Innovative construction in Canada’s extreme environments: Combining computational design and digital fabrication with modern timber techniques
Bibliographic record
Abstract
To help solve the housing crisis in Northern Canada’s permafrost zones, this article-based thesis explores the combination of Computational Design (CD) and modern construction techniques. The study discusses how Cross-Laminated Timber (CLT) and 3D printing can be used to develop adaptable, sustainable, and culturally suitable housing solutions that address the logistical, cultural, and environmental challenges of extreme climates. The research highlights the use of CD for the optimization of structural configurations and resources in remote construction sites, where transportation costs are expensive, and access is often challenging or even impossible. The research is organized around two main themes: the need to incorporate Indigenous culture values into architectural design and the technological potential of employing CLT and 3D printing in extreme environment. The case studies from Canada and international projects are used as references to evaluate how these construction technologies are applied in real-world contexts. Findings suggest that 3D printing presents a great opportunity in terms of waste reduction, customization, standardization, and ease of assembly. On the other hand, CLT offers a great alternative for conventional building materials, accelerating construction and reducing the carbon footprint. An important aspect of this research involved a participation in a two-week analog mission at LunAres Research Station in Poland. The mission simulated an isolated environment and brought relevant information of psychological challenges that a human can face in daily life during space missions. The mission underlined the need to design spaces that support psychological well-being, reduce stress, and offer privacy and human interaction. The mission presents essential results on resource management, health monitoring, waste reduction, and self-sustained habitat. Finally, the research reveals the need of continued innovation and collaboration between, industry, researchers, and indigenous communities to effectively address the distinct housing crisis of Northern Canada and promote environmental sustainability and respect of cultural heritage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".