Optimized CNN-BiLSTM-Attention with hybrid signal denoising: a novel interpretable framework for prediction of shield tunneling advance speed
Key Points
The proposed framework successfully predicts tunneling advance speed using advanced algorithms.
Prediction accuracy improved by 25% with optimized signal denoising techniques in the model.
The analysis leverages a combination of CNN and BiLSTM architectures with attention mechanisms for detailed insights.
Significance lies in the model's interpretability, allowing for better understanding and adjustments in tunneling operations.
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Optimized CNN-BiLSTM-Attention with hybrid signal denoising: a novel interpretable framework for prediction of shield tunneling advance speed | Synapse