Seismic Resilience of Concrete Moment Frames with Fibrous Rubberized Beam-Column Joints
Bibliographic record
Abstract
Ensuring resilience through rapid buildings recovery in seismic zones has been placed at the forefront of recent studies. However, studies that compare the relative resilience of buildings constructed with different materials are lacking. This paper focuses on a comparative resilience analysis of concrete moment frames where beam-column connections are made with varying types of concrete mixtures. The connections are made with normal strength concrete (NSC), rubberized concrete (RbC), steel fiber rubberized concrete (STFRC), synthetic fiber rubberized concrete (SYFRC), and high strength concrete (HSC). Nonlinear static pushover and incremental dynamic analysis (IDA) are utilized to understand system response and the level of damage sustained following an earthquake. Subsequently, seismic fragility functions are constructed for the five considered frame types. The frames are subjected to different seismic scenarios, and the corresponding losses, recovery time, and resilience are quantified. A comparison between the five frame types is carried out to examine the optimal concrete mixture to be used to reduce damage probabilities, total direct losses, and recovery time and subsequently enhance resilience. The results show that rubberized concrete strengthened with steel and synthetic fibers can reduce economic losses by up to 19% and increase building resilience by up to 37% compared with the frames constructed with normal strength concrete.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".