A systematic review of quality management of offsite construction
Bibliographic record
Abstract
Offsite Construction (OSC) continues to gain popularity for faster, safer, cheaper and more sustainable construction project delivery. An improved quality performance is a chief selling point in the advocacy for the widespread adoption of OSC. Paradoxically, quality issues that arise in OSC projects can be extremely costly. However, quality management (QM) is underexplored in the growing OSC literature. This paper critically reviews the QM of OSC literature to uncover the state-of-the-art and proffer recommendations for future research. 38 articles, selected from Scopus and Web of Science, published from 2009 to 2021 and distributed across 20 journals, were selected through a systematic literature review supplemented by a snowball search. An overview of QM of OSC research is provided based on the yearly distribution of articles, country/territory of affiliation, journal sources, OSC types, project life cycle stages and technologies utilised. The findings revealed a growing interest in the sub-domain. The articles were categorised under six topics: post-production quality assessment, rework and defect management, quality risk management, process improvement, requirements management and quality performance factors. This paper also proposes future research directions based on the prevailing knowledge gaps.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".