Performance of Short and Slender Ultra High Performance Concrete filled Steel Tube and Double Skinned Columns
Bibliographic record
Abstract
This thesis examines the axial compression load-carrying capacity of ultra-high performance fiber reinforced concrete (UHPC)-filled steel tubes (UHPC-FSTs) and double skin systems, including short and slender columns. The first part of the study investigates experimentally and numerically the axial loading behavior of 135 MPa UHPC-filled double skin steel tubular columns (UHPCFDSTs). A total of 37 short stub columns were tested, including totally filled control tubes and tubes filled with normal- and high-strength (NSC and HSC) concrete. The study examined the effects of outer tube diameter-to-thickness (Do/to) ratio and inner-to-outer tube diameter ratio on the axial capacity of UHPCFDSTs. A nonlinear finite element model using the computer program LS-DYNA was also developed and verified. The experimental results were compared against the Canadian CSA (CAN/CSA) S16:19 code provisions which were found to overestimate the axial capacity by 12-56%. A modification factor was developed and is recommended to be introduced in the code equation. The second part of the study investigates slenderness effects in UHPC-FST columns. A robust three-dimensional nonlinear finite element model was developed using LS-DYNA to simulate the slender columns under concentric axial compressive loads and was validated using a large experimental database. An extensive parametric study was then performed, varying slenderness ratio (kL/r) based on column length (L) where r is the radius of gyration, diameter-to-thickness ratio (D/t) of the steel tube, effective length factor (k), and steel yield strength (fy). The CAN/CSA S16:19 code provisions grossly underestimated the load-carrying capacity of slender UHPC-FSTs by up to 48% at k= 2.0. A modification to CAN/CSA S16:19 equation was proposed based on multiple regression analysis of the results of the parametric study.
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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) | 0.000 | 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 | 0.000 | 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".