TBM tunneling performance fusion prediction based on multimodal decomposition and multi-deep learning

Kang Fu, Yiguo Xue, Daohong Qiu, Fanmeng Kong, Min Han, Haolong Yan,
TBM tunneling performance fusion prediction based on multimodal decomposition and multi-deep learning,
Automation in Construction,
Volume 176,
2025,
106271,
ISSN 0926-5805,
https://doi.org/10.1016/j.autcon.2025.106271.
(https://www.sciencedirect.com/science/article/pii/S0926580525003115)
Abstract: Accurate prediction of TBM tunneling performance is crucial for improving construction efficiency. This paper proposes a fusion prediction method based on multimodal decomposition and multi-Deep Learning. First, tunneling data are preprocessed to build a sample database. Then, an improved ISTL model is developed to decompose tunneling performance into trend, seasonal, cycle, and residual components. Hyperparameters of multiple Deep Learning models are optimized using an improved IWOA algorithm, forming the ISTL-multi-DL model for preliminary prediction. Subsequently, error correction is applied to obtain the CISTL-multi-DL model, achieving MAPE values of 1.89 % and 1.43 % for FPI and TPI predictions, respectively. Comparative analysis shows that the CISTL-multi-DL model outperforms the IWOA-Autoformer, IWOA-Attention-LSTM, IWOA-BiTCN, and IWOA-DeepAR models by an average of over 40 %, and demonstrates superiority over unoptimized and traditional Machine Learning models. The proposed model provides accurate multi-step predictions and valuable support for TBM tunneling construction.
Keywords: TBM; Tunneling performance; Multimodal decomposition; Multi-deep learning; Multi-step prediction