곱방법(CNN)의 장기의존성 문제를 해결할 수 있으며 공간 및 시간적 특징을 동시에 모델링 할 수 있다. 제안 방법의 성능을 검증하기 위해 하수관거 내부 균열 데이터를 활용하여 학습데이터, 초기학습률 및 최대 Epochs를 변화하면서 RMSE를 비교한 결과 모든 시험 구간에서 제안 방법의 예측 성능이 우수함을 알 수 있다. 또한 데이터가 발생하는 시점에 대한 예측 성능을 살펴본 결과 역시 제안방법이 우수하게 나타나 균열검출의 예측에서 제안 방법이 효율적인 것을 검증하였다. 기존 합성곱방법(CNN) 단독 모델과 비교함으로써 본 연구를 통해 확보된 제안 방법과 실험 결과를 활용할 경우 콘크리트 구조물의 균열데이터뿐만 아니라 시계열 데이터가 많이 발생하는 환경, 인문과학 등 다양한 영역에서 응용이 가능하다.

Keywords

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