Performance of Bond between Old and New Concrete Layers: The Effective Factors, Durability and Measurement Tests—A Review
Bibliographic record
Abstract
With the rise in construction costs and aging of existing concrete structures, retrofitting and strengthening have gained more popularity. Among all of the available techniques, adding new repairing layers on top of old concrete ones has proven to be highly effective. However, the efficacy of such method is dependent on the performance of the cold bond between old and new layers of concrete whose establishment requires different considerations, such as paying attention to the properties of concrete layers, namely their strength, permeability, aggregate size, density, etc., and the qualities of the interface between the layer, such as how wet it is or its roughness degree. In this paper, the factors which can impact shear and tensile bond strength are fully discussed while being categorized into two major groups of factors related to each concrete layer’s properties and those directly associated with the connection area. The durability of the bond after exposure to various environments in terms of temperature and relative humidity is also addressed and then a list and comparison of numerous tests that are commonly conducted to measure the bond strength are provided. The findings indicate the characterization of suitable materials and surface roughening techniques which can ensure an adequate bonding between substrate and overlay, along with recommendations for the scope of future research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".