The Evolution of Crop-Based Materials in the Built Environment: A Review of the Applications, Performance, and Challenges
Bibliographic record
Abstract
The use of bio-based building materials as an alternative to replacing concrete or insulation materials is called to become a growing trend in the construction industry. On Science direct, publications concerning “bio-based materials” have increased from 4 in 2002 to 1073 twenty years later, demonstrating a growing interest in these materials However, among bio-based materials, crop or plant-based materials are not as popular. Due to their relative novelty, little is known about their potential applications, physical characteristics, and environmental impacts. The aim of this review is to qualitatively investigate the technical and environmental viability of crop-based materials in modern building applications. The specific objectives of the study consider greenhouse gas (GHG) emissions using life cycle assessment (LCA) approaches, contribution to the circular economy, and physical and hygrothermal characteristics. Another objective is to examine the progress of crop-based materials’ R&D, current bottlenecks, and a future roadmap for their evolution in state-of-the-art renewable buildings. The paper is broad enough to capture a large readership rather than experts in the domain. The review reveals that crop-based materials have the potential to replace traditional, highly emissive building materials. They offer low environmental impacts, in all stages of their life cycle.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".