Numerical Investigation of the Pull-Out and Shear Mechanical Characteristics and Support Effectiveness of Yielding Bolt in a Soft Rock Tunnel

Conventional bolts frequently fail under large deformations due to stress concentration in soft rock tunnels. In contrast, yielding bolts incorporate energy-absorbing mechanisms to sustain controlled plastic deformation. This study employed FLAC3D to numerically investigate the pull-out, shear, and bending behaviors of yielding bolts, evaluating their support effectiveness in soft rock tunnels. Three-dimensional finite difference models incorporating nonlinear coupling springs and the Mohr–Coulomb criterion were developed to simulate bolt–rock interactions under multifactorial loading. Validation against experimental pull-out tests and field measurements confirmed the model accuracy. Under pull-out loading, the axial forces in yielding bolts decreased more rapidly along the bolt length, reducing stress concentration at the head. The central position of the maximum load-bearing capacity in conventional bolts fractured under tension, resulting in an hourglass-shaped axial force distribution. Conversely, the yielding bolts maintained yield strength for an extended period after reaching it, exhibiting a spindle-shaped axial force distribution. Parametric analyses reveal that bolt spacing exerts a greater influence on support effectiveness than length. This study bridges critical gaps in understanding yielding bolt behavior under combined loading and provides a validated framework for optimizing energy-absorbing support systems in soft rock tunnels.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2025-06-19
DOI
https://doi.org/10.3390/app15126933
Primary Topic
Rock Mechanics and Modeling
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Numerical Investigation of the Pull-Out and Shear Mechanical Characteristics and Support Effectiveness of Yielding Bolt in a Soft Rock Tunnel

Mingbo Chi, Yan Zhu, Yanyan Tan, Ersheng Zha et al.
Applied Sciences
Rock Mechanics and Modeling
article

Numerical Investigation of the Pull-Out and Shear Mechanical Characteristics and Support Effectiveness of Yielding Bolt in a Soft Rock Tunnel

Mingbo Chi, Yan Zhu, Yanyan Tan, Ersheng Zha, Yuwei Zhang
article en

Abstract

Conventional bolts frequently fail under large deformations due to stress concentration in soft rock tunnels. In contrast, yielding bolts incorporate energy-absorbing mechanisms to sustain controlled plastic deformation. This study employed FLAC3D to numerically investigate the pull-out, shear, and bending behaviors of yielding bolts, evaluating their support effectiveness in soft rock tunnels. Three-dimensional finite difference models incorporating nonlinear coupling springs and the Mohr–Coulomb criterion were developed to simulate bolt–rock interactions under multifactorial loading. Validation against experimental pull-out tests and field measurements confirmed the model accuracy. Under pull-out loading, the axial forces in yielding bolts decreased more rapidly along the bolt length, reducing stress concentration at the head. The central position of the maximum load-bearing capacity in conventional bolts fractured under tension, resulting in an hourglass-shaped axial force distribution. Conversely, the yielding bolts maintained yield strength for an extended period after reaching it, exhibiting a spindle-shaped axial force distribution. Parametric analyses reveal that bolt spacing exerts a greater influence on support effectiveness than length. This study bridges critical gaps in understanding yielding bolt behavior under combined loading and provides a validated framework for optimizing energy-absorbing support systems in soft rock tunnels.

Applied SciencesVol. 15(12)
China University of Mining and Technology (CN), China Academy of Safety Sciences and Technology (CN)
National Natural Science Foundation of China, National Science and Technology Major Project
Openalex Percentile: Top 84%
Rock Mechanics and Modeling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.