MétaCan
Menu
Back to cohort
Record W2805429131 · doi:10.1061/9780784481578.049

Development of a Screening Tool for Impact Hammer Selection for Installation, Testing and Damage Mitigation of Steel Pipe and H-Piles

2018· article· en· W2805429131 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIFCEE 2018 · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsRead Jones Christoffersen (Canada)
FundersNewcastle University
KeywordsHammerPileSelection (genetic algorithm)Structural engineeringEngineeringDynamic testingGeotechnical engineeringComputer science

Abstract

fetched live from OpenAlex

Typically, impact hammer selection must consider drivability, testing requirements (assuming the same hammer is used for high strain dynamic testing), and the potential for pile damage, especially where piles are installed into dense or hard strata. Criteria for minimum, preferred and maximum hammer size required for pile installation, testing, and damage mitigation will be reviewed and summarized. Maximum driving stresses and the effect of pile cross section relative dimensions will be discussed. The paper will provide suggested guidelines for a preliminary screening tool for selecting an impact hammer to safely install and test steel pipe and H-piles, and illustrate this through a brief case study. A companion paper will examine in depth a recent case history where an H-pile was damaged during installation and testing, make use of the screening tool and advanced pile driving simulation software to show how the pile damage could have been predicted.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.332

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 head0.023
GPT teacher head0.266
Teacher spread0.243 · 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