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Record W4390964657 · doi:10.1139/cgj-2023-0404

Understanding effects from overburden drilling of piles—a rational approach to reduce the impacts on the surroundings

2024· article· en· W4390964657 on OpenAlexvenueno aff
E.J. Lande, Stefan Ritter, Kjell Karlsrud, Steinar Nordal

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsDrillingGeotechnical engineeringHammerGeologyPetroleum engineeringDrilling fluidEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

This paper presents two case studies dealing with undesirable impacts of overburden drilling of casings for end-bearing piles to bedrock. Monitored pore-water pressures and ground settlements are used to document and assess the influence from rotary percussive drilling with “down-the-hole” (DTH) hammers. The studies show that drilling with high-pressure air-driven DTH hammers may cause considerable erosion and soil volume loss adjacent to the drill bit and along the casing, resulting in settlements of the surrounding ground. The risk of soil volume loss increases when the drilling is carried out in erodible soils such as silt and fine sands. The volume loss is found to be caused by a combined air-lift pump effect and a Venturi suction effect. Monitoring pore pressures in the vicinity of the drilling may be used to reduce soil volume loss and prevent damaging settlements. Results from drilling with water-driven DTH hammer showed significantly less ground settlements and influence on pore pressures compared to using an air-driven hammer. The study suggests that the drilling parameters flow rate and penetration rate, and the cross-sectional area of the pile casing can be combined in a non-dimensional methodology to assess the mass balance of drill cuttings when drilling with water flushing. A design framework is suggested to guide overburden drilling in urban settings to reduce potential impact on the surroundings.

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)0.001
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 score0.781
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.038
GPT teacher head0.226
Teacher spread0.187 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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