Modular Industry Characteristics and Barriers to its Increased Market Share
Bibliographic record
Abstract
Modular and offsite construction reduces project duration and cost by synchronizing offsite and onsite work. Project activities are constructed in a controlled offsite facility to minimize effects of inclement weather and site disruptions, while meeting safety and quality requirements. In recent years, many organizations have conducted questionnaires to study characteristics of modular and offsite construction, such as the Modular Building Institute (MBI), Buildoffsite campaigning organisation in the UK, Canadian Manufactured Housing Institute (CMHI), National Institute of Building Sciences, McGraw-Hill Construction, and Fails Management Institute (FMI). This paper introduces a summary of results for a new questionnaire carried out in collaboration between the Department of Building, Civil and Environmental Engineering (BCEE) at Concordia University, MBI, Niagara Relocatable Buildings, Inc. (NRB) in Canada, and the Nasseri School of Building Science and Engineering at the University of Alberta. This questionnaire focuses on two issues: (1) the characteristics of the modular and offsite construction industry, and (2) detected barriers to the increased market share of this industry. For the latter, effort was made to address five factors emanated from a workshop on äóěChallenges and opportunities for modular construction in Canadaäóť held in Montreal in October 2015 to analyze barriers to growth of modular construction in Canada. Key findings of this questionnaire include requests for use of a separate code of modular construction design, innovative financing and insurance solutions, standards that consider procurement regulations, and lending institutions that partner with financial houses to create special lending programs for 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".