## 6 Conclusions  
This study proposed a probabilistic prediction model for the construction risk of DFPs adjacent to existing underpass tunnels using the N-K model and dynamic Bayesian network. The Nanning underground comprehensive utilization project was taken as an example. The risk inference analysis of the DFP construction adjacent to the existing underpass tunnel was conducted from three aspects: probability prediction, sensitivity analysis, and risk diagnosis, which verified the reasonableness and superiority of the model. The following conclusions were drawn:  
(1) Using the N-K model, this study quantified the coupling values of various risk factors in the construction accident of DFP of adjacent existing underpass tunnels in different ways: T4 > T33 > T32 > T21 > T23 > T34 > T31 > T22 > T26 > T24 > T25. The results indicated that the more risk factors involved in the coupling, the greater the coupling value. However, a phenomenon where the double factor coupling value is greater than the multi-factor coupling value also appeared, which need to be paid attention to. This study optimized the dynamic Bayesian network model according to the order of the coupling values of risk factors. (2) Based on the construction data of the DFP project adjacent to the existing underpass tunnel, this study used association rule mining for risk correlation analysis to identify the set of frequent items and the correlation between risks requiring special attention. Based on these correlations and the calculated risk coupling results of the N-K model, a dynamic Bayesian network model was established in this study. It analyzed the node transfer probability of a dynamic Bayesian network using the Markov model. Actual monitoring data were employed to verify the model. The findings showed that the model's accuracy for risk prediction exceeded 95% and could pass the verification of three axioms, demonstrating the reasonableness of the model. (3) Through the dynamic Bayesian network coupling risk causal inference, the probability of risk events occurring during the construction of DFP of adjacent existing underpass tunnels can be predicted at various times when the risk factors were in different states. In addition, sensitivity analysis could also be applied to determine the most critical sensitive factor to the risk event among many risk factors at different times. Finally, if an accident occurs, risk diagnosis could be performed according to the model structure and parameters to define the most likely cause combination at a specific time and state. (4) The results revealed that: (I) there was uneven excavation and unloading (UEU), the slope was too steep (RSS), the groundwater level was too high (HGL), the leakage occurred in the sealing curtain (LOSC), and the building inclination and damage (BID) should be focused on. (II) Risk probability prediction, sensitivity analysis, and risk diagnosis would be affected by dynamic nodes and changed with time, which showed the necessity of introducing dynamic nodes. (III) The risk diagnosis function of the dynamic Bayesian network model for DFP adjacent to an existing underpass tunnel can accurately and quickly identify the accident-causing risk factors, thereby significantly improving the efficacy of handling safety accidents. **Author Contributions:** Supervision, J.J. and X.O. ; Writing--original draft, G.L. ; Writing--review & editing, G.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 52068004), the Natural Science Foundation of Guangxi Province (Grant No. 2018 GXNSFAA050063), and the Key Research Projects of Guangxi Province (Grant No. AB19245018). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author.