Decarbonization of Concrete Structures: A Path Towards Industrialization
Bibliographic record
Abstract
The increasing demand for more ef ficient and eco-f riendly building practices has led to developing and improving traditional construction methods to address building issues concerning environmental impacts and costs. However, alongside the incorporated benefits, they introduce new obstacles and challenges that impact the final product. Alternatives to decrease carbon dioxide emissions by reducing construction materials usage are strongly emerging in the industry due to environmental harm and its impact on climate change. In many developed countries such as Canada, dif ferent acts and measures are being taken to achieve net-zero emissions shortly, fostering a collaborative commitment across the industry to eliminate millions of tonnes of greenhouse emissions. This study employs building information modeling technologies and examines six construction methods regarding material usage, carbon footprint, and costs. This approach analyzes basement walls methods: Insulating Concrete Form (ICF) walls, concrete sandwich wall panels, ribbed wall panels, and concrete slabs: cast-in-place, hollow-core, and ribbed slabs to assess material cost implications and carbon footprint of reinforcement steel, insulation, formwork timber, and concrete in the manufacturing, transportation, and material waste, stages. The study aims to identify the most sustainable and cost-ef fective construction practices by comparing these methods under consistent project conditions, constraints, location, and transportation distances. The findings indicate significant mitigation of carbon emissions and cost savings with ribbed structures. However, these benefits may vary depending on construction location, transportation distances, material types, site temperature conditions, choice of manufacturers, and seismic activity. The study highlights the need for continuous innovation to meet environmental goals and ensure economic viability.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".