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 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.

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Publication Details

Journal
Applied Sciences
Published
2024-08-31
DOI
https://doi.org/10.3390/app14177701
Citations
3
Primary Topic
Civil and Geotechnical Engineering Research
Type
article
Field-Weighted Citation Impact
2.29
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article

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

Tielin Chen, Xuexuan Yin, Hao Jiang, Rong Fan et al.
3 citations
Applied Sciences
Civil and Geotechnical Engineering Research
2.29
article

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

Tielin Chen, Xuexuan Yin, Hao Jiang, Rong Fan, Shunyu Wang
article en
3 citations

Abstract

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.

Applied SciencesVol. 14(17)
Beijing Jiaotong University (CN)
Sustainable cities and communities
Openalex Percentile: Top 12%
Civil and Geotechnical Engineering Research
2.29
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Study on Influencing Factors and Prediction of Tunnel Floor Heave in Gently Inclined Thin-Layered Rock Mass — Tielin Chen, Xuexuan Yin, et al. · Applied Sciences (2024) | TGRS Research Map | TGRS