Sustainable Masonry: Mortar Containing Biochar and Recycled Aggregate in Concrete Blocks
Bibliographic record
Abstract
The building and construction industry is the third-largest producer of CO2 in Canada. Additionally, emissions from this industry are expected to go up by more than nine megatonnes of carbon dioxide equivalent (Mt CO2 eq) by the year 2030. As such, we need to find ways to reduce the carbon footprint of the construction industry. Any decrease in the emissions of carbon from the construction industry will help improve the long-term sustainability of the industry and also help the environment. \nBiochar is an excellent way of achieving carbon reductions. Biochar is made from wood waste using the process of slow pyrolysis, and it can be mixed with cement in construction to create potential carbon sinks. This study will focus on biochar as a cementitious additive for cement mortar. Biochar is added to the cement mortar in different proportions of cement by weight (1.25%, 2.5%, 5%, 7.5% 10% and 12.5%) to create small-scale specimens that are tested under compressive load after 7, 28, and 56 days. It is noticed that there is a slight reduction in strength as the proportion of biochar is increased when it is used as a cement replacement. Biochar also increases the absorption in cement mortar because of its ability to store water and release it slowly, which provides internal curing to the mortar, helping it gain strength over time. \nThe second part of this thesis examines recycled concrete aggregate for the production of concrete blocks. Ontario has one of the highest rates of production of concrete blocks in North America. In addition to using large amounts of raw material, concrete block fabrication produced concrete waste at the production facilities in the form of spillage and culled blocks that are not fit for use. This waste is generally sent to landfills. A novel way to reduce this waste is to reuse it to make new blocks. In this study, recycled concrete aggregate (RCA) procured from a block manufacturer is used to replace coarse aggregate in proportions of 10%, 25%, 50%, 75%, and 100% by volume of aggregate. The blocks are cast in the Queen’s University Concrete Lab and are tested for compression after 28 days. Results indicate that the use of RCA in blocks does not affect the compressive strength adversely.
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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) | 0.002 | 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".