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Record W2022089002 · doi:10.1139/t00-083

A new socket roughness factor for prediction of rock socket shaft resistance

2001· article· en· W2022089002 on OpenAlexvenueno aff
Julian P. Seidel, Benjamin Collingwood.

Bibliographic record

VenueCanadian Geotechnical Journal · 2001
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsPileSurface finishGeotechnical engineeringCompressive strengthSurface roughnessStructural engineeringShear strength (soil)Computer scienceEngineeringGeologyMechanical engineeringMaterials science

Abstract

fetched live from OpenAlex

Prediction of rock socket shaft resistance is a complex problem. Conventional methods for predicting the peak shaft resistance are typically empirically related to unconfined compressive strength through the results of pile load tests. It is shown by reference to international pile socket databases that the degree of confidence which can be applied to these empirical methods is relatively low. Research at Monash University has been directed at understanding and then modelling the complex mechanisms of shear transfer at the interface between the socketed piles and the surrounding rock. Important factors that affect the strength of pile sockets have been identified in laboratory and numerical studies. With a knowledge of the effect of these factors, the reasons for the large scatter around traditional empirical correlations can be deduced. A computer program called ROCKET has been developed which encompasses all aspects of the Monash University rock socket research. This program has been used to develop design charts for rock-socketed piles based on unconfined compressive strength and a nondimensional factor which has been designated the shaft resistance coefficient (SRC). Implementation of the SRC method in design requires an estimate of the likely socket roughness to be made. Very few researchers or practitioners have measured socket roughness, so there is little available guidance in selection of appropriate values. Although many socket load tests are described in the technical literature, the physical parameter which is regularly missing is the socket roughness. With a knowledge of the shaft resistance, and an estimate of all other relevant parameters, the authors have been able to back-calculate the apparent socket roughness using the SRC method. Based on the back-calculated roughness data, socket roughness guidelines for use in analysis and design of rock sockets have been proposed. Using these roughness guidelines, it is shown that the SRC method is able to predict the scatter observed in previously published international load test databases.Key words: rock socket, drilled shaft, shaft resistance, roughness, shaft resistance coefficient.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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 designBench or experimental
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

Citations93
Published2001
Admission routes1
Has abstractyes

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