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

Tomoyuki Okuda, Kouyu Suzuki, Naohiko Kohtake

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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%.

本文言語English
ホスト出版物のタイトルProceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
編集者Kiyota Hashimoto, Naoki Fukuta, Tokuro Matsuo, Sachio Hirokawa, Masao Mori, Masao Mori
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1053-1054
ページ数2
ISBN(電子版)9781538606216
DOI
出版ステータスPublished - 2017 11 15
イベント6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 - Hamamatsu, Shizuoka, Japan
継続期間: 2017 7 9 → …

出版物シリーズ

名前Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017

Other

Other6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
国/地域Japan
CityHamamatsu, Shizuoka
Period17/7/9 → …

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • 情報システム
  • 情報システムおよび情報管理

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