## 5 Conclusions  
A fusion early-warning method for subway pit collapse risk based on deformation prediction and improved cloud D-S evidence theory is proposed in this research. Combining GPR for rolling prediction of deformations, then using cloud models for basic probability  
Figure 9: The results of single data early warning and multi-data fusion early warning. (**a**) Early-warning results of monitoring point C1. (**b**) Early-warning results of monitoring point C5. (**c**) Early-warning results of monitoring point C11. (**d**) Early-warning results of monitoring point C14. (**e**) Early-warning results of monitoring point C15. (**f**) Early-warning results of monitoring point C18. (**g**) Early-warning results for single and multiple data sources. assignment transformation, and applying the improved D-S evidence theory for fusion early-warning, the final risk early-warning level is obtained. Deformation prediction value as an information source for multi-data fusion can predict the future collapse situation in advance, so that decision makers have enough time to understand the construction site situation and grasp the main factors leading to the risk of collapse so as to target the corresponding measures to reduce the probability of engineering accidents. The proposed method is applied to the Luochongwei station and the following conclusions can be obtained: (1) Limited to the variability of the geological conditions of the pit and the uncertainty of the construction process, it is difficult to judge the early-warning level by the environmental conditions and structural conditions of the pit. Since all kinds of monitoring data are the visual embodiment of the integrated effect of environmental conditions, construction conditions, etc., it is of great significance to study the early warning of pit risk based on monitoring data. (2) Multi-step rolling prediction of deformation trends is performed using a small-sample machine learning approach. The predictions are transformed into basic probability distributions through cloud modelling, providing multiple sources of input information for the fusion early-warning model. The small-sample prediction results in this paper are consistent with the construction site monitoring results, which can provide decision makers with an over-advanced risk prediction signal so that construction workers have sufficient risk emergency response time to reduce the probability of collapse accidents. (3) In order to solve the problems of low credibility of a single source of information and the failure of fusion of highly conflicting evidence from multiple sources, an improved multi-source information fusion early-warning method is proposed. The three sources of information, namely surface settlement, groundwater level, and pile horizontal displacement, are considered together, and the predicted values of multiple monitoring items are fused for early warning based on the newly defined evidence correction parameters and optimized fusion rules. The results show that the method proposed in this paper outperforms the single information source method and the traditional D-S method. The proposed method has high accuracy and effectiveness. The research in this paper can provide a new idea for the construction risk early warning of subway foundation pit engineering. This method can reflect the construction risk state through multiple indicators and multiple angles. At the same time, the predicted displacement is used as an information source for multi-data fusion, which can predict the collapse failure after a few days and provide sufficient reaction time for the construction personnel. The decision makers should fully understand the on-site construction situation, have a preliminary understanding of the main risk factors causing the collapse, and take corresponding measures for specific problems to achieve the right remedy, greatly improving the efficiency of construction.