Bibliographic record
Abstract
In Canada, USA, and other countries, a large number of steel structures such as steel beams, steel trusses, and steel columns have been faced with various deteriorations such as corrosion and cracks. Hence, the need for development and validation of cheaper, greener, efficient, and innovative rehabilitation techniques for these deficient steel structures is on the rise. Several research studies were conducted on rehabilitation of corroded steel beams using carbon fiber reinforced polymer (CFRP) fabrics. However, only a very limited studies were undertaken to determine the effectiveness of basalt fiber reinforced polymer (BFRP) fabrics for flexural rehabilitation of steel beams. Further, no studies on the rehabilitation of shear deficient steel beams have been undertaken using BFRP. Hence, this study was designed and carried out to investigate feasibility and effectiveness of rehabilitation technique for steel beams with deficiency in flexural and shear strengths. Both CFRP and BFRP fabrics were used to develop a new rehabilitation technique. The study was completed using both experimental tests and numerical method using finite element method. The study found that both CFRP and BFRP fabrics are able to restore elastic stiffness, yield strength, and ultimate load capacity of a flexural deficient steel beam, however the BFRP rehabilitated beams experienced better behaviour such as more ductility. In addition, the study showed using BFRP fabrics in appropriate orientation (45 degree) could be the optimum technique to completely restoration of the web thickness loss. The study also concluded that the basalt fabric offers a much cheaper and green alternative to other fabrics like carbon fabric.
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 | 0.000 |
| 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) | 0.001 | 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".