No Cover Image

Journal article 574 views 62 downloads

Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study

Hui Yao, Shibo Zhao, Zhiwei Gao, Zhongjun Xue, Bo Song, Feng Li, Ji Li Orcid Logo, Yue Liu, Yue Hou, Linbing Wang

Transportation Geotechnics, Volume: 40, Start page: 100957

Swansea University Authors: Ji Li Orcid Logo, Yue Hou

  • 62655_VoR.pdf

    PDF | Version of Record

    © 2023 The Author(s). This is an open access article under the CC BY license

    Download (2.85MB)

Abstract

The service quality of the subbase may affect the overall road performance during its service life. Thus, monitoring and prediction of subbase strain development are of great importance for civil engineers. In this paper, a method based on the time-series augmentation was employed to predict the sub...

Full description

Published in: Transportation Geotechnics
ISSN: 2214-3912
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa62655
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: The service quality of the subbase may affect the overall road performance during its service life. Thus, monitoring and prediction of subbase strain development are of great importance for civil engineers. In this paper, a method based on the time-series augmentation was employed to predict the subbase strain development. The time-series generative adversarial network (TimeGAN) model was implemented to perform the augmentation of time-series data based on the original monitored data. The augmented data was trained through deep learning network to learn the feature correlation of the subbase strain. The effectiveness of TimeGAN on the prediction accuracy was evaluated through the Attention-Sequence to Sequence (Attention-Seq2seq) model, and temporal convolution network-adaptively parametric rectifier linear units (TCN-APReLU) model. Results indicated that the TimeGAN network could capture sufficient information from the time-series monitored data of subbase strain development so that the corresponding augmented data matches well with the original data, which improves the prediction accuracy. It is also discovered that the combination of TimeGAN and TCN-APReLU appropriately predict the subbase strain development based on the original monitored data.
Keywords: Subbase strain development; Intelligent analysis; Data augmentation; Model interpretability; Deep analysis
College: Faculty of Science and Engineering
Funders: This work was supported by the Opening project fund of Materials Service Safety Assessment Facilities (MSAF- 2021-109), the International Research Cooperation Seed Fund of Beijing University of Technology (No. 2021A05), the National Natural Science Foundation of China (grant number 52008012), and Hunan Expressway Group Co. Ltd and the Hunan Department of Transportation (No. 202152) in China.
Start Page: 100957