Long-Distance Shield Tunnelling Performance Prediction Based on Informer.

Shield performance prediction plays a critical role in construction decision-making. However, current models suffer from significant performance degradation in long-distance prediction. To address this gap, we propose a novel Long-Distance Shield Performance Prediction model (LSPP), which leverages the long-term prediction capabilities of Informer. The LSPP model incorporates conventional monitoring data, tunnelling parameters, and stratigraphic spatial information and is optimized using a ProbSparse self-attention mechanism and dynamic decoding techniques. A series of experiments demonstrate that LSPP significantly outperforms traditional models, such as LSTM and GRUs, particularly in long-distance predictions and under conditions of stratigraphic changes. Notably, the model achieves an R² of 0.82 when predicting penetration after six rings, making it highly accurate and stable for engineering decision-making. [ABSTRACT FROM AUTHOR]