Effective strategies for real time hybrid simulation of near seismic collapse response of moment resisting frames
Bibliographic record
Abstract
Reliable assessment of seismic performance of structural systems requires accurate and robust simulation techniques that can efficiently predict inelastic response in the large deformation range, up to structural collapse. This paper presents a real-time dynamic substructuring (RTDS) test program carried out on steel moment resisting frames (MRF) tested up to near collapse. A single-story, industrial building with steel MRFs at perimeter was examined applying the Loma Prieta earthquake record. Columns were pinned at their bases, while full stiffness and resistance was retained at beam-to column joints. The physical substructure included only one column that was installed in the inverted position i.e. clamped at the base and pinned at the top: in this way only one lateral degree of freedom was involved in physical tests. The other column, the beam, building masses, gravity loads and damping forces were included in the numerical substructure. Time integration was performed using a variant of a Rosenbrock-W scheme implemented into the Math Works's Simulink and XPC target computer environment. The tangent stiffness matrix of the structure was evaluated using different numerical strategies including data smoothing and filtering. Control techniques with constant or adaptive delay compensation for the feed-forward filter were implemented. The obtained results are compared and discussed to highlight the effect on structural response predictions. As a result, RTDS tests appear to be effective in the prediction of near collapse seismic response of steel frames, provided that robust numerical strategies are implemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".