Abstract
In view of the shortcomings in the risk assessment of deep-buried tunnels, a dynamic risk assessment method based on a Bayesian network is proposed. According to case statistics, a total of 12 specific risk rating factors are obtained and divided into three types: objective factors, subjective factors, and monitoring factors. The grading criteria of the risk rating factors are determined, and a dynamic risk rating system is established. A Bayesian network based on this system is constructed by expert knowledge and historical data. The nodes in the Bayesian network are in one-to-one correspondence with the three types of influencing factors, and the probability distribution is determined. Posterior probabilistic and sensitivity analyses are carried out, and the results show that the main influencing factors obtained by the two methods are basically the same. The constructed dynamic risk assessment model is most affected by the objective factor rating and monitoring factor rating, followed by the subjective factor rating. The dynamic risk rating is mainly affected by the surrounding rock level among the objective factors, construction management among the subjective factors, and arch crown convergence and side wall displacement among the monitoring factors. The dynamic risk assessment method based on the Bayesian network is applied to the No. 3 inclined shaft of the Humaling tunnel. According to the adjustment of the monitoring data and geological conditions, the dynamic risk rating probability of level I greatly decreased from 81.7% to 33.8%, the probability of level II significantly increased from 12.3% to 34.0%, and the probability of level III increased from 5.95% to 32.2%, which indicates that the risk level has risen sharply. The results show that this method can effectively predict the risk level during tunnel construction.
Highlights
With the growth of China’s economy, the demand for infrastructure such as railways is increasing, especially in western China
In view of the shortcomings of dynamic risk analysis, this paper proposes a dynamic risk rating method based on a Bayesian network for deepburied tunnel construction and constructs a dynamic risk rating system
According to the specific conditions of tunnel construction, the specific parameters of the three types of factors are graded according to certain rules, and a dynamic risk rating system structure is constructed; the Bayesian network structure is obtained
Summary
With the growth of China’s economy, the demand for infrastructure such as railways is increasing, especially in western China. Zhang et al [15] proposed a new assessment method based on casebased reasoning, advanced geological prediction, and rough set theory. The method includes a combination of a geological prediction model that allows prediction of geology before tunnel construction and a construction strategy decision model that allows selection of the construction strategy that leads to the least risk Both models are based on Bayesian networks. Gerassis et al [27] presented a methodology for safety prioritization in tunnel construction based on Bayesian analysis of data from occupational accidents recorded in recent years This method has not been verified by engineering. Some scholars have carried out research on the application of Bayesian networks in tunnel construction risk assessment, many shortcomings remain in prior probability acquisition, model construction, and engineering verification. Dynamic risk ratings are achieved based on feedback from monitoring data as construction progresses
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