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Record W4408805427 · doi:10.37308/dfi49.20241320321

The Use of BDSLT for Design of Large Capacity Cast-In-Place Piles

2024· article· en· W4408805427 on OpenAlexaboutno aff
Alper Turan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMaterials Engineering and Processing
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

Cast-in-place concrete piles socketed in rock are a commonly used foundation solution in the province of Ontario. Although cast-in-place concrete piles are common, limited pile load testing has been undertaken on these foundations to confirm shaft friction (side shear) and end bearing parameters. Particularly, the large diameter piles bearing on rock makes conventional pile loads tests impractical and costly due to large capacities that need to be mobilized during testing. Performance of piles loads test come with the advantage of confirming the assumed geotechnical design parameters and permitting the use the larger strength reduction factor as per LRFD design. This paper presents the preparation, execution, and result interpretation of a pile load test carried out on two Cast-in-place concrete piles with diameters of 1.5m and 1.8m for Garden City Skyway Project in the City of St Catherines Ontario, Canada. The 1.5m and 1.8m diameter test piles were socketed 4m and 6m into shale bedrock. Osterberg cells were used in both load tests to provide end bearing and shaft friction parameters for the shale bedrock as well as overlying layers of till and soft clay. The results of the load test provided valuable insight into the load settlement performance exhibited by these piles. The results are also compared with the analytical estimates of the pile capacities presented in geotechnical report as well as the results of a number of other analytical socket design approaches.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.244
Teacher spread0.183 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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Same topicMaterials Engineering and ProcessingFrench-language works237,207