Failure characteristics of unloading red shale with different bedding angles based on the particle discrete element

Understanding the unloading failure behavior of layered red shale is crucial for the stability of deep roadways and other underground structures. This study combines triaxial unloading experiments with discrete element method (DEM) simulations to reveal the mechanical, acoustic emission (AE), and meso-mechanical responses of red shale with different bedding orientations.Laboratory tests demonstrate that unloading reduces peak strength by 15-25% compared with conventional triaxial loading, and bedding angle critically controls the failure mode: 0° specimens exhibit shear-dominated failure, whereas 90° specimens tend to split in tension. The DEM model, calibrated with experimental results (error < 2%), reproduces stress-strain behavior and captures the brittle-ductile transition.AE analysis shows concentrated activity under low confining pressures and continuous ductile emissions under high pressures, reflecting the correlation between AE characteristics and crack evolution. Force-chain network and fabric anisotropy analysis reveal that unloading accelerates crack coalescence and structural reorganization, with fabric anisotropy increasing by more than 25-fold.This integrated framework (AE + DEM + fabric evolution) provides a robust basis for evaluating unloading-induced instability. The findings advance understanding of bedding-controlled failure in red shale and offer practical guidance for roadway support optimization and underground engineering safety, with potential extension to related fields such as shale gas development.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-01-03
DOI
https://doi.org/10.1038/s41598-025-31022-0
Primary Topic
Rock Mechanics and Modeling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Failure characteristics of unloading red shale with different bedding angles based on the particle discrete element

Zhu Yi, Changlun Sun, H. Wang, Yu Bao et al.
Scientific Reports
Rock Mechanics and Modeling
article

Failure characteristics of unloading red shale with different bedding angles based on the particle discrete element

Zhu Yi, Changlun Sun, H. Wang, Yu Bao, Chunhong Mao, Zhenqian Ma
article en

Abstract

Understanding the unloading failure behavior of layered red shale is crucial for the stability of deep roadways and other underground structures. This study combines triaxial unloading experiments with discrete element method (DEM) simulations to reveal the mechanical, acoustic emission (AE), and meso-mechanical responses of red shale with different bedding orientations.Laboratory tests demonstrate that unloading reduces peak strength by 15-25% compared with conventional triaxial loading, and bedding angle critically controls the failure mode: 0° specimens exhibit shear-dominated failure, whereas 90° specimens tend to split in tension. The DEM model, calibrated with experimental results (error < 2%), reproduces stress-strain behavior and captures the brittle-ductile transition.AE analysis shows concentrated activity under low confining pressures and continuous ductile emissions under high pressures, reflecting the correlation between AE characteristics and crack evolution. Force-chain network and fabric anisotropy analysis reveal that unloading accelerates crack coalescence and structural reorganization, with fabric anisotropy increasing by more than 25-fold.This integrated framework (AE + DEM + fabric evolution) provides a robust basis for evaluating unloading-induced instability. The findings advance understanding of bedding-controlled failure in red shale and offer practical guidance for roadway support optimization and underground engineering safety, with potential extension to related fields such as shale gas development.

Scientific ReportsVol. 16(1)
Guizhou University (CN), Guizhou Minzu University (CN), Guizhou Institute of Technology (CN)
Sustainable cities and communities
Openalex Percentile: Top 98%
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.