A hybrid machine learning framework for wind pressure prediction on buildings with constrained sensor networks
Bibliographic record
Abstract
Accurate and efficient prediction of wind pressure distributions on high-rise building façades is crucial for mitigating structural risks in urban environments. Conventional approaches rely on extensive sensor networks, often hindered by cost, accessibility, and architectural limitations. This study proposes a novel hybrid machine learning (ML) framework that reconstructs high-fidelity wind pressure (HFWP) coefficient fields from a limited number of sensors by leveraging dynamic spatiotemporal feature extraction and mapping. The methodology consists of four key stages: (1) low-fidelity pressure field reconstruction from limited sensor data using constrained QR decomposition, (2) dimensionality reduction of both low-fidelity wind pressure and HFWP reconstructions to extract dominant spatiotemporal features, (3) dynamic mapping of the reduced-order representations using a long short-term memory network, and (4) prediction of the high-fidelity pressure field reconstruction over time. The proposed approach, which predicts the time history of high-fidelity pressure coefficients for various wind directions, is validated using wind tunnel data, with case studies on multiple façades—including the windward, right-side, and leeward surfaces—under various constrained sensor placement scenarios. The proposed methodology is also evaluated against alternative ML models, demonstrating superior accuracy in reconstructing the full pressure field. The results highlight the robustness and generalization capability of the model across different wind directions and sensor configurations, making it a practical solution for real-time wind pressure estimation in structural health monitoring and digital twin applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".