Barriers and Potential Solutions to the Adoption of Modular and Offsite Construction: A Review
Bibliographic record
Abstract
Modular and offsite construction (MOC) offer improved efficiency, sustainability, and flexibility in the construction industry compared to traditional methods. However, its widespread adoption remains hindered by various barriers. This paper presents a collaborative study conducted by researchers from Concordia University, the University of Alberta, and the National Research Council Canada (NRC) to identify and address these challenges. A comprehensive review of existing global research was conducted to identify barriers to MOC. These barriers were analyzed and categorized into six key groups: 1) Regulatory, 2) Economic, 3) Technical, 4) Organizational, 5) Workforce, and 6) Environmental. Additionally, recommendations to overcome these barriers are proposed and discussed. The findings from this study will serve as a foundation for a field survey to evaluate the significance of these barriers and assess their real-world impact within the current construction ecosystem. This study contributes to advancing MOC by identifying its barriers and supporting the industry's urgent need to adopt more sustainable and innovative alternatives to traditional construction methods.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".