Axial Load Behaviour of Repaired Piers with Ultra High Performance Concrete Jacket
Bibliographic record
Abstract
Rehabilitation of bridge and other infrastructures damaged by continuous wear, as well as time-dependent and environmental effects, is a major concern. Every year, worldwide billions of dollars are spent to repair and maintain a large number of reinforced concrete (RC) bridges. Recent developments on different high performance concretes (HPCs) such as ultra high performance concrete (UHPC) significantly made these materials feasible and convenient to use for repair of damaged and deficient piers. UHPC can protect the existing core pier against aggressive environmental agents and increase strength and durability of the piers as the confinement material. This paper presents an investigation on the performance of repaired damaged RC circular bridge piers using HPC jacketing technique. Jackets made of UHPC having different thicknesses with same reinforcement configuration were used to repair damaged RC core piers and analyze their behavior. Reinforced core piers were axially loaded to 60% of their ultimate load to induce damage before being repaired with HPC jackets. Jacketed pier specimens were tested to failure under concentric axial load applied through the core pier. Test results indicated performance enhancement of UHPC jacketed repaired piers in terms of improved ductility, energy absorbing capacity and strength recovery. The jacket confining effect and overall ductility characteristics of repaired piers were influenced by jacket thickness to core pier diameter ratio which needs to be optimized.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".