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Record W3162601294 · doi:10.1155/2021/8815564

Study of Two‐Step Parallel Cutting Technology for Deep‐Hole Blasting in Shaft Excavation

2021· article· en· W3162601294 on OpenAlexaff
Renshu Yang, Changda Zheng, Liyun Yang, Jinjing Zuo, Tonglei Cheng, Chenxi Ding, Qing Li

Bibliographic record

VenueShock and Vibration · 2021
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsGeomechanica (Canada)
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsRock blastingExcavationDeep holeEngineeringMining engineeringMechanical engineeringEngineering drawingComputer scienceGeotechnical engineering

Abstract

fetched live from OpenAlex

In hard rock deep‐hole blasting excavation, blastholes are rarely utilized due to the clamping effect of the lower rock, which affects excavation progress and restricts the development and application of deep‐hole blasting technology. Cut blasting is a key to improving excavation speed. In this paper, a new cutting method designating the two‐step cutting technology was presented. The blasthole was divided into upper and lower sections without changing the blasthole layout. The upper section was detonated first, creating sufficient free surface for the lower section, which was detonated secondly. This created a larger cavity and improved blasthole utilization. Results showed good blasting effects for two‐step cutting technology through theoretical analysis and engineering applications. The blasthole utilization rate was 96.1% when the upper and lower specific charge ratio = 0.78. This paper provides a good reference for resolving the low blasthole utilization problem in deep‐hole blasting of vertical shafts within a hard rock.

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: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.266

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 head0.251
Teacher spread0.237 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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