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Record W4319068586 · doi:10.1139/cgj-2022-0053

Evaluation of prediction models for tip resistances of rock-socketed drilled shafts

2023· article· en· W4319068586 on OpenAlexvenueno aff
Anjerick Topacio, Chong Tang, Yit‐Jin Chen, Kok‐Kwang Phoon

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDisplacement (psychology)Geotechnical engineeringGeologyStructural engineeringMathematicsEngineering

Abstract

fetched live from OpenAlex

This study compiled a database called CYCU/RockTip/51 consisting of 51 rock-socketed drilled shafts installed at different sites worldwide, covering a wide variety of rock properties and shaft geometries. The tip resistances from seven representative prediction models were compared with the measured values. These measured values were obtained from the load–displacement curves of field load tests using three interpretation criteria. It was found that the prediction models of Teng, Coates, Rowe and Armitage, and ARGEMA overpredicted the measured capacity, while Zhang and Einstein, Vipulanandan et al. and Zhang are less biased. These tip prediction models also could be classified according to displacement requirements. The proposed tip prediction models of this study are presented based on different interpretation methods and the displacement ranges that they are mobilized. Finally, the normalized load–displacement curve was fitted to the hyperbolic curve with two model parameters ( a and b). The parameters a and b define the reciprocal of the initial slope and the asymptotic resistance, respectively. The statistics of ( b, a) for rock-socketed drilled shafts in CYCU/RockTip/51 are mean = (0.76, 1.09), COV = (0.13, 0.59), and correlation coefficient = −0.79. These statistics are useful for reliability-based 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 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)0.002
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 score0.861
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.026
GPT teacher head0.239
Teacher spread0.213 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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