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

Yao, H., Zhao, S., Gao, Z. , Xue, Z., Song, B., Li, F., Li, J., Liu, Y., Hou, Y. and Wang, L. (2023) Data-driven analysis on the subbase strain prediction: a deep data augmentation-based study. Transportation Geotechnics, 40, 100957. (doi: 10.1016/j.trgeo.2023.100957)

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.

• Item Type: Articles
• Additional Information: 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.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gao, Dr Zhiwei
• Authors: Yao, H., Zhao, S., Gao, Z., Xue, Z., Song, B., Li, F., Li, J., Liu, Y., Hou, Y., and Wang, L.
• College/School: College of Science and Engineering > School of Engineering > Infrastructure and Environment
• Journal Name: Transportation Geotechnics
• Publisher: Elsevier
• ISSN: 2214-3912
• ISSN (Online): 2214-3912
• Published Online: 11 February 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Transportation Geotechnics 40: 100957
• Publisher Policy: Reproduced under a Creative Commons License