Circular Construction: Systemic Sustainability Assessment of Agro-industrial Wastes as Cement Alternatives
Bibliographic record
Abstract
The construction industry faces increasing environmental pressure to reduce the impact associated with cement-based materials.At the same time, the agro-industrial sector generates large volumes of organic waste that are scarcely utilized.This study proposes a multicriteria assessment framework focused on sustainability to analyze the technical and environmental feasibility of three agro-industrial wastes: eggshell ash, rice husk ash, and sugarcane bagasse as partial substitutes for cement.Based on secondary data obtained from scientific literature, four key criteria are deeply analyzed: chemical analysis, carbon footprint, local availability, and compressive strength.A multicriteria matrix was developed to classify the materials based on their performance and environmental impact, supported by a circular model illustrating their integration into low-emission construction systems.The results show that the incorporation of agro-industrial materials as cement replacements presents promising performance both technically and environmentally, especially in Latin American countries.This work provides a circular construction cycle model to promote the use of materials.It is proposed to create a cement incorporating 10% agro-industrial waste ash, thus contributing to the development of sustainable materials to address the challenges of pollution and inadequate waste management in Latin America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".