Effect of Different Parameters of U-Concrete Jacketing on Behavior of Strengthened Beam
Bibliographic record
Abstract
With the continuous developments of civil engineering practices in general and structural engineering in particular, engineers have been investigating the rehabilitation/strengthening techniques of existing structures to save them and/or increase their load capacity by raising the efficiency of the structural elements of existing buildings such as beams and columns.This technique used for different purposes such as in increasing the lifetime of these structures, adapting with the change of functionality of the building, and to overcome the error design.The objective of this study is to investigate the effect of changing some properties of reinforced concrete jacket u-shaped which used for strengthening reinforced concrete (RC) beams.This study focused on increasing the load capacity of beams in existing buildings.The effect of changing yield strength, concrete strength, rebar size, and thickness of RC jackets on the load capacity of beams have been investigated aiming at identifying the best practices in this regard using Finite Element Method (FEM)-based numerical analysis utilizing ANSYS software.Moreover, reinforcement stress is discussed.The results of the numerical analysis have verified experimentally utilizing previous research.It is noticed that some of the proposed changes for RC jackets contribute in improving of performance for U-concrete jacketing technique to be a more effective technique to strengthen the existing beams which shall help engineers in practical use in the field.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".