Historical Development of Arch Dams: From Cut-stone Arches to Modern Cconcrete Designs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".