Nonparametric sector dependence modelling for the directional synthesis of local wind climate and building aerodynamic responses: Adaptive kernel-based approach
Bibliographic record
Abstract
• A nonparametric sector dependence modelling technique using Adaptive Kernel Density Estimators (Adaptive-KDE) is proposed to address challenges in sector-based directionality methods. • The applicability, accuracy, and robustness of the Adaptive-KDE framework have been demonstrated through application examples, in which local wind climate data from the three cities were synthesized with the wind tunnel test data of an 18-story prototype mass timber building. • The proposed Adaptive-KDE directionality analysis framework gives predictions that closely match the results of the direct calculation method for service-level acceleration responses. • For high MRI winds, where dependence between sectors weakens, the predictions of the Adaptive-KDE approach align more closely with the multi-sector method. • The new framework enables the use of narrow sectors, extending the application of sector-based directionality analysis to state-of-the-art performance-based wind design approaches. Accounting for the directionality of wind is crucial in estimating the response of buildings to wind load. Sector-based directionality techniques are widely used for analyzing directionality effects. In single- and multi-sector methods, directional sectors of the local wind climate and building aerodynamic responses are analyzed separately, while their statistical correlation is assumed to be fully dependent or independent, respectively. The multi-sector method, which is preferred for structural design due to its relative conservatism, requires the use of wide sectors to ensure the statistical independence assumption holds. This, in turn, requires interpolating aerodynamic response parameters, which is prone to errors due to rapid variations with small directional changes. Moreover, performance-based wind design (PBWD) approaches, as outlined in the American Society of Civil Engineers Prestandard for PBWD, require 10-degree or narrower sectors in aerodynamic response representation for detailed directional resolution. Narrow wind sectors often exhibit correlation, necessitating accurate dependence modelling. Parametric copula-based methods have been used to model sector correlations; however, they impose restrictive assumptions on dependence patterns. Therefore, this paper proposes a sector-based directionality technique with nonparametric dependence modelling using adaptive kernel density estimators. To demonstrate the applicability and accuracy of the method, wind responses of a prototype mass-timber building hypothetically located in three cities: i.e., Toronto (Canada), Melbourne (Australia), and Baltimore (USA), were predicted. The predictions were compared with responses empirically computed from historical records. The results demonstrated that the method extends the applicability of sector-based directionality analysis to narrow sectors, making it suitable for PBWD approaches.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".