Evaluating the Embodied Carbon of One-Way Slab Strips
Bibliographic record
Abstract
The global emphasis on reducing greenhouse gas emissions means that structural engineers, contractors, and architects are increasingly required to account for embodied carbon in their designs. This also provides engineers with an opportunity to optimize their designs with respect to embodied carbon by considering the carbon footprint of concrete and steel. This thesis explores practical methods for carbon accounting and reducing the embodied carbon of one-way reinforced concrete slabs designed to CSA A23.3 standards. The research focuses on optimizing material selection and structural design to minimize carbon emissions while maintaining structural integrity. A comprehensive review of current practices reveals significant variability and challenges in carbon accounting, including exposure class, strength at age of the concrete age, cold weather construction, and concrete mix constituents, highlighting the need for standardized reporting practices and accurate data. The study compares the embodied carbon of different concrete mixes and reinforcing steel across four Canadian provinces, demonstrating the impact of material optimization on reducing carbon emissions. Through detailed case studies and experimental analysis, the influence of varying concrete and steel strengths, reinforcement ratios, and span lengths on the overall global warming potential (GWP) of slabs is evaluated. The study compares slab deflection design methods, including CSA minimum thickness provisions and deflection calculations using Branson's and Bischoff's formulas. The findings indicate that 15 MPa concrete combined with 600 MPa steel can reduce embodied carbon up to 50% compared to 50 MPa slab with 400 MPa steel due to the inherently lower embodied carbon of lower strength mixes. Bischoff's deflection calculation method is shown to provide more accurate estimates and yield thinner slabs, thus reducing material use and embodied carbon. The recommendations for accurate carbon accounting and lower carbon slab design practices aims to influence more sustainable, lower carbon building design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".