Minor defect correlation with dynamic elastic properties of polypropylene fiber-reinforced concrete
Bibliographic record
Abstract
The efficient rehabilitation of aging civil infrastructure requires innovative and emerging materials along with the proper implementation of structural health monitoring (SHM). Prior to identifying a strategic SHM technique, the understanding of defects in structures is vital. Common defects in concrete include consolidation problems and the development of microcracks during consolidation or stress induction. Monitoring the dynamic characteristics of concrete can play an essential role in detecting real-time and early stages of deterioration. Much research is focused on detecting large defects; however, not much information is available on the detection of minor defects in composites such as fiber-reinforced concrete. This study focuses on testing and monitoring of the dynamic elastic behavior of concrete using a non-destructive resonant frequency approach. The change in dynamic elastic properties of normal concrete under flexural and compression loading is analyzed. Moreover, an initial attempt to monitor the change in the elastic behavior when polypropylene fibers are added as reinforcement is also investigated. Experimental results show a decrease in the dynamic modulus of elasticity when minor defects are present and when polypropylene fibers are added to plain concrete mixture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".