## 6 Conclusions  
It is vital to grasp the tunnel face deformation information for rock tunnels using the drilling and blasting construction method. This study implemented a WSN, including gateway transmission, relay point, and sensor nodes, to obtain high-granularity deformation data during construction. A transformer model was proposed, which considers the construction sequence into the positional bedding and has an attention module to deep learn the high dimensionality correlation between the nearby deformation data and the tunnel face deformation. An attention-enhanced LSTM model and the LSTM model were also constructed to predict the tunnel face deformation and compare it to the performance of the transformer model.  
A site study conducted on a shallow buried tunnel section suggested good performance of the proposed WSN system. The transformer model showed the best performance in terms of the model prediction results, whereas the attention-enhanced LSTM and LSTM models showed less accurate prediction results. The prediction results suggest that the transformer can extract more information from the time sequence data than the attention-enhanced LSTM and LSTM. Instead of accurately predicting the deformation value, the proposed approach can capture the dynamic changing of deformations during tunnel construction, allowing the site engineers to determine the possible risks involved in the rock tunnel construction process.  
It should be noted that the deformation monitoring data during the construction phase only constitute part of the site conditions. The tunnel design specifications, the embedding depth, and the surrounding rock conditions should also be considered to understand the tunnel risks. Therefore, computer vision-based techniques can be adopted in future studies to obtain rich features of the surrounding rock mass and identify the discontinuities, weak interlayers, and water leakages in the excavated sections. With multisource data input, predicting the tunnel face deformation and the potential risks associated with the construction process can become more accurate and reliable.  
Conceptualization, M.Z., Z.X., C.N. and K.F.; data curation, M.Z., C.N. and Z.S.; formal analysis, M.Z. and C.N.; funding acquisition, M.Z., Z.X. and K.F.; investigation, M.Z. and C.N.; methodology, M.Z., Z.X., C.N., B.H. and K.F.; project administration, M.Z., Z.X. and K.F.; resources, M.Z., Z.X., B.H. and K.F.; software, M.Z. and C.N.; supervision, M.Z., Z.X. and K.F.; validation, M.Z., C.N., Z.S. and K.F.; visualization, M.Z., C.N. and Z.S.; writing--original draft, M.Z., Z.X., C.N., Z.S., B.H. and K.F.; writing--review and editing, M.Z., Z.X., C.N., Z.S., B.H. and K.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China State Construction Railway Investment & Engineering Group, grant number CSCECZJTT-2021-07.  
Not applicable.  
The financial support is gratefully acknowledged. In addition, we also thank [PERSON], [PERSON], [PERSON], and [PERSON] from Tongji University, China, for their valuable works in the monitoring data collection of this paper.  
The authors declare no conflict of interest.