Intelligent design and dynamic adaptation model of building facade based on artificial intelligence
Bibliographic record
Abstract
An intelligent design and dynamic adaptation model of building facade based on artificial intelligence (AI) is proposed. The model adopts hierarchical architecture, including data acquisition layer, intelligent design layer and dynamic adaptation layer. The data acquisition layer collects environmental parameters and user preference information through sensor networks and user surveys; Intelligent design layer uses deep learning and genetic algorithm to generate a variety of innovative and practical design schemes; The dynamic adaptation layer combines reinforcement learning and fuzzy control algorithm to optimize and adjust the design scheme according to real-time environmental changes and user feedback. The experimental results show that the model is superior to the traditional method in terms of design innovation, practicability and user satisfaction. The building energy efficiency is improved by 18.27%, the residential comfort score is improved by 22.39%, and the user satisfaction score is improved by 20%. This study provides an intelligent and dynamic solution for building facade design, which has important theoretical and practical significance.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".