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Monitoring blasting-induced vibrations in a Québec clay deposit

2025· article· en· W4412904871 on OpenAlexaffabout
Sarah Bouchard, Antony Gagné, Mourad Karray

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2025
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversité de SherbrookeMinistère des Transports
Fundersnot available
KeywordsRock blastingBrecciaVibrationGeologyGeotechnical engineeringGeochemistryPhysicsAcoustics

Abstract

fetched live from OpenAlex

Abstract Blasting operations carried out as part of road projects can generate vibrations that affect the stability of surrounding slopes. However, the effects of these vibrations on clay soils remain poorly documented. To date, few cases of landslides or major deformations have been recorded in these types of soils following blasting. Currently, blasting is controlled by peak particle velocity (PPV) thresholds. In Queébec, existing regulations are becoming increasingly restrictive, leading to a significant increase in blasting costs. This article is a continuation of the recommendations from previous studies, which highlighted a lack of field data on vibrations caused by blasting in sensitive clay deposits, as well as the absence of a clear methodology for carrying out measurements (number of devices, distances between recording sites, etc.). One of the objectives of this article is to show vibration recording data produced by blasting activities in clays in order to gain a better understanding of these problems. As part of this project, two blasting operations were carried out near a clay deposit. Various installation devices and techniques were used to measure vibrations and are presented in this article. These field tests highlighted limitations in the surface recordings, which affect our understanding of blasting wave propagation mechanisms in clay soils. These understanding of blasting wave propagation mechanisms in clay soils. These limitations will be discussed in detail and provide the basis for a subsequent phase of research.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.392

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 head0.012
GPT teacher head0.207
Teacher spread0.196 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes2
Has abstractyes

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