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Record W2143302481 · doi:10.1139/t08-029

Discussion of “A procedure for the design of protective filters”Appears in Canadian Geotechnical Journal,<b>44</b>: 490–495.

2008· article· en· W2143302481 on OpenAlexvenueaboutno aff
F. Delgado, J. M. Poyatos

Bibliographic record

VenueCanadian Geotechnical Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsGradationInternal erosionPermeability (electromagnetism)Geotechnical engineeringCohesion (chemistry)Filter cakeConsolidation (business)Filter (signal processing)CloggingEngineeringMathematicsLeveeComputer scienceEnvironmental engineeringPhysics

Abstract

fetched live from OpenAlex

According to the International Commission of Large Dams (ICOLD 1994), two fundamental functions are required for the filters in embankment dams and other hydraulic structures: (i) retention function, in which the filter must prevent the migration of base soil particles; and (ii) permeability function, in which the filter must accept the seepage flows from adjacent foundation or fill materials without the buildup of excess hydrostatic pressure. Therefore, the filter must be not only fine enough to prevent the erosion of the base soil but also coarse enough to be many times more pervious than the base soil. For this reason, two main criteria have been used separately in the design of granular filters: the retention criterion and the permeability criterion. Moreover, there are other conditions that the filter must meet: (i) it must not segregate, (ii) it must not change in gradation, (iii) it must not have apparent or real cohesion, (iv) it must be internally stable, and (v) it must have sufficient discharge capacity. Much research work has been done in relation to the retention criterion, mainly based on laboratory tests. These so-called empirical retention criteria usually compare the filter and base soil particle-size distributions (PSDs) (Sherard and Dunnigan 1989; Honjo and Veneziano 1989; Foster and Fell 2001), but other empirical retention criteria are based on a comparison between the filter permeability and the base soil particle sizes (Vaughan and Soares 1982; Delgado et al. 2006) because the permeability indirectly takes into account not only the whole PSD of the filter, but also other important characteristics such as compaction, particles shape, and porosity. Recently, important results have been found for retention criteria based on the fundamental physics of filtration. The so-called analytical retention criteria have been developed by Locke and Indraratna (2002), Lone et al. (2005), Indraratna et al. (2007), and others. For example, Indraratna et al. considered the constriction-size distribution (CSD) of the filter and also incorporated the effect of nonuniformity of the cohesionless base soil. Their analytical model has been verified by experimental data. In the case of permeability criterion, if the permeability of the base soil is known, for example from a permeameter test, the well-known theories of filtration indicate that the filter permeability should be at least 25 or even 100 times higher (ICOLD 1994). This criterion can be considered analytical, but sometimes the permeabilities of both the filter and base soil are estimated from their PSD (e.g., k 1⁄4 0:35ðD15Þ, Sherard et al. 1984), which is why the empirical permeability criterion D15f > 4D15b is normally used (ICOLD 1994; NRCS 1994). The authors have presented a procedure for the design of protective filters based on an analytical solution that takes into account factors like pore size, permeability, and factor of safety against soil boiling conditions. This analytical solution is offered as a single equation that would be able to substitute both the traditional retention criterion and the permeability criterion.

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: none
Teacher disagreement score0.940
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.002
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 head0.016
GPT teacher head0.209
Teacher spread0.193 · 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

Citations2
Published2008
Admission routes2
Has abstractyes

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