Security Assessment Of Buildings Closed To The Urban Tunnels

Along with China’s accelerating process of urbanization and incensement of urbanized population, Traffic Congestion has gradually become a critical issue that hinders the further development of the city. Since the land available is limited, the expansion towards the underground has been emphasized frequently. Urban tunnels, as a typical and successful example of underground expansion, has by far made major contribution to the solution of traffic conjunctions. However, these urban tunnels are usually built beneath the busiest CBDs of the city. With concentrated architectures and infrastructure facilities deployed above, the ground subsidence during the building of these tunnels will inevitably brought potential threats to the structures on the surface around, even cause structure failures which would consequently cause major economic loss and negative social impacts.Analyzing the mechanism of how tunnel construction would impact the structures around and conducting corresponding safety assessment, thus proposing preventative risk management protocols has become the new trend and become highly valued by stakeholders from political or industrial sectors. Recognizing the steady mathematical foundation, mature algorithm and software compatibility of Bayesian network, the author decides to apply Bayesian network to the security assessment of city tunnel constructions. Firstly, we establish the Bayesian network according to Event Tree/Fault Tree, making initial feasibility test of the model for the application in structure safety assessment, then apply the model to the real-case simulative calculations and compare the result with the analytical Fuzzy AHP. By doing this the author has obtained the following conclusions:(1) The hypothesis of establishing the Bayesian network according to Event Tree/Fault Tree is practical and has recognizable performance in real case applications. The contingent probability derived from the logic sequence of Event Tree/Fault Tree has seen an alignment between deductive results and prior knowledge;(2) The five risk factors discussed by the article has different structural damage between different Building fortification levels. Even indulged with similar damage, the mechanism could be different, with geographical factors as the dominating element in several cases while structural factors rules the rest of them;(3) Bayesian network exhibits higher efficiency and reliability, while providing more thorough analysis in real case application compared with Fuzzy AHP.However, there would still be some problems during the analysis, and the cause would be a lack of sufficient data to prove the accuracy of the theory. Recognizing this, an effective approach of establishing standardized database containing sample parameters of structures and tunnels, and a reliable plan to further improve the accuracy of the topological structure of Bayesian network through its online self-learning ability would significantly impact the value of this article’s theoretical and applicational value. Which would also remain to be a possible direction for future study...