Behaviour and Seismic Design of Stiffeners for Steel Bridge Tower Legs and Piers
Bibliographic record
Abstract
Thin-walled steel bo x colu mns have wide applications in p iers of u rban highway bridges, and in the towers of suspension and cable-stayed bridges. Currently, in practice, the stiffeners for tower legs and steel b ox pier colu mns are flat plates, all having the same cross sections and equally spaced from each other and fro m outside walls. With the constraint due to the adjacent walls, and with the stiffeners, especially the middle stiffeners, being not stiff and strong enough to form nodal lines due to yielding during cyclic loading, the middle portion of the stiffened plate tends to have the largest out-of-plane deformation. A new and more efficient concept for design of longitudinal stiffeners is proposed in this paper -to invest mo re stiffening material in the middle stiffeners instead of making all stiffeners to have the same cross section. In addition, based on the studies summarized here, we propose to use se ctions other than flat plates as stiffeners. We studied the effects of stiffeners cross sections and stiffener spacing on the local and overall buckling as well as the resulting stiffness and cyclic ductility of the steel bo x pier and steel tower legs. Our investigations showed that using stiffeners with an angle, plate or pipe welded to the traditional flat plate stiffener can improve the performance of th e stiffened plate considerably -delay local buckling and increase cyclic ductility of the stiffened plate. So me of the new stiffener geomet ries we studied and recommended can very efficiently be used in seismic retrofit of the steel bo x piers and tower legs of elevated freeways and major cable-supported suspension and cable-stayed bridge towers.
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) | 0.001 | 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".