Identification and Evaluation of the Key Decision Support Factors for Selecting Off-site Construction in Canada: A Building Information Modeling (BIM)-enabled Approach
Bibliographic record
Abstract
The construction industry lags behind other sectors in terms of productivity performance, with many megaprojects experiencing cost overruns. While there are various reasons for this, the most significant one is the lack of efficiency. Adopting the off-site construction (OSC) methodology can improve productivity by enhancing project efficiency mainly in terms of time, cost and quality. Although OSC, off-site manufacturing (OSM), Industrialized Building System (IBS), prefabrication, modular, or other similar terms are not novel concepts, it is essential to shift any aspect of construction project activity from traditional onsite methods to a controlled, factory-based and manufacturing concept of production. Industrialization and digital fabrication have gained significant prominence in recent years, as they are perceived as a viable solution to the issues faced by the construction sector. As OSC is gradually gaining interest in building projects, it is crucial to identify and validate the key decision support factors (KDSFs) for selecting an appropriate OSC method from the early design stage. The purpose of this study is to identify, verify, and evaluate the KDSF for selecting OSC in Canada. This study utilized a mixed-methods design, comprising a systematic literature review (SLR) and pilot expert reviews through semi-structured interviews and surveys, to accomplish the research objectives and ensure the validity and reliability of the findings. Twelve interviews were conducted to validate and analyze the KDSFs, which were then prioritized using the mean score (MS) analysis and weighting function. Based on the research methodology, 32 KDSFs were validated and grouped into 7 'dimensions'. Further analysis concluded that the most important 'dimension' in selecting OSC for a building project in Canada is project time which consists of the design period, production time, mobilization and transfer time, as well as the assembly, and construction periods.
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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 | 0.031 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".