SEISMIC RISK MITIGATION THROUGH RETROFITTING NONDUCTILE CONCRETE FRAME SYSTEMS
Bibliographic record
Abstract
A large proportion of existing buildings across the world consists of non-ductile structural systems. Performance of structures during recent earthquakes has demonstrated seismic vulnerability of these systems. The majority were designed prior to the enactment of modern seismic codes, while some were designed more recently in areas where code enforcement can not be ensured. These structures constitute significant seismic risk, especially in large metropolitan centers. Because it is economically not feasible to replace a large segment of the existing infrastructure with seismically superior systems, retrofitting non-ductile systems remains to be a viable seismic risk mitigation strategy. The objective of this paper is to highlight recent research at the University of Ottawa in Canada on seismic retrofit strategies for such systems. Specifically, projects on i) concrete frames with unreinforced masonry (URM) infill walls retrofitted with surface bonded FRP sheets, ii) non-ductile reinforced concrete frames braced by diagonal prestressing, and iii) the use of active control and smart structure technology for seismic retrofitting non-ductile reinforced concrete frames are presented. Research findings indicate that the application of these retrofit techniques produces good to excellent improvements in strength and inelastic deformability of otherwise seismically deficient non-ductile systems.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".