Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning

Wu, Yongxin; Yang, Hanzhi; Zhang, Houle; Hou, Yue; Yang, Shangchuan

doi:10.1111/mice.70096

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Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning

Yongxin Wu, Hanzhi Yang, Houle Zhang, Yue Hou

, Shangchuan Yang

Computer-Aided Civil and Infrastructure Engineering

Swansea University Author: Yue Hou

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DOI (Published version): 10.1111/mice.70096

Abstract

This study introduces a novel integrated framework for real-time tunnel boring machine (TBM) thrust prediction, addressing critical limitations in handling non-stationarity, complex spatiotemporal dependencies, and abrupt disturbances. First, a real-time windowed multi-resolution analysis process, w...

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Published in:	Computer-Aided Civil and Infrastructure Engineering
ISSN:	1093-9687 1467-8667
Published:	Wiley 2025
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa70560

first_indexed	2025-10-02T14:10:59Z
last_indexed	2025-10-25T06:47:39Z
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spelling	2025-10-24T14:44:20.4654885 v2 70560 2025-10-02 Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2025-10-02 ACEM This study introduces a novel integrated framework for real-time tunnel boring machine (TBM) thrust prediction, addressing critical limitations in handling non-stationarity, complex spatiotemporal dependencies, and abrupt disturbances. First, a real-time windowed multi-resolution analysis process, which performs decomposition strictly within each segmented sample window, is presented to explicitly disentangle the latent multi-scale dependencies embedded in the thrust data. This ensures strict causality (using only current/historical data), prevents information leakage, and enhances resolution adaptability by capturing local dynamics specific to each data segment, overcoming global averaging effects. Second, a novel synergistic prediction architecture, integrating a hybrid static model with dynamic online residual correction, is proposed. A specifically optimized CNN-LSTM-attention primary model learns complex long-term global patterns. Crucially, an efficient random Fourier features-based online module is dedicated solely to real-time learning of the primary model's residual dynamics, acting as a dynamic corrector rather than an independent predictor. This targeted residual correction significantly enhances robustness against non-stationarity and disturbances. These innovations form an integrated solution and systematically address real-time capability, local adaptability, complex pattern learning, and dynamic error correction. The results indicate that the presented method reduces the mean absolute percentage error from 2.84% to 1.89% and increased R2 from 0.901 to 0.953. The generalizability of the model was further confirmed through the application of diverse datasets obtained from various chainages along the route. The proposed machine learning–based model can provide guidance for operators in real-time TBM parameter adjustment during construction. Journal Article Computer-Aided Civil and Infrastructure Engineering 0 Wiley 1093-9687 1467-8667 10 10 2025 2025-10-10 10.1111/mice.70096 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) The authors acknowledge the support of the National Natural Science Foundation of China under grant number 42377140. 2025-10-24T14:44:20.4654885 2025-10-02T15:06:43.6029421 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Yongxin Wu 1 Hanzhi Yang 2 Houle Zhang 3 Yue Hou 0000-0002-4334-2620 4 Shangchuan Yang 5 70560__35475__471f99e6615c4b76b1b9952d77dd0dd4.pdf 70560.VOR.pdf 2025-10-24T14:38:11.2248446 Output 2327749 application/pdf Version of Record true © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC BY). true eng http://creativecommons.org/licenses/by/4.0/
title	Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning
spellingShingle	Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning Yue Hou
title_short	Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning
title_full	Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning
title_fullStr	Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning
title_full_unstemmed	Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning
title_sort	Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning
author_id_str_mv	92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv	92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author	Yue Hou
author2	Yongxin Wu Hanzhi Yang Houle Zhang Yue Hou Shangchuan Yang
format	Journal article
container_title	Computer-Aided Civil and Infrastructure Engineering
container_volume	0
publishDate	2025
institution	Swansea University
issn	1093-9687 1467-8667
doi_str_mv	10.1111/mice.70096
publisher	Wiley
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	This study introduces a novel integrated framework for real-time tunnel boring machine (TBM) thrust prediction, addressing critical limitations in handling non-stationarity, complex spatiotemporal dependencies, and abrupt disturbances. First, a real-time windowed multi-resolution analysis process, which performs decomposition strictly within each segmented sample window, is presented to explicitly disentangle the latent multi-scale dependencies embedded in the thrust data. This ensures strict causality (using only current/historical data), prevents information leakage, and enhances resolution adaptability by capturing local dynamics specific to each data segment, overcoming global averaging effects. Second, a novel synergistic prediction architecture, integrating a hybrid static model with dynamic online residual correction, is proposed. A specifically optimized CNN-LSTM-attention primary model learns complex long-term global patterns. Crucially, an efficient random Fourier features-based online module is dedicated solely to real-time learning of the primary model's residual dynamics, acting as a dynamic corrector rather than an independent predictor. This targeted residual correction significantly enhances robustness against non-stationarity and disturbances. These innovations form an integrated solution and systematically address real-time capability, local adaptability, complex pattern learning, and dynamic error correction. The results indicate that the presented method reduces the mean absolute percentage error from 2.84% to 1.89% and increased R2 from 0.901 to 0.953. The generalizability of the model was further confirmed through the application of diverse datasets obtained from various chainages along the route. The proposed machine learning–based model can provide guidance for operators in real-time TBM parameter adjustment during construction.
published_date	2025-10-10T05:31:08Z
_version_	1851098051435823104
score	11.089386

Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning

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