Automated Development of Construction Schedules Using Onsite Data Acquisition
Bibliographic record
Abstract
Automated Development of Construction Schedules Using Onsite Data Acquisition Magdy Ibrahim, Chantale Germain, Michel Guevremont, Martin Forcier, Osama Moselhi Pages 840-848 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Detailed as-built project schedules are necessary to close out construction projects, benchmarking, forecasting, dispute resolution, and improving cost estimates of future projects. Manual procedures for developments of as-built documentation is time consuming, involves numerous interfaces and human interventions. This paper presents computational framework that encompasses automated site data acquisition and generates schedule updates utilizing commercially available project scheduling software. The work is carried out collaboratively with a Hydro Quebec team. The site data is captured employing mobile computing using iPad® type computers and Wi-Fi. The information is directly compiled in a centralized database server. The synchronization tool is a bi-directional application and is used on servers to communicate with iPad® computers deployed onsite. The captured data is stored in Microsoft SQL® relation database that consists of 63 entities. Computer software application has been developed in Microsoft Visual Basic® (vb.net) environment for extracting the collected site data, linking the database to the as planned schedule, and generating the actual, also known as as-built schedule. The development can be utilized in automated progress reporting, evaluating future bids, generating master schedules and a wide range of efficient EVM applications. Keywords: Automated data collection, Mobile computing, As-built schedule DOI: https://doi.org/10.22260/ISARC2013/0091 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".