Design Optimization of Building Structures Using a Metamodeling Method
Bibliographic record
Abstract
Numerical optimization techniques have been widely used in the design of different structural and mechanical engineering systems. For practical building structures, finite element (FE) analyses are required in order to obtain structural responses, such as stresses, forces, and deformations. In a nested application of design optimization, an FE code shall be integrated with an optimization algorithm; therefore, software input/output and user-interface programming are necessary. In this work, an alternative approach was pursued. A metamodeling method based on augmented radial basis functions (RBFs) was used to approximate structural responses so that the FE software was replaced by an approximate model, i.e., metamodel. It was not required to directly integrate the FE code in the numerical optimization loop. Once the explicit response functions became available, a traditional gradient-based algorithm was applied so that an optimal design could be found. The overall procedure of the design optimization problem was presented. As an illustrative example, a three-dimensional (3D) reinforced concrete (RC) building was studied and the optimization objective was determined to reduce the torsional responses of the building. Detailed FE analyses were first performed to obtain the building responses at the sample points, before the metamodel was constructed and optimization algorithm was applied. The optimization method worked well and provided a useful tool for practicing engineers.
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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".