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Real‐time prediction of tunnel boring machine thrust based on multi‐resolution analysis and online learning
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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...
| Published in: | Computer-Aided Civil and Infrastructure Engineering |
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| ISSN: | 1093-9687 1467-8667 |
| Published: |
Wiley
2025
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70560 |
| 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, 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. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
The authors acknowledge the support of the National Natural Science Foundation of China under grant number 42377140. |