Seismic Retrofit of Steel Moment-Resisting Frames with High-Performance Fiber-Reinforced Concrete Infill Panels: Large-Scale Hybrid Simulation Experiments
Bibliographic record
Abstract
Recent earthquakes around the world have demonstrated that steel moment-frame buildings designed based on older seismic provisions are seismically deficient. To enhance the seismic performance of these buildings, a new seismic retrofit system has been developed and evaluated experimentally as part of a two-story steel moment-resisting frame, designed in California in the 1980s. The proposed retrofit system consists of high-performance fiber-reinforced concrete (HPFRC) infill panels acting as energy dissipation elements that can be easily replaced after a major earthquake. Through two large-scale hybrid simulation tests of the retrofitted two-story steel moment-resisting frame, it is demonstrated that (1) the proposed retrofit system is effective in terms of reducing the maximum story drift ratios and residual deformations of the retrofitted steel moment resisting frame relative to the predicted bare frame performance, and (2) the structural damage of the retrofitted steel moment-resisting frame is kept minimal because energy dissipation is concentrated in the infill panel retrofit system.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".