Experimental Evaluation and Numerical Modeling of Wide-Flange Steel Columns Subjected to Constant and Variable Axial Load Coupled with Lateral Drift Demands
Bibliographic record
Abstract
This paper presents results from an experimental evaluation on the pre- and post-buckling behavior of 12 steel wide-flange cantilever columns under axial load and lateral drift demands. The influence of several loading and geometric parameters, including the cross-sectional local web and flange slenderness ratios, applied axial load, and lateral and axial loading history on the performance of these columns is thoroughly examined. The test data indicate that cross-sectional local buckling is highly asymmetric in steel columns under variable axial load. A relatively high compressive axial load can significantly compromise the steel column seismic stability and ductility, but this also depends on the imposed lateral loading history. The AISC axial load–bending moment interaction equation provides accurate estimates of a steel column’s yield resistance. However, the same equation underestimates by at least 30% the column’s peak resistance, regardless of the loading scenario. Measurements of column flange deformation, axial shortening, flexural resistance, and lateral drift are combined in a single graphical format aiding the process of assessing steel column repairability after earthquakes. The test data suggest that current practice-oriented nonlinear component modeling guidelines may not provide sufficient accuracy in establishing both the monotonic and first-cycle envelope curves of steel columns. It is also shown that high-fidelity continuum finite-element models should consider geometric imperfections of proper magnitude, in addition to the steel material inelasticity, to properly simulate the inelastic buckling of wide-flange steel columns and generalize the findings of physical tests. Issues arising due to similitude are also discussed to properly limit steel column instability modes in future studies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".