AWCNet: A TimesNet-based model for real-time TBM thrust and torque prediction over extended durations
Source abstract
Abstract
Accurate real-time prediction of thrust and torque is crucial for tunnel boring machines (TBM) tunneling safety and efficiency. This paper proposes Att-Wave-ConvNet (AWCNet), a TBM thrust and torque prediction model based on TimesNet, designed for longer real-time forecasting. It replaces fast Fourier transform and complex convolution with discrete wavelet packet transform and two-dimensional elevated convolution, while integrating a multi-head self-attention mechanism to enhance long-sequence feature extraction. Experiments on field data from a challenging hard-rock tunneling project show that AWCNet achieves reliable prediction even at a 60-s length, outperforming TimesNet, Dlinear, Informer and Transformer. Compared to existing methods, it reduces lowest root mean square error (RMSE) and mean absolute error (MAE) by 0.169 and 0.047, improvesR2by 4.8%, and shortens prediction runtime by 26.07%. The results confirm that AWCNet ensures both accuracy and efficiency for real-time TBM load prediction, demonstrating its practical applicability over extended durations.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Tunneling and Rock Mechanics · Drilling and Well Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
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