Abstract
This paper describes a method of predicting the rutting depth by introducing Adam and dropout into MLP of neural network and GRU of recurrent neural net that can handle time series data. We built a model to predict the current rutting depth from the past rutting 3 years ago. We compared RMSE with the multiple regression model (MLR), which is most frequently used as a regression problem for the time variation of rutting depth. As a result, RMSE decreased in the order of MLR, MLP, GRU. The difference between GRU and RMSE of MLR was about 10%.
Original language | English |
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Title of host publication | Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1053-1054 |
Number of pages | 2 |
ISBN (Electronic) | 9781538606216 |
DOIs | |
Publication status | Published - 2017 Nov 15 |
Event | 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 - Hamamatsu, Shizuoka, Japan Duration: 2017 Jul 9 → … |
Other
Other | 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 |
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Country | Japan |
City | Hamamatsu, Shizuoka |
Period | 17/7/9 → … |
Keywords
- Machine learning
- Pavement condition survey
- Recurrent neural network
- Road management
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
- Information Systems and Management