Origami Infrastructure: A Viable Solution to Construction for Challenging Environments
Bibliographic record
Abstract
Infrastructure built in challenging environments should be able to withstand unforeseen climate and environmental challenges, including extreme temperatures, high winds, severe rainfall, and natural disasters. Most importantly, dwellings should provide humans with a safe, livable, and resilient shelter. To this end, construction materials, design solutions, and construction processes must be considered to ensure safe and livable conditions. Furthermore, technology and innovative means of construction must be explored to reinforce infrastructure resilience and sustainable performance. However, previous literature has not addressed which features and materials are most adequate for resilient, deployable, and cost-effective origami shelter structures. This research aims to fill this gap by (1) identifying the suitable materials and features of structures that have been proven to be effectively deployable in such challenging environments through literature review; and (2) proposing and analyzing the viability of origami infrastructure as a solution to construction obstacles in challenging environments. Origami is rapidly emerging in science, engineering, and construction applications as deployable and reconfigurable engineering systems of all scales that can be fabricated with a wide variety of materials. Origami structures are geometrically versatile, adaptable, and can be easily and quickly assembled. Moreover, they are lightweight and foldable, which facilitates their transportation into challenging environments where access can be difficult and limited. Therefore, origami structures present an innovative, sustainable, resilient, and feasible solution to construction obstacles in challenging environments. The findings of this study serve researchers and construction stakeholders who will be designing and building infrastructure systems in challenging environments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".