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Record W4225143290 · doi:10.11159/icgre22.179

Cellular Cofferdams for Hydropower Generation

2022· article· en· W4225143290 on OpenAlexvenueno aff
Marte Gutierrez, Simon Heru Prassetyo

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsnot available
FundersOffice of Energy EfficiencyOffice of Energy Efficiency and Renewable EnergyU.S. Department of Energy
KeywordsCofferdamHydropowerComputer scienceEngineeringElectrical engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

A comprehensive study is conducted on the potential use of cellular cofferdams as basis for the design and construction of water retaining structures for sustainable and cost-effective hydropower generation. Previously, cellular cofferdams have been widely used mainly as temporary water exclusion devices to permit dry construction of in-water structures such as dams, locks, bridge footings and piers, and hydroelectric power plants. Design and construction requirements for cellular cofferdams are less stringent than for hydropower dams. To make cellular cofferdams suitable for permanent hydropower use, different design concepts that utilize cellular cofferdams as the main or core element of the water-retaining dam structure are proposed. One particular key design concept is the socalled "dry construction technique" in which the granular fill in cofferdam cells and the downstream berm are permanently kept dry in contrast to the wet construction technique for temporary use of cellular cofferdams. The viability of the proposed permanent cellular cofferdam design concepts is demonstrated using well-established structural and geotechnical design procedures and computational modeling.

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 score0.325
Threshold uncertainty score0.600

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 head0.004
GPT teacher head0.159
Teacher spread0.155 · 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
Published2022
Admission routes1
Has abstractyes

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