Abstract
The environmental conditions in tunnel structures are extremely complex, and the mechanism between supporting structures and the surrounding rock is not fully understood because its parameters are random variables. In this paper, reliability analysis and the Monte Carlo method were employed to develop a stochastic finite element method to analyze the safety of tunnel structures in different rock conditions, with which the reliability of a large-span station tunnel lining system was analyzed, based on a real project of China. The results show that: the system reliability of the tunnel decrease with the increase of the surrounding rock class. Under II to IV-class surrounding rock conditions, the location of the minimum reliable indicators is in the arch springing and the haunch, and only a small section of the tunnel lining structure cannot reach the requirements of the reliable indicator, whereas the lower limit of reliable indicator can meets the specification requirements. However, under the condition of V-class rock, the minimum reliable indicator is located in the vault, the lower limit of reliable indicator does not meet the specification requirements. Considering that it is impossible for the sections to achieve complete correlation actually, thus, the reliability index of the actual structural system must be greater than the lower limit value, the railway tunnels are reasonably designed.
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Acknowledgements
This work was supported by The National Natural Science Foundation of China under Grants 51678578 & 51108472; The Guangdong Natural Science Foundation of China under Grant 2016A030313233; The Guangdong Provincial Science & Technology Program of China under Grant 2015A020217004; The Guangzhou Science & Technology Program of China under Grant 201704020139; and The Department of Communications of Guangdong Province of China under Grant 2016-02-026.
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Huang, L., Huang, S., Tao, C. et al. The finite element method for the reliability analysis of lining structures based on Monte Carlo stochastic. Cluster Comput 20, 3313–3325 (2017). https://doi.org/10.1007/s10586-017-1073-3
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DOI: https://doi.org/10.1007/s10586-017-1073-3