Effectiveness of simple approaches in mitigating residual deformations in buildings
Bibliographic record
Abstract
Abstract Developments in performance‐based seismic design and assessment approaches have emphasized the importance of considering residual deformations. Recent investigations have also led to a proposed direct displacement‐based design (DDBD) approach which includes an explicit consideration of the expected residual deformations as an integral part of the design process. Having estimated the expected residual deformations in a structure, engineers are faced with the problem of reducing them to meet the targeted performance levels under pre‐defined seismic hazard levels. Previous studies have identified the post‐yield stiffness as a primary factor influencing the magnitude of residual deformations in single degree of freedom and multiple degree of freedom structures. In this paper, a series of simple approaches to increase the post‐yield stiffness of traditional framed and braced systems for the purpose of reducing residual deformations are investigated. These methods do not utilize recentring post‐tensioned technology. This contribution addresses the feasibility of altering the lateral post‐yield stiffness of structural systems by: (i) using different reinforcement materials with beneficial features in their stress–strain behaviour; (ii) re‐designing the section geometry and properties of primary seismic‐resisting elements; and (iii) introducing a secondary elastic frame to act in parallel with the primary system. The efficiency of each of these techniques is investigated through monotonic and cyclic moment‐curvature and non‐linear time‐history analyses. Of these approaches the design and introduction of an elastic secondary system was found to be most effective and consistent in reducing residual deformations. A simplified design approach for achieving the desired increase of a system's post‐yield stiffness is also presented. Copyright © 2007 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".