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http://dx.doi.org/10.12814/jkgss.2022.21.2.011

Short-Term Crack in Sewer Forecasting Method Based on CNN-LSTM Hybrid Neural Network Model  

Jang, Seung-Ju (Civil Eng. Office 1, Seoul Metro)
Jang, Seung-Yup (Dept. of Transportation System Engineering, Graduate School of Transportation)
Publication Information
Journal of the Korean Geosynthetics Society / v.21, no.2, 2022 , pp. 11-19 More about this Journal
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.
Keywords
CNN; LSTM; Hybrid model; Sewer pipe;
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