The Application of Bees Algorithm in Finite Element Model Updating
Bibliographic record
Abstract
This research work is aimed to investigate the application of bees algorithm (BA) to the finite element (FE) model updating. BA is an evolutionary optimization algorithm that imitates the natural foraging behavior of the honeybees to find the global optimum of an objective function. Here, the weighted squared sum of the error between the measured modal parameters and the FE model predictions is considered as the objective function. To demonstrate the effectiveness of the proposed method, BA is applied on a piping system to update several physical parameters of its FE model. The results obtained from the numerical model are compared with the experimental ones obtained through the modal testing. The results show that BA successfully updates the FE model. Moreover, the performance of this approach is compared with two popular optimization methods; the genetic algorithm (GA) and the particle swarm optimization (PSO). The comparison shows the advantage of BA over GA and its similarity to PSO in terms of accuracy in the presented case study. However, BA reaches to the optimum solution faster than PSO and GA. Therefore, it can be concluded that BA is a robust and accurate optimization method that could be a good candidate for the FE model updating.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".