## 3 Results
### Multi-source data fusion.
Similarly, the early-warning levels for groundwater level (E2) and pile horizontal displacement (E3) are yellow and orange, respectively. However, the actual early-warning level is yellow. It indicates that when a single source of information is used for early warning, the results are often biased due to the uncertainty of the data. Step (2). Early-warning results from multi-source data fusion. The fusion process is shown in Figure 8. Each monitoring point corresponds to six indicators, which need to be fused five times in total. As the number of fusion rounds increases, the early-warning level gradually stabilizes. It is due to the fact that the fusion rule maintains the positive risk information, allowing the positive information to be superimposed one by one during the fusion process. For monitoring point C1, the final early-warning result is obtained by fusing the BPAs of the three information sources in turn. E1 si [0.065, 0.0243, 0.566, 0.233, 0.109]. E2 is [0.158, 0.515, 0.084, 0.078, 0.165]. E3 is [0.077, 0.062, 0.566, 0.186, 0.109]. E1 is the surface settlement, E2 is the groundwater level, and E3 is the pile horizontal displacement. The conflict degree \(\alpha\), the uncertain degree \(\beta\), and the degree of credibility \(\delta\) are as follows:  
\[\alpha= \begin{bmatrix}0.462\\ 0.382\end{bmatrix} ,\ \beta= \begin{bmatrix}0.873&0.703\\ 0.867&0.799\end{bmatrix} ,\ \delta= \begin{bmatrix}0.310&0.378\\ 0.395&0.427\end{bmatrix}  \tag{8}\]  
The three single model results are fused using the proposed method, and the fused probability distribution is [0.133, 0.537, 0.163, 0.075, 0.092]. The results show that the early-warning level for monitoring point C1 is yellow and the early-warning results are consistent with the facts. The actual early-warning level for monitoring point C1 is yellow. As shown in Table 7, both E1 and E3 give incorrect results with low confidence, but E2 with higher confidence gives correct results. This demonstrates that the method proposed in this paper takes into account both the conflict and uncertainty of the model, such that the correct result of E2 corrects the incorrect results of E1 and E3 back to the correct result of the fusion model. In addition, the fact that the traditional D-S early-warning level is safe suggests that \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **m(Safe)** & **m(Yellow)** & **m(Orange)** & **m(Alarm)** & **m(G)** & **Model Levels** & **Actual Levels** \\ \hline E1 & 0.099 & 0.191 & 0.248 & 0.094 & 0.368 & orange & yellow \\  & & & k1 = 0.967, k2 = 0.947 & & & & \\ E2 & 0.197 & 0.705 & 0.037 & 0.059 & 0.002 & yellow & yellow \\  & & & k1 = 0.8, k2 = 0.833 & & & & \\ E3 & 0.098 & 0.201 & 0.227 & 0.088 & 0.386 & orange & yellow \\  & & & k1 = 0.9, k2 = 0.958 & & & & \\ D-S & 0.655 & 0.128 & 0 & 0.123 & 0.094 & safe & yellow \\ Method in this paper & 0.133 & 0.537 & 0.163 & 0.075 & 0.092 & yellow & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fusion results for monitoring point C1. traditional D-S can produce erroneous or even completely opposite results when dealing with high-conflict evidence. However, the method proposed in this paper reduces the adversarial impact of high-conflict evidence by improving the fusion rule and employing adjustments to global conflicts to obtain more accurate results. Firstly, single-source information early-warning models do not provide accurate decision-making advice for on-site construction. This is due to the fact that a single  
Figure 8: Fusion early-warning process. (**a**) C1. (**b**) C5. (**c**) C11. (**d**) C14. (**e**) C15. (**f**) C18. source of information does not fully consider the risk factors of pit collapse, and there are errors and uncertainties in the information, which ultimately leads to a slight deviation of the early-warning results from the actual data. Secondly, traditional D-S only accumulates consensus support; if a proposition is opposed by any evidence, it is rejected altogether, even though it receives support from other evidence.