Efficiency concept for upgrading the lateral stiffness of reinforced concrete wall systems
Bibliographic record
Abstract
Abstract This paper presents a new concept, the efficiency concept, for upgrading the lateral stiffness of reinforced concrete walls connected by a coupling system that is partially or not effective in flexure. The efficiency concept is based on the capacity of the connecting medium to reduce the overall drift. By definition a system of uncoupled walls has an efficiency of 0, whereas a system of fully coupled walls has a maximum efficiency of 1. Based on this concept, it is proposed to upgrade the wall systems with adequate beams, which are capable of transferring the additional forces between the walls. The stiffness, the number and the location of these added beams are optimized using the efficiency concept by which the overall drift is controlled. Results of the study are presented as easy‐to‐use charts for the practising engineer. These charts allow a rapid choice of an efficient structure, which can then be refined using any discrete structural analysis method. The method is efficient, convenient and cost‐effective as it minimizes the number of coupling beams to be added to the system to achieve a targeted drift. It also maximizes the clearance along corridors, particularly in buildings with low interstorey height. A practical example, following a step‐by‐step procedure, is provided to demonstrate the applicability of the concept. Copyright © 2002 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".