Multiobjective Structural Layout Optimization of Tall Buildings Subjected to Dynamic Wind Loads
Bibliographic record
Abstract
The tall building design process typically goes through a time-consuming iterative procedure to ensure the cost efficiency of the proposed structural system, especially in the conceptual design stage where the layout is designed. Despite that, this procedure does not guarantee to yield an optimal layout. Consequently, an automated layout optimization procedure will result in a more economical and sustainable design. This paper presents a novel multiobjective lateral load resisting system (LLRS) (i.e., shear walls) layout optimization framework provided for dynamically sensitive tall buildings subjected to a wind load time history. The developed framework relies on an artificial neural network (ANN) surrogate model for constraints and objective function evaluation to reduce the computational time of the optimization process. The adopted surrogate model is built based on an automated finite element models-generated database using MATLAB code via the Open Application Program Interface of the ETABS software. The ANN surrogate model proved its efficiency in capturing complex variations in the structural response with a correlation coefficient that ranges between 90% and 98%. A nongradient optimization algorithm (NSGA-II) is adopted to identify the optimal shear wall layout to resist the applied dynamic wind load. In order to reduce the number of optimal layout solutions on the Pareto front, a pruning algorithm is used to limit the optimal solutions to 24 layouts. This will enable designers to use the direct selection method to choose an appropriate layout that fits the project’s objectives. Also, a case study building is presented where the optimized results are analyzed and discussed in the numerical example to verify the effectiveness of the proposed optimization framework.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".