Implementation of Agriculture Wastes in Different Construction Applications
Bibliographic record
Abstract
Due to the growing population and increasing demand for more construction, much attention has been paid to environmental issues and the devastating effects of overgrowth in nature. Many challenges, including the 2030 challenge, have united developed countries to come together for a better and cleaner future. Canada is one of the allies in this challenge. In recent years, many alternatives to the main components of concrete have been introduced, which is known as one of the most widely used building materials. The use of waste in concrete as a substitute for main natural components (such as aggregates and sand) is one of the most popular methods to reduce environmental pollution by construction during these years. The use of tires, electronic components and agricultural waste are among the uses of waste as an alternative to concrete. Due to its ecofriendly natural, low cost and easy access, agricultural waste has received more attention than others. Several agricultural wastes such as hemp, coconut shells, and others were utilized successfully in producing agro-concrete. However, the limited availability of in-service data and stability of agro-concrete in the agricultural environment, which is very aggressive, is halting its acceptance in the construction industry. Therefore, this dissertation focused on examining the potential of using agro-waste in different construction applications. Effects of various factors including shape, replacement rate and physical properties of used agriculture wastes, type of binding materials, and exposure conditions on mechanical performance were evaluated. Also, a special type of concrete, known as Controlled low strength materials (CLSM), was tested as a potential hosting for high amounts of agro-wastes. Two types of CLSM were evaluated: a cement-based CLSM (i.e. with ordinary Portland cement) and zero-cement CLSM (i.e. with alkali-activated binder). Results showed the high potential of implementing agro-wastes in various construction applications, including agro-concrete, controlled low strength concrete for filling applications and zero-emission materials (i.e. zero cement).Moreover, alkali-activated CLSM showed a greater potential to incorporate a high amount of agro-wastes than that of cement-based CLSM. The research results represent a crucial point in getting these materials as acceptable as construction materials. Also, it will allow the agriculture industry to effectively recycle/reuse the agro-waste, along with converting it to a valuable product. This will have a measurable impact on the Canadian specifications for concrete for farm and livestock buildings.
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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 | 0.001 |
| 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 | 0.001 | 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".