Realization of historical Persian ornamental and geometric patterns as architectural components in innovative reciprocal frame barrel vaults
Bibliographic record
Abstract
Barrel vaults are one of the most widely used forms in Persian architecture. While they are mainly built with heavy and compressive materials such as brick, today’s advances in construction techniques has led the architectural industry to utilize lightweight structural systems including reciprocal frame structures (RF). The purpose of this paper is to generate a barrel vault form using RFs through a revival of historical Persian ornamental and geometric patterns known as girih. This research was carried out in three phases. After extracting the essential criteria necessary to produce reciprocal configuration, four geometric girih that were compatible with those criteria were selected. The selected patterns were then modified to localize their reciprocal configurations following the Persian ornamental and geometric patterns. A structural analysis was performed using the Karamba Plugin in order to make a structural comparison between a barrel vault constructed with RFs and one made with bricks. The results showed that the use of RFs can significantly reduce the structural weight while using a minimum of material in covering the specified span. In addition, it was concluded that the vault with Hasht-e-moraba and Chahar-lenge ghenas patterns behaved in a more optimal way for the transmission of axial forces with less displacement and deformation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".