Seismic Retrofitting of Rectangular Bridge Piers with Deficient Lap Splices Using Ultrahigh-Performance Fiber-Reinforced Concrete
Bibliographic record
Abstract
The effectiveness of an innovative retrofitting method using ultrahigh-performance fiber- concrete (UHPFRC) was investigated experimentally on RC bridge piers with deficient lap splice details. The objective is to study the performance of the proposed retrofitting technique for eliminating the bond failure mode in lap splice regions of rectangular columns with a cross-sectional aspect ratio exceeding 2. The strengthening technique consists of substituting the normal concrete around lapped bars in the splice region by UHPFRC. The test program includes unidirectional reverse cyclic tests conducted on five rehabilitated and one control rectangular large-scale specimens. The longitudinal reinforcement ratio ranged from 1.30 to 1.67% with bar diameters (db) equal to 25, 30, 35, and 45 mm. A splice length at the column base of 24db and stirrup spacing of 300 mm were used for all specimens to replicate column design practice before the introduction of modern seismic design specifications. The selected self-compacting UHPFRC mix contained 234 kg/m3 (3% by volume) of 10 × 0.20-mm straight fibers. The failure of all retrofitted specimens was progressive and ductile because they all failed due to the successive rupture of the dowel bars in the footing at a very high displacement ductility ratio. The lapped splice splitting failure mode was eliminated, whereas no longitudinal bar buckled. The UHPFRC cover integrity contributed to eliminate all failure modes associated with concrete damage (crushing, spalling). Recommendations for applying the proposed technique to deficient column rehabilitation are suggested.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".