Research Trends in Off-Site Construction Management: Review of Literature at the Process Level
Bibliographic record
Abstract
Off-Site Construction (OSC) is a new construction method based on factory production. Due to its advantages over traditional methods, such as high productivity, economic efficiency, and excellence in quality, OSC research has actively been conducted worldwide ranging from design and production standardization, transportation method, to construction planning. Thus, to understand what knowledge has been developed to improve the management of OSC projects, this study reviewed OSC papers that focus on improving a specific project management area (e.g., time, cost, and quality) in a specific phase of a project, i.e., “process-level research.” This study found 94 papers with such a focus, out of 222 OSC project management papers published from 1986 to 2018, and assessed the trends of the research with multiple dimensions, including project phases, OSC types, application types, and management areas. Main findings are as follows: (1) process-level research has been increasing fast since 2006. (2) Non-volumetric pre-assembly type contributes the most to the increase of process-level OSC management research. (3) Research focuses vary depending on the application type (e.g., living quality issues for residential, economics issues for non-residential, productivity issues for plant). (4) Wider project management areas (e.g., quality, human resources, risk) have gained attention from OSC papers since 2006. (5) Non-volumetric type gained interests in residential and non-residential buildings, whereas modular type was studied frequently in plants. This study would help project management researchers understand the trends in OSC and plan and conduct future OSC project management research.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".