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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

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DOI (Published version): 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 sub...

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Published in: Transportation Geotechnics
ISSN: 2214-3912
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62655
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spelling 2023-03-06T10:38:10.8237078 v2 62655 2023-02-14 Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study 4123c4ddbcd6e77f580974c661461c7c 0000-0003-4328-3197 Ji Li Ji Li true false 92bf566c65343cb3ee04ad963eacf31b Yue Hou Yue Hou true false 2023-02-14 CIVL 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. Journal Article Transportation Geotechnics 40 100957 Elsevier BV 2214-3912 Subbase strain development; Intelligent analysis; Data augmentation; Model interpretability; Deep analysis 1 5 2023 2023-05-01 10.1016/j.trgeo.2023.100957 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University SU Library paid the OA fee (TA Institutional Deal) 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. 2023-03-06T10:38:10.8237078 2023-02-14T09:59:09.1918732 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Hui Yao 1 Shibo Zhao 2 Zhiwei Gao 3 Zhongjun Xue 4 Bo Song 5 Feng Li 6 Ji Li 0000-0003-4328-3197 7 Yue Liu 8 Yue Hou 9 Linbing Wang 10 62655__26744__fa77ffab5c5b49d0ba5991b716c2a931.pdf 62655_VoR.pdf 2023-03-06T10:36:27.6801534 Output 2989706 application/pdf Version of Record true © 2023 The Author(s). This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study
spellingShingle Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study
Ji Li
Yue Hou
title_short Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study
title_full Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study
title_fullStr Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study
title_full_unstemmed Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study
title_sort Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study
author_id_str_mv 4123c4ddbcd6e77f580974c661461c7c
92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 4123c4ddbcd6e77f580974c661461c7c_***_Ji Li
92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Ji Li
Yue Hou
author2 Hui Yao
Shibo Zhao
Zhiwei Gao
Zhongjun Xue
Bo Song
Feng Li
Ji Li
Yue Liu
Yue Hou
Linbing Wang
format Journal article
container_title Transportation Geotechnics
container_volume 40
container_start_page 100957
publishDate 2023
institution Swansea University
issn 2214-3912
doi_str_mv 10.1016/j.trgeo.2023.100957
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description 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.
published_date 2023-05-01T04:22:26Z
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score 11.013148

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