Beyond Survival: Developing Sustainable and Resilient Refugee Shelters through Biomimicry
Bibliographic record
Abstract
Abstract This study explores the use of biomimicry as a vital response to the urgent demand for sustainable and resilient refugee shelters. Recognizing the challenges faced by displaced populations due to climate disasters, it emphasizes the critical need for substantial improvements in shelter solutions. By embracing biomimicry principles that emulate nature’s efficiency, resilience, and adaptability, the study proposes environmentally conscious and resource-efficient shelter structures under a practical framework that meets immediate needs, contributes to climate mitigation, and harnesses nature’s innovative potential. The research methodology involves a comprehensive investigation utilizing architectural modelling, drawings, and the comparative analysis of various shelters. Through the meticulous assessments of photographs, structures, and diagrams, alongside extensive data aggregation, an informed analysis is presented. Utilizing computational tools and drawing inspiration from nature’s resilient strategies, this study presents a design methodology that applies the sustainable principles of biomimicry to effectively balance the construction of new settlements with the preservation of existing ecologies. By analysing diverse refugee shelter designs, including tarp-based tents, traditional shelters using local and industrial materials, transitional shelters, shelter kits, and climate-adapted tents, the study presents a practical biomimetic framework that responds to their limitations. Addressing challenges such as structural integrity, insulation, and adaptability, biological strategies are abstracted and applied through computational design processes to provide a tailored framework to assist designers in the development of refugee shelters. Amidst the exacerbating effects of climate change, this research highlights the untapped potential of biomimicry in creating sustainable and resilient refugee shelter settlements. By integrating advanced computational tools and a nuanced understanding of biological models, it establishes a framework for effective shelter development that prioritize sustainable and resilient shelter solutions worldwide.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".