Analytical modeling of thick/stiffened base plates with internal anchor rods under eccentric compressive and tensile loads
Bibliographic record
Abstract
To date, some analytical issues of base plate connections such as the contribution of internal rows of tensile anchor rods and the case of eccentric tensile loads have not been addressed in design guidelines. In this paper, extended analytical approaches were proposed to obtain closed-form solutions capable to study such analysis cases for thick/stiffened base plates. The predicted analytical results were compared to those of existing experiments as well as numerical models for two example problems, which are large and typical base plate connections. The results illustrated similar trends and fairly good agreement, since relative errors were less than 10%. The contribution of internal rows of tensile anchor rods in terms of their total forces was observed to be up to 90%. Moreover, the reduction in the relative errors of the predicted results of the proposed extended method, compared to conventional ones, was remarkable, up to 39% for the two example problems. The proposed computationally cost-effective approaches can be utilized in analyzing thick/stiffened base plate connections, in the design phase. However, the proposed analytical approaches are not appropriate for intermediate and thin base plates. This can be attributed to the reduction of accuracy, in terms of large relative errors of up to 20%, due to excessive flexure and inelastic deformations in intermediate and thin base plates under large loads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".