## 5 Conclusions and Future Work  
Based on the improved D-S model, a high-accuracy multi-source data fusion method is proposed in this research, which can achieve accurate assessment of tunnel collapse risk. In this method, the evidence conflict coefficient K is used as the identification index, and the credibility and importance are introduced. The weight coefficient is determined according to whether the conflicting evidence is divided into two situations. The advanced geological forecast data, on-site inspection data and instrument monitoring data are trained by CM,  
Figure 6: Confusion matrix diagram of different models. (**a**) Advanced Geological Prediction. (**b**) Field Inspection. (**c**) Instrument Monitoring. (**d**) D-S. (**e**) Improved D-S.  
Figure 7: Scattered point distribution of three evidence conflict values. (**a**) Traditional D-S model. (**b**) Improved D-S model. The red line represents the conflict threshold. The dots and stars represent evidence conflict value.  
GBDT and SVC, respectively, to obtain the initial BPA value. Finally, the identified conflict evidence is adjusted by combining the weight coefficient, and the overall collapse risk value is obtained by fusing the evidence from different sources. The methods developed in this paper have the following innovations and capabilities:  
1. It can synthesize multi-source information to obtain a more accurate result for tunneling collapse risk assessment. Due to many influencing factors, the tunneling collapse risk assessment is a multi-attribute decision making problem. Single-source assessment methods have difficulty fully considering all risk factors, resulting in biased prediction results. The performance of the fused model is better than the single information sources model with higher precision.
2. Compared with the traditional D-S theory, the improved method has more advantages in dealing with high conflict information. When the risk assessment results of three single information sources are inconsistent, the improved fusion model considers the importance rating and credibility of the assessment results, which improves the accuracy of the final assessment results.  
The method proposed in this paper still has limitations. Experts still need to participate in the entire assessment process, which means that a truly fully automated risk assessment has not been achieved. The tunnel collapse training data set is still very small, and a big data system needs to be developed nationwide. In addition, this method cannot predict the risk status of the next construction process, and further research is needed.  
Conceptualization, B.W., J.Z. and R.Z.; methodology, J.Z., R.Z. and W.Z.; software, J.Z., R.Z. and W.Z.; validation, J.Z., R.Z. and W.Z.; formal analysis, J.Z., R.Z. and W.Z.; investigation, J.Z., R.Z. and W.Z.; resources, J.Z., R.Z. and W.Z.; writing--original draft preparation, J.Z.; writing--review and editing, J.Z.; visualization, J.Z., R.Z. and W.Z.; supervision, B.W. and C.L.; project administration, B.W. and C.L.; funding acquisition, B.W. and C.L. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.