Ten questions concerning wind-focused shape and structural optimization of tall buildings
Bibliographic record
Abstract
Tall buildings, over 60 m in height, are characteristic of modern urban cities and are expected to form a significant portion of future urban habitats. Given that the construction industry accounts for 38% of worldwide carbon emissions, targeting sustainability in this sector is vital. Due to the multi-dimensional nature of the tall building design process, optimization shows growing promise in developing solutions that increase design efficiency while sufficiently exploring design spaces and limiting computational costs. However, integrating architectural concepts with structural design to ensure that aesthetic goals are met without compromising structural integrity is challenging. Additionally, the critical dependency of lateral load magnitudes on a building’s outer shape and structural details underscores the need for a holistic design approach to meet both serviceability and capacity requirements for highly tall and flexible structures. Characterized as dynamically sensitive structures, tall buildings require thorough consideration of wind and seismic loads, which exert a dominant influence on the structural system. This paper provides a roadmap for the structural optimization of tall buildings, focusing on wind loads, presenting a perspective on design parameters, algorithms, and modelling techniques, in addition to utilizing machine learning in various design stages. It also assesses the impact of different load types on the optimization process, the role of surrogate models in facilitating design frameworks, and innovative methods offering promising solutions surpassing current capabilities. Cumulatively, this paper addresses avenues for future research into optimization and tall building design.
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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 | 0.000 | 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 | 0.000 |
| 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 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".