Proposal and Evaluation of Prediction of Pavement Rutting Depth by Recurrent Neural Network

Tomoyuki Okuda; Kouyu Suzuki; Naohiko Kohtake

doi:10.1109/IIAI-AAI.2017.177

Proposal and Evaluation of Prediction of Pavement Rutting Depth by Recurrent Neural Network

Tomoyuki Okuda, Kouyu Suzuki, Naohiko Kohtake

Graduate School of System Design and Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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	https://doi.org/10.1109/IIAI-AAI.2017.177
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 → …

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

Access to Document

10.1109/IIAI-AAI.2017.177

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Cite this

Okuda, T., Suzuki, K., & Kohtake, N. (2017). Proposal and Evaluation of Prediction of Pavement Rutting Depth by Recurrent Neural Network. In Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 (pp. 1053-1054). [8113413] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2017.177

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

Okuda, T, Suzuki, K & Kohtake, N 2017, Proposal and Evaluation of Prediction of Pavement Rutting Depth by Recurrent Neural Network. in Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017., 8113413, Institute of Electrical and Electronics Engineers Inc., pp. 1053-1054, 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, Hamamatsu, Shizuoka, Japan, 17/7/9. https://doi.org/10.1109/IIAI-AAI.2017.177

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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%.",

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