Performance of X and inverted V bracing systems in controlling progressive collapse of reinforced concrete buildings
Bibliographic record
Abstract
Progressive collapse can lead to partial or total failure of structures under extreme events such as explosions or earthquakes. While brace systems effectively enhance lateral stiffness, their use in reinforced concrete (RC) buildings is limited due to detailing challenges at connection points. This study evaluates the influence of X and inverted V bracing systems on the progressive collapse behavior of RC buildings and investigates the effectiveness of optimally placed X-bracing configurations under multiple column removal scenarios. Three groups of 9-, 12-, and 15-story RC buildings, designed per the Turkish Building Earthquake Code (TBEC-2018), were modelled using the Applied Element Method (AEM) in the ELS software. The first group included unbraced reference models. The second group comprised 54 fully braced models using X and inverted V configurations, subjected to three distinct column removal scenarios. The third group included 27 models with X bracing placed only in selected bays to determine optimal configurations for collapse prevention. Nonlinear dynamic analysis of 84 models revealed that both the reference buildings and those with inverted V bracing experienced progressive collapse, with maximum top displacements reaching over 65 cm. In contrast, fully braced X systems effectively prevented collapse, reducing displacements to below 10 cm. Optimized X-bracing layouts, applied to only 30–50% of the bays, achieved comparable performance while reducing material usage and detailing complexity. This study demonstrates the superiority of RC X-bracing in enhancing progressive collapse resistance and provides practical recommendations for optimal placement in reinforced concrete (RC) buildings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".