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Record W4255749541 · doi:10.1061/9780784481578.056

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

2018· article· en· W4255749541 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
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsRead Jones Christoffersen (Canada)
Fundersnot available
KeywordsHammerPileSelection (genetic algorithm)EngineeringImpact energyStructural engineeringReliability engineeringGeotechnical engineeringMarine engineeringComputer science

Abstract

fetched live from OpenAlex

Typically, impact hammer selection must consider drivability, testing requirements (assuming the same hammer is used for dynamic load testing), and the potential for pile damage, especially where piles are installed into dense or hard strata. There are various guidelines regarding the maximum allowable driving stresses and the maximum recommended driving energy of the impact hammer to avoid damaging steel pipe piles and H-piles during installation under such conditions. In a companion paper, these guidelines were reviewed to develop a preliminary screening tool for selecting an impact hammer to safely install and test steel pipe and H-piles. This paper will examine a case history where an H-pile was damaged during installation. The paper will review the hammer selection against this screening tool and how the pile damage could have been predicted using the screening tool in conjunction with sophisticated simulation and analysis software. The paper will also demonstrate the importance of interpreting the results of a pile driving simulation properly to ensure a reliable prediction of the actual pile driving conditions.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.285

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 integrity0.0000.000
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 head0.042
GPT teacher head0.292
Teacher spread0.250 · 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