Performance-based layout optimization framework of tall buildings subjected to dynamic wind load
Bibliographic record
Abstract
In the pursuit of advancing structural design methodologies, this paper presents a comprehensive framework for the conceptual design stage to systematically identify the layout of the main wind force-resisting system (MWFRS) (i.e., shear walls) of tall buildings within a performance-based wind design (PBWD) perspective. The framework integrates computational fluid dynamics for wind load evaluation with a finite element method to perform a linear time history analysis. A deep neural network surrogate model is developed to reduce the computational cost required for objective function evaluation. The optimization problem is defined to minimize structural material usage while adhering to performance constraints based on the PBWD described in the ASCE prestandards through using a non-dominated sorting genetic algorithm-II to derive the multi-objective optimization process. Finally, a nonlinear time history analysis is employed to predict the inelastic behaviour of tall buildings based on the prestandards assumptions to quantify the differences that can be generated using the developed framework. To ensure the proposed framework's effectiveness and practicality, a case study of a standard tall building is presented. • Novel optimization framework for performance-based wind design of tall buildings. • Integration of CFD and FEM for wind loading evaluation. • Development of a deep neural network surrogate model to reduce computational costs. • Utilization of genetic algorithms for multi-objective optimization. • Nonlinear time history analysis for wind-induced tall buildings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".