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Record W3195984256 · doi:10.11159/ijci.2021.020

Network Method As A Tool To Study The Influence Of The Position Of A Sheet Pile Under A Dam On Pore Pressure And Groundwater Flow

2021· article· en· W3195984256 on OpenAlexvenueno aff
Encarnación Martínez‐Moreno, Iván Alhama, Gonzalo García‐Ros

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsSheet pilePosition (finance)GroundwaterPileGeotechnical engineeringGroundwater flowGeologyFlow (mathematics)MechanicsBusinessAquiferPhysics

Abstract

fetched live from OpenAlex

Before building gravity dams, some verifications must be carried out in order to check its safety, most of them related to groundwater flow and pore pressure distribution at the base of the structure. By placing a sheet pile under the dam, these phenomena, connected to piping and the value and location of the uplift force, can be controlled. These variables are usually studied considering the soil as isotropic, which simplifies the problems to obtain universal solutions, whether these are graphical or analytical. Nevertheless, assuming this simplification, real scenarios are not reflected, for example anisotropic hydraulic conductivities. In this paper, the effect of the location of a pile under the dam in different scenarios is studied: dam without a sheet pile and dam with a sheet pile located at the heel, centre and toe of the structure, modelling two different media in all cases, with isotropic and anisotropic hydraulic conductivities. In this way, the change in safety due to the pile and anisotropy can be considered. The scenarios are simulated with a tool based on the network method, with which, employing the electrical analogy, the variables of the problem (hydraulic potential, h, and groundwater flow, Q) are obtained by solving electric quantities voltage (V) ad electric current (I), respectively.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.252

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 head0.005
GPT teacher head0.234
Teacher spread0.229 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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