Multi-Criteria Design Evaluation and Optimization of School Buildings Using Artificial Intelligent Approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
School buildings are one of the most important educational and learning environments and the appropriate design of these spaces has a significant impact in enhancing both students and teachers performance, comfort and satisfaction. As a result, the preliminary design evaluation and optimization of school buildings should be given a significant consideration. The key factor in design optimization of a school building, is defining the users' expectations, which is qualitative and subjective in nature. To capture these qualitative and imprecise aspects of the problem, and optimize school building design parameters, a multi-criteria fuzzy expert system is employed and the design evaluation and optimization model is developed. Different school building design parameters such as; building orientation and layout, envelope features, indoor air quality as well as day-lighting systems are investigated as part of the design evaluation and optimization process. The fuzzy expert system is used to analyze the optimal values of a list of parameters associated with the building design process to enhance the learning environment for school buildings. This method employs both quantitative and qualitative design performance parameters, and allows for different design alternatives to achieve the objective of the project.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it