Impact of Fast Automated Tracking of Construction Components on Labor Productivity
Bibliographic record
Abstract
Impact of Fast Automated Tracking of Construction Components on Labor Productivity David Grau, Carlos H. Caldas, Carl T. Haas, Paul M. Goodrum,Jie Gong Pages 505-511 (2009 Proceedings of the 26th ISARC, Austin, USA, ISBN 978-0-578-02312-0, ISSN 2413-5844) Abstract: Even though the fundamental importance of construction components in any given project, industry practices still solely rely on the human ability to individually control thousands of these components on the field. This lack of automation frequently results in critical errors that negatively affect project cost and schedule. Recently, though, the undemonstrated notion that materials tracking processes can highly benefit from the implementation of information technologies has been gaining wide industry acceptance. This paper presents the results of a massive study on a large industrial site that aimed at quantifying the impact associated with automating materials tracking processes on craft labor performance. For this purpose, field records from manual and automated tracking processes were collected during the trial. Then, the influence of the automated tracking process on construction performance was determined by considering the manual approach as the baseline for comparison. The results indicate that information technologies can significantly enhance craft labor productivity. Keywords: No keywords DOI: https://doi.org/10.22260/ISARC2009/0045 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".