The State of Practice of Prefabrication
Bibliographic record
Abstract
In today?s construction industry, electrical contractors must think about adopting non-conventional construction methods such as prefab in order to improve their performance and to face the continuous challenges they encounter. Challenges facing electrical contractors today include: low labor productivity levels, low and fluctuating profit margins and frequent schedule compressions. \nMultiple studies conducted by other researchers revealed that the appropriate use of prefab for many trades has a positive impact on project performance. In general, prefab has the potential to positively impact the project in the following project factors: cost, quality, schedule, safety and productivity. In addition, these studies discussed some impediments for the use of prefab. Common impediments for the use of prefab include increased engineering requirements, transportation considerations and organizational requirements. However, there is a gap in the literature about prefab in the electrical contracting industry. No recent research exists that is specific to the electrical contracting industry. \nBased on the literature review, a comprehensive survey about prefab was prepared. The survey was generated online and emailed to electrical contractors using Qualtrics, which is a web-based survey service. 142 electrical contractors representing diverse regions of the United States and Canada responded to the survey. A substantial variety of electrical works conducted by electrical contractors are represented. In addition, small, medium and large size companies are well represented in these responses. \nOf the 142 electrical contractors who responded to the survey, this research showed that 74 percent currently use prefab. The majority of these current-users began adopting prefab in their companies over five years ago. However, 63 percent of users spend only 1 to 9 percent of their company?s labor hours on prefab. Electrical contractors reported that prefab can help in improving \n \nlabor productivity as they estimated that one productive hour in the shop equals (on average) 2.2 hours in the field. Finally, this study showed that electrical contractors will still have disparate use of prefab during the next two years. On average, users and non-users estimated prefab would be used on 37 percent of electrical construction projects during that time. However, electrical contractors currently using prefab are planning on using prefab on more than 75 percent of their projects during the next two years and 72 percent of electrical contractors that are not currently using prefab anticipate starting use in the next two years. This seems to indicate that as electrical contractors learn to use prefab they use it with increasing frequency which implies that the use of prefab will continue to grow.
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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 | 0.000 |
| 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".