DYNAMIC RESPONSE OF EXISTING STEEL FRAMES WITH MASONRY INFILLS UNDER MULTIPLE EARTHQUAKES
Bibliographic record
Abstract
Existing steel moment-resisting frames in several seismic regions worldwide are often characterised by high vulnerability to earthquakes due to insufficient local and/or global ductility. Therefore, it is of paramount importance to assess their response under strong motions and provide cost-effective retrofitting remedies. However, the current code-based assessment framework utilized in Europe for assessing existing structures is inadequate and requires improvement, especially to account for the contribution of masonry infills as they significantly influence the seismic response of steel buildings. To this end, the H2020-INFRAIA-SERA project HITFRAMES (i.e., HybrId Testing of an Existing Steel Frame with Infills under Multiple EarthquakeS) aims at experimental evaluation of a case study building representative of nonseismically designed European steel frames. This paper presents the dynamic response analyses of the case study building and serves as a theoretical prediction of the experimental results for HITFRAMES. The case study building is analysed as a bare, an infilled and a retrofitted frame with buckling restrained braces (BRBs), respectively. It is subjected to the natural seismic sequence recorded during the 2016-2017 Central Italy earthquakes. The modal properties of the case study building are determined first, followed by the investigation of its non-linear dynamic response. The dynamic tests are performed with the earthquake records scaled to different intensity levels to simulate the structural performance under different limit states according to Eurocode 8-Part 3. The impact of masonry infills and BRB-retrofit is also investigated by comparing the response of models with different configurations. It can be concluded that appropriately-designed BRBs are effective in protecting steel frames from experiencing critical damage during earthquakes and reducing significantly the transient and residual drift.
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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".