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Record W2269923565

Historical Development of Arch Dams: From Cut-stone Arches to Modern Cconcrete Designs

2001· article· en· W2269923565 on OpenAlexaboutno aff
Hubert Chanson, DPatrick James

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsArchButtressArch damAbutmentEngineeringGeotechnical engineeringForensic engineeringCivil engineeringStructural engineering
DOInot available

Abstract

fetched live from OpenAlex

Dam designs may be divided into three main types : gravity structures relying on their weight for stability, arch structures using the abutment reaction forces and buttress dams. The design of an arch dam relies on the abutment reaction forces to resist the water pressure force and it requires advanced engineering expertise. The present study demonstrates that the historical development of arch dams took place in five stages. The world's oldest arch dams were built by the Romans in France and Spain. The Mongols who built dams in Iran during the 13th and 14th centuries followed them. However it is not until the 19th century that significant progress in arch dam design was made. Four remarkable structures were the Meer Allum dam (India 1804), the Jones Falls dam (Canada 1831), the Zola dam (France 1854) and Parramatta dam (Australia 1856). Australian engineers pioneered the use of concrete as a construction material for arch dams (ie. 75-Miles and Lithgow No. 1 dams). Modern concrete arch dam designs were introduced in North America at the beginning of the 20th century: eg. constant-angle arch, doublecurvature arch. Since then no major design breakthrough has taken place and modern arch dams are based upon the single-radius, constant-angle or double-curvature arch design. It is the writers' opinion that the introduction of concrete as construction material marked a major innovation in allowing flexibility in arch shape design.

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: none
Teacher disagreement score0.514
Threshold uncertainty score0.604

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 head0.047
GPT teacher head0.241
Teacher spread0.194 · 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
Published2001
Admission routes1
Has abstractyes

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