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Record W4416186692 · doi:10.1016/j.undsp.2025.07.002

Excavation-induced fracturing mechanisms in deep hard rock: A hierarchical block model

2025· article· en· W4416186692 on OpenAlexaboutno aff
Shili Qiu, Shirui Zhang, Quan Jiang, Yi Fang, Ping Li, Shaojun Li, Ya-Xun Xiao, Dingping Xu

Bibliographic record

VenueUnderground Space · 2025
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSpallBlock (permutation group theory)ExcavationFracture (geology)Discrete element methodBlock modelRock mass classificationComputer simulation

Abstract

fetched live from OpenAlex

The fracture characteristics of the excavation damage zones (EDZs) of deeply buried tunnels are closely related to energy evolution, and they are highly valuable for support design. Advanced numerical simulation techniques have shown the potential for evaluating the EDZ properties. On the basis of the finite-discrete element method (FDEM) and Poisson’s random block generation technique, the virtual block model (VBM) is proposed to characterize the intact rock masses surrounding tunnels. Moreover, a virtual block upscale principle (VB-UP) is proposed to determine the geometric and meso-mechanical parameters. The Canadian Underground Research Laboratory (URL) and China Jinping Underground Laboratory Phase II (CJPL-II) project excavations are simulated, and the excavation-induced fracture characteristics of the surrounding rock masses are analyzed in detail. The VBM captures the tensile, shear, and mixed fracture properties under excavation-induced confining pressure evolution. Then, the thicknesses of the spalling rock slabs in Lab #7 of the CJPL-II project are evaluated via the Otsu method. Combined with onsite monitoring data, the validity and advancement of the VBM are verified. This study expands the applicability of the FDEM and provides a new method for assessing the EDZs of surrounding rocks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.229
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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