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 |
|---|---|
| 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 |
|---|---|
| Country | Japan |
| City | Hamamatsu, Shizuoka |
| Period | 17/7/9 → … |
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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
Cite this
Proposal and Evaluation of Prediction of Pavement Rutting Depth by Recurrent Neural Network. / Okuda, Tomoyuki; Suzuki, Kouyu; Kohtake, Naohiko.
Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1053-1054 8113413.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Proposal and Evaluation of Prediction of Pavement Rutting Depth by Recurrent Neural Network
AU - Okuda, Tomoyuki
AU - Suzuki, Kouyu
AU - Kohtake, Naohiko
PY - 2017/11/15
Y1 - 2017/11/15
N2 - 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%.
AB - 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%.
KW - Machine learning
KW - Pavement condition survey
KW - Recurrent neural network
KW - Road management
UR - http://www.scopus.com/inward/record.url?scp=85040550726&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040550726&partnerID=8YFLogxK
U2 - 10.1109/IIAI-AAI.2017.177
DO - 10.1109/IIAI-AAI.2017.177
M3 - Conference contribution
SP - 1053
EP - 1054
BT - Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -