Long-Distance Shield Tunnelling Performance Prediction Based on Informer
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Tunneling and Rock Mechanics · Geotechnical and Geomechanical Engineering · Geoscience and Mining Technology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
SHU-SUCG Research Centre for Building Industrialization, Shanghai University, Shanghai 200072, China
SHU-UTS SILC Business School, Shanghai University, Shanghai 201800, China
School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200072, China
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