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Record W2803295630 · doi:10.1139/cgj-2018-0131

Evaluation of lateral response of single piles to adjacent excavation using data from cone penetration tests

2018· article· en· W2803295630 on OpenAlexvenueno aff
Hongjiang Li, Songyu Liu, Liyuan Tong

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsExcavationGeotechnical engineeringPileBending momentBearing capacityPenetration testEngineeringGeologyStructural engineeringSubgrade

Abstract

fetched live from OpenAlex

Excavation inevitably induces stress changes in the surrounding soil, which causes significant lateral movements, additional forces, and bending moments in the existing pile foundations. To prevent or minimize damage to adjacent piles, this paper presents an actual full-scale instrumented study to examine the lateral response of existing piles to an adjacent test pit excavation. Cone penetration tests (CPTs) near the test piles before and after excavation are conducted and compared. A simple p–y evolution model for lateral piles during excavation is proposed. The proposed model is defined by the pre-excavation and post-excavation p–y curves from CPT data, which can provide better predictions by comparison with the field measured results. Additionally, for analysis of the pile behaviour after excavation, the observed lateral bearing capacity of full-scale tests are compared with those computed by the pre-excavation and post-excavation p–y curves. The post-excavation p–y curve can generally give a satisfactory prediction of the residual pile bearing capacity after excavation. The calculated results from the free-field cone parameters have a serious overestimation and are detrimental to the service design of pile foundations.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.066
GPT teacher head0.282
Teacher spread0.216 · 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

Citations25
Published2018
Admission routes1
Has abstractyes

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