Life-cycle reliability estimation of asphalt pavement based on machine learning approach

J. Xin, M. Zhang, M. Akiyama, D. M. Frangopol, J. Pei

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Asphalt pavement is a complex engineering system which deteriorates due to several mechanical and environmental stressors (e.g. moisture damage, freeze-thaw cycles and traffic load). To predict the time-dependent performance of asphalt pavement, it is necessary to develop a deterioration model incorporating the associated variables under uncertainty. Artificial Neural Networks (ANNs) are effective intelligence technologies to develop an accurate prediction model with a large amount of data. In this paper, a time-dependent reliability assessment method based on the ANNs model is presented. ANNs are used to develop the performance prediction model of asphalt pavement according to the training data selected from the Long-term Pavement Performance database. The life-cycle reliability of asphalt pavement is calculated using the ANNs model based on Monte Carlo simulation with Importance Sampling. Two case studies are presented to investigate the effects of sublayers thickness and traffic levels on the life-cycle reliability.

本文言語English
ホスト出版物のタイトルLife-Cycle Civil Engineering
ホスト出版物のサブタイトルInnovation, Theory and Practice - Proceedings of the 7th International Symposium on Life-Cycle Civil Engineering, IALCCE 2020
編集者Airong Chen, Xin Ruan, Dan M. Frangopol
出版社CRC Press/Balkema
ページ246-251
ページ数6
ISBN(電子版)9780367360191
DOI
出版ステータスPublished - 2020
イベント7th International Symposium on Life-Cycle Civil Engineering, IALCCE 2020 - Shanghai, China
継続期間: 2020 10月 272020 10月 30

出版物シリーズ

名前Life-Cycle Civil Engineering: Innovation, Theory and Practice - Proceedings of the 7th International Symposium on Life-Cycle Civil Engineering, IALCCE 2020

Conference

Conference7th International Symposium on Life-Cycle Civil Engineering, IALCCE 2020
国/地域China
CityShanghai
Period20/10/2720/10/30

ASJC Scopus subject areas

  • 計算力学
  • 土木構造工学

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