Resilience Assessment of Deep Excavation Engineering Systems Based on Bayesian Network

Abstract

As a key component of infrastructure, the construction safety of deep excavation is particularly important. In order to improve the safety and stability of Deep excavation construction, research on the risk resistance and rapid recovery capabilities of Deep excavation projects is carried out. By introducing the concept of resilience, we analyze the key factors that affect the safety and resilience of Deep excavation projects, use Bayesian networks to establish a resilience model that expresses the causal relationship between factors, and learn the parameters of the network model through data samples collected through surveys; by adjusting the network Analyze the relationship between each factor and system resilience at each node, and clarify the measures that should be taken and key control objects, thereby improving the system resilience and ensuring that normal construction can be carried out safely and orderly. Research shows that this model can be used to quantify the safety toughness value of deep excavation and reason and analyze the impact of various factors on the toughness value, thereby improving risk management and decision support capabilities.