Bibliographic record
Abstract
Whereas on a global scale, more than one billion people live in precarious housing situations, many construction materials are often sent to landfill sites or, worse, burned. However, these rejected materials represent a richness whose reallocation would lead to a significant economy of resources. Therefore, reusing materials from the construction industry could eventually be part of the solution. In this paper, we will present the results of a study carried out within the framework of a master's thesis project, which attempts to establish an architectural response to this issue. The proposed solution involves a constructive system that allows the assembly of temporary shelters using a wide range of reclaimed materials. This approach implies the use of digital tools to generate a form resulting from the analysis of locally salvaged materials. The algorithm developed in this project can generate multiple formal configurations optimized for the available resources. Any shape obtained in this manner will be composed of a low number (3-5) of unique edge lengths. This rationalization strategy also limits the unique triangle typologies in the structure to a manageable number. The different elements, whether planar or linear, are then joined using low-tech metal nodes that can be easily assembled and disassembled. Because the standardized edge lengths and triangle types are compatible, the proposed workflow unlocks mixed material reuse for complex reticular structures. The resulting flexibility allows for several variations or even a partial or complete reconfiguration of the initial shape, thus further supporting the implementation of the circular economy principles for the construction of complex architectural structures.
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 | 0.000 | 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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.015 |
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".