Modal analysis and calibration of finite element model of a three-story steel frame using machine learning and physics-based techniques
Bibliographic record
Abstract
Purpose This paper aims to focus on the state-of-the art methods for modal parameters extraction from modal testing. Design/methodology/approach The finite element (FE) model is updated using hybrid method (machine learning-based) and physics-based approaches. A three-story bookshelf frame has been used for the experimental study and a free vibration test has been conducted. The bookshelf frame, made of galvanized steel, has the following dimension: 60 cm width, 27 cm depth and 133 cm height. The frame has been instrumented with tri-axial wireless sensors. Three accelerometers have been installed on each floor of the frame. The frequency domain decomposition (FDD) and modified complex Morlet wavelet methods have been used to extract the modal properties from dynamic response. Findings The extracted results from both methods have been compared, and they are found to be close to each other. The MATLAB-based compiler called M-FEM is used to create FE models. The initial FE model is updated using different approaches. Originality/value The updated FE model output shows the efficiency of hybrid technique in updating the FE model, and the results are well correlated with the physics-based approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".