Productivity-Based Management System for Offsite Manufacturing: Case Study of Noralta Lodge
Bibliographic record
Abstract
Large-scale projects entail a zero-tolerance policy in regards to on-time project delivery and project quality. Severe winter conditions in Canada challenge conventional on-site construction activities and raise the risk of project delays and deficiencies. Industrialized (modularized) construction stands as an alternative that provides high quality products in a timely manner. Moreover, modular construction offers manufactured building components in a controlled environment, which ensures that quality standards remain consistent regardless of weather conditions. Once manufactured, modular units are then shipped to the site to be assembled. Two major geographical phases are common in offsite construction: the manufacturing phase, and the on-site installation phase. Consequently, management teams face challenges related to productivity and optimum work sequence in both phases. Traditional project planning and control methods consider the duration of a task as a static entity resulting from the direct relationship between the sizes of the crews on-site and labour productivity. Learning curves, skill-based tasklabour matrices, and resource levelling techniques are factors that imply the dynamic nature of construction tasks; delays in one task may affect other subsequent tasks both directly and indirectly. The Productivity-Based Management System (PBMS) provides opportunities to increase the production rates of task duration, and decrease actual task duration. The proposed research introduces a framework for a PBMS to manage and control the on-site phase of modular construction. In this research, the PBMS is developed, implemented, and then applied to a 1,700- bedroom workforce camp in Fort McMurray, Alberta, Canada.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".