Wood I-Joists with Excessive Web Openings: An Experimental and Analytical Investigation
Bibliographic record
Abstract
Two series of tests with different depth wood I-joists were conducted to study the effects of circular and square web openings and the placement of steel angle retrofits at openings. Finite-element analysis of all test configurations was used to improve understanding of how web openings and retrofits affect the stiffness, stress distributions around openings, and ultimate failure mechanisms. Test results show failure loads well above prescribed design loads; however, the presence of web openings affected the type of failure modes. Control joists failed at the tension flange, whereas joists with web openings failed in shear through web openings or by means directly attributable to the presence of an opening. Ultimate capacity and stiffness was generally reduced by the presence of a web opening, with openings located closer to high shear areas producing worse results. Square web openings lowered the ultimate capacity approximately 10% more than circular openings when located at the same location. Steel angle retrofits improved capacity if an adequate retrofit length was used to redistribute stress concentrations. Finite-element models accurately predicted decreases in stiffness due to web openings; however, the models predict an improvement in stiffness due to the presence of steel retrofits that was not ascertained in experimental results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".