Evaluating the behavior of notched connections in timber-concrete composite beams
Bibliographic record
Abstract
The existing design procedures for timber-concrete composite (TCC) beams typically rely on pushout tests or simplified analytical methods developed from pushout tests to estimate the capacity of notched connections. However, these methods do not account for the differences in loading conditions and stress distributions between pushout tests and actual beams. In addition, there is limited guidance on optimizing the design of notched connections in TCC beams to enhance structural performance at ultimate load. This study addresses these gaps through both numerical and analytical modeling of notched connections. A detailed finite element (FE) modeling approach is developed and validated against experimental results from various TCC beam and pushout tests. Following validation, an extensive parametric study is carried out to assess the influence of key design parameters, such as notch geometry, spacing, location, and material properties, on connection effectiveness and beam behavior. Corresponding pushout models are also developed to evaluate the reliability of pushout tests in predicting connection capacity in beams. The use of FE analysis enables direct comparison of connection behavior between pushout and beam models. Based on the results of over 100 case studies, strength modification factors are proposed to adjust pushout capacities to more accurately represent connection behavior in TCC beams. These factors are incorporated into a previously developed simplified analytical method, improving its prediction accuracy and extending its applicability. The findings provide practical guidance for designing more reliable and efficient notched connections in TCC beams.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".