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Record W2131710373 · doi:10.1139/l03-075

An evaluation of pile cap design methods in accordance with the Canadian design standard

2004· article· en· W2131710373 on OpenAlexvenueaboutno aff
William Cavers, Gordon A. Fenton

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPileLimit state designDesign methodsStructural engineeringEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

There are a number of design methods that have been described for the design of pile caps, but there has been no consensus on which method provides the best approach for the working designer. This paper describes a study conducted to establish the performance of several pile cap design methods, particularly with respect to the Canadian standard, CSA A23.3-94. Previous research was examined to determine the basis of the design methods and the state of current research. The design methods identified were then applied to pile caps for which test data were available. The theoretical loads obtained using the various design methods were compared with the experimental loads. The results of this study indicate that two design models of the five examined are the most suitable. This study also indicates that the provisions of the Canadian design standard are adequate. A possible refinement of the strut-and-tie model incorporating a geometric limit is also outlined.Key words: building codes, footings, pile caps, reinforced concrete, structural design.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.776
Threshold uncertainty score0.446

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.057
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.269
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2004
Admission routes2
Has abstractyes

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