Simulation of Daylighting Conditions in a Virtual Underground City
Bibliographic record
Abstract
From the Piranesi fantastic architectures to the animation movies and video games of the last thirty years, a new design approach has been introduced and developed: the design of the virtual space. Designing the virtual means experiencing a multidisciplinary approach where architecture, engineering, and urban planning meet the new horizons of information and communication technology. This study is focused on virtual space, which is an underground city. Mankind have always made and used underground environments: the possibilities of unlimited spaces to potential development, the reduced needs for raw materials for the construction and the protection from outdoor weather are some of the reasons that prompted humans to the realization of underground spaces in the past. These reasons and the availability of innovative technologies could encourage a breakthrough in the realization of new underground environments. A recent example is represented by the Underground City of Montreal (RÉSO). We present the architectural design of a virtual underground city, which is called Arch[ane], and its evaluation. The underground city is modular and the studied module is composed of eight floors with a total depth of 400 m and dimensions of 800 m × 800 m. The study comprises the evaluation of the effect of sunlight on each eight floors of the city. Daylighting simulations were performed considering different cities at different latitudes, days, and hours. The results have shown that the particular design of the underground city with skylights gives significant values of illuminance at a certain depth. Furthermore, the simulation results show how huge can be the potentialities of software to simulate extremely big environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".