NAIL-LAMINATED TIMBER-CONCRETE COMPOSITE BEAMS WITH NOTCHED CONNECTIONS AND STEEL FIBRE REINFORCEMENT
Bibliographic record
Abstract
The utilization of timber-concrete composite (TCC) floors, which involves connecting a wood beam or panel to a concrete layer via shear connectors, has experienced a surge in popularity in recent years.Although notched connections have proven to be cost-effective shear connections, without proper concrete reinforcement, cracks can develop in the notched area and ultimately lead to brittle shear failure.In this paper, we present research aimed at mitigating crack growth in concrete and preventing notch shear failure in concrete by employing steel fibre reinforcement.Eight groups of nail-laminated TCC beams with varying notch depth, location and number of notches, and thickness ratio of concrete to timber were tested under third-point bending.The results demonstrated that the number of notches and thickness ratio had the greatest impact on beam bending properties, while the influence of notch location and depth was marginal.Furthermore, the steel fibres effectively delayed crack propagation in the concrete notches, resulting in timber failure in all tested specimens.The notches cut on NLT and filled with concrete affect the modulus of elasticity of NLT and need to be considered for the calculation of composite efficiency and effective TCC beam bending stiffness.
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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 | 0.000 |
| 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".