Defining Information Requirements for Off-Site Construction Management: An Industry Case Study from Canada
Bibliographic record
Abstract
Off-site construction projects have demonstrated the potential to drive transformation in the construction sector to achieve better outcomes in terms of high precision and efficiency of deliveries through the collaborative design and fabrication processes, long-term stakeholder engagement and relations across projects, product standardization, risk-sharing strategies, and investment in a strong integrated supply chain. However, repeating the successful off-site construction projects to scale the benefits can be challenging considering that construction projects vary across regions and contexts. The streamlined processes and a consistent collection of information requirements should be leveraged to alleviate the challenge and help stakeholders effectively adopt the off-site construction approach. To achieve this goal, this study defines the information requirements across multiple stages of the off-site construction supply chain, using the case study approach to delineate the information requirements for an off-site construction company located in Edmonton, Alberta, Canada. The evaluation and interpretation of the case study underscore the need for consistent collection, storage and utilization of logistics, construction, and postconstruction activities information to tie back to sales and design activities. To facilitate the activities integration, an information requirement framework is proposed for off-site construction processes based on the learnings collected from the case study and a digital twin technology platform. Although the reuse and interoperability of information among various systems remains a long-standing issue to prevent the wide adoption of off-site construction, the findings and the proposed information requirement framework can be an integral part of the potential guidelines for successful implementation of off-site construction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".