Constraints of Modular Construction for Fully Serviced and Finished Homes: Lessons Learned from Canada
Bibliographic record
Abstract
Among different approaches to modular construction, the fully serviced and finished home is the most advanced approach that has revolutionized the construction industry. The benefits of this approach are realized in enhancing the workflow, efficient use of resources, improving construction safety, reducing construction wastes, cutting the on-site contractors, optimizing the construction scheduling, and consequently the project duration. However, more investigations are required to explore the constraints of employing this approach and the possible mitigations to overcome those. The aim of this study is to tackle the constraints of adopting fully serviced and finished homes from a manufacturer’s perspective. To accomplish this objective, a literature review was conducted and the most important constraints found were used for a semi-structured interview with a Canadian manufacturer. The three most important constraints were highlighted and the approached adopted to overcome those limitations by that specific manufacturer are discussed. Furthermore, five themes that were less important to this manufacturer were investigated. This study is an informative asset for the industry practitioners to have a better understanding of the limitations and applicability of modular construction.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".