Sustainability Design Considerations for Timber‐Concrete Composite Floor Systems
Bibliographic record
Abstract
Over the last few decades, there has been growing interest in the use of low‐carbon materials to reduce the environmental impacts of the construction industry. The advent of mass timber panels (MTP), such as cross laminated timber (CLT), has allowed structural engineers to specify a low‐carbon material for a variety of floor design considerations. However, serviceability issues such as vibration and deflection are limiting the construction of longer span timber‐only floor systems and have encouraged the development of timber‐concrete composite (TCC) systems. The use of concrete would negatively impact on the carbon footprint of the TCC floor system and should be minimized. The purpose of this study was to study the impact on embodied carbon in the TCC system, when the ratio of timber and concrete was varied for specific floor spans. Two MTP products were considered, CLT and glued laminated timber (GLT). The floors were designed to satisfy structural, acoustic, and vibration criteria, and the results were presented in the form of span tables. It was found that using thicker MTP instead of adding concrete thickness to meet a specific span requirement can lead to lower embodied carbon values. Increasing concrete thickness for long‐span floor systems led to a reduction in allowable floor span due to the vibration criterion being the controlling design parameter. Increasing timber thickness also resulted in higher strength and stiffness to weight ratios, which would contribute toward reducing the size of lateral load resisting systems and foundations, resulting in further reductions in the embodied carbon of the entire structure.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".