Empirical Formulation for Compressive Capacity of Gusset Plates
Bibliographic record
Abstract
Gusset plates play a critical role in the behavior and stability of bracing systems and truss bridges. While the behavioral characteristics of gusset plates have been widely investigated and analysis procedures have been developed, considerable uncertainty exists in the design equations, due primarily to the complexity of stress distribution in the connection area. Current design procedures rely heavily on highly simplified approaches, which typically result in an inconsistent design factor of safety for various gusset configurations and boundary conditions. In this research, a powerful genetic programming (GP) tool is employed to develop an empirical formulation for compressive capacity of corner gusset plates using a comprehensive database collected from previously published test results and test-validated finite element models. The predictive model correlates the ultimate compressive strength of gusset plates with their mechanical and geometrical properties. A comparative study is performed to evaluate the performance of the derived expression compared to the results of the well-known effective length factor method. The results indicate that the GP-based equation accurately estimates the compressive capacity of gusset plates and its prediction performance is significantly better than that of the current procedures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".