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Use of Geosynthetics to Aid Construction Over Soft Soils – Successes and Cautions

2012· article· en· W2015596744 on OpenAlexaff
R. Kerry Rowe, C. Taechakumthorn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeosyntheticsSoil waterGeotechnical engineeringEnvironmental scienceEngineeringCivil engineeringForensic engineeringSoil science

Abstract

fetched live from OpenAlex

Geosynthetic reinforcement and prefabricated vertical drains (PVDs), both separately and in combination, have substantially improved the engineer's ability to cost-effectively construct embankments over difficult and soft soils. This paper explores the reason for the success and the benefits that can be achieved using geosynthetics, as a basal reinforcement, and PVDs to increase short-term stability of embankments and to accelerate consolidation and long-term strength gain. The paper examines both embankments on fibrous peats and a range of soft clays. It demonstrates that good geotechnical engineering is at least as important with the use of geosynthetics as it is without their use. For example, geosynthetic basal reinforcement can be extremely effective in allowing timely construction of embank· ment over peats and soft clays - however an understanding of the mechanism at work and the key engineering properties is essential for consistent success. Not all soils are the same and likewise not all geosynthetic reinforcement is the same. The paper discusses the key mechanism and engineering properties of both soil and reinforcement critical to success. It highlights not only the advantages but also the limitations of different types of reinforcement for different types of soil. It provides field examples, supported by numerical analysis, of great success in the use of geosynthetics - and failures. The paper discusses where both can be anticipated. Finally the paper explores the role of the rate·sensitivity of both reinforcement and soil on the long-term performance of embankments over difficult soils.

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)0.000
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: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.211
Teacher spread0.197 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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