Study on Influencing Factors and Prediction of Tunnel Floor Heave in Gently Inclined Thin-Layered Rock Mass

In recent years, the construction of new railway tunnels worldwide has become increasingly challenging due to larger cross-sections, deeper burial depths, higher in situ stress, and more complex geological conditions. During both construction and operation, some tunnels have encountered significant...

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Veröffentlicht in:Applied sciences 2024-09, Vol.14 (17), p.7701
Hauptverfasser: Fan, Rong, Chen, Tielin, Wang, Shunyu, Jiang, Hao, Yin, Xuexuan
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Sprache:eng
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Zusammenfassung:In recent years, the construction of new railway tunnels worldwide has become increasingly challenging due to larger cross-sections, deeper burial depths, higher in situ stress, and more complex geological conditions. During both construction and operation, some tunnels have encountered significant issues with floor heave. This paper begins by identifying the primary causes of deformation and instability in tunnel floor structures through an investigation and statistical analysis. It then examines floor heave across more than 20 railway lines, summarizing the types, generation mechanisms, and mechanical models associated with this issue. Additionally, extensive survey data indicate that tunnel floor heave is most likely to occur in gently inclined thin-layered rock masses. Therefore, using a tunnel passing through the plate suture zone in such a rock mass as a case study, numerical simulations, theoretical analyses, and on-site monitoring were conducted. This study systematically analyzed the influence of single and multiple factors, as well as the mechanical behavior of the support system, on tunnel floor heave in gently inclined thin-layered surrounding rock. Furthermore, several key models were proposed: a tunnel floor heave estimation and load formula based on a mechanical model, a dynamic relationship between surrounding rock support force and tunnel floor heave using the Nishihara model, a tunnel floor settlement estimation formula based on deformation statistics, and a tunnel floor heave energy prediction model utilizing the B-P neural network algorithm. These conclusions have been validated and widely applied in practical engineering, providing a robust theoretical foundation and technical support for future tunnel construction.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14177701