Assessing the environmental impact of building houses in remote areas: 3D printing vs. traditional construction techniques
Bibliographic record
Abstract
The building sector holds a pivotal position in influencing our artificial landscape, emphasizing the pressing need to embrace green and inventive approaches, equipment, and instruments to mitigate its ecological impacts. In this study, a comprehensive evaluation of environmental burdens associated with construction methods in the remote area of Attawapiskat, a First Nation community located in northern Ontario, Canada, is carried out. The study includes a detailed comparison of the sustainability between 3D printing technology and conventional construction approaches. Six distinct scenarios for comparing 3D printing technology and conventional construction in Attawapiskat were identified. Subsequently, a cradle-to-gate life cycle analysis (LCA) was conducted for each scenario, facilitated by SimaPro software. The environmental impact across various categories was then compared. Ultimately, sensitivity analyses were employed to interpret the results, assessing the influence of variations on our findings. Our study highlights a substantial environmental improvement when using Geo-polymer concrete in 3D printing in construction compared to conventional methods. To boost 3D printing's eco-friendliness further, we advocate local material sourcing to cut transportation emissions and reduce the carbon footprint, ultimately advancing sustainability in construction. • Comprehensive evaluation of environmental burdens associated with construction methods in remote area of Attawapiskat. • Comparison of the sustainability between 3D printing technology and conventional construction approaches. • Analyze six distinct scenarios for comparing 3D printing technology and conventional construction in Attawapiskat. • Explore the benefit of using Geo-polymer concrete in 3D printing in construction compared to conventional methods. • Provide suggestions to reduce transportation emissions and carbon footprint to boost 3D printing's eco-friendliness.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".