Analysis and Parametric Study of Partially Composite Precast Concrete Sandwich Panels under Axial Loads
Bibliographic record
Abstract
This paper presents a numerical model developed to predict the response of partially composite load bearing concrete sandwich panels under axial loads applied to the structural wythe at any eccentricity. The model accounts for material nonlinearity, second-order effects, and cracking of concrete and plasticity of steel reinforcement, and can also model fiber-reinforced polymer (FRP) connectors. The analysis uses a bond-slip model to simulate partial composite action between the two wythes resulting from various configurations of insulation and shear connectors. A variety of failure modes can be detected, including concrete crushing, flexural yielding, connectors yielding, pullout or rupture, and stability failures. Progressive failure of connectors is also modeled. The degree of composite action (κu) can be calculated for a given design. The model was verified against experimental data and used to conduct a comprehensive parametric study. It was shown that κu increases with panel length. Connectors’ diameter and spacing greatly affect κu and give similar gains in strength and stiffness at the same connector’s reinforcement ratio (ρv). Connectors inserted at an angle are considerably stronger than those inserted normal to panel face. The insulation bond alone provides a 31% κu without any connectors, but this contribution decreases as ρv increases.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".