Development of BIM based rehabilitation and maintenance process for roads
Bibliographic record
Abstract
Development of BIM based rehabilitation and maintenance process for roads R. Heikkilä, M. Marttinen Pages 1216-1222 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: The utilization of Building Information Modeling (BIM) and model based 3-D control of work machines have increased a lot in new road construction in Finland as well as in other Nordic Countries. Up today, much less attention has been paid into the development of maintenance and rehabilitation processes. As a part of a large research and development program RYM PRE Infra FIN BIM in Finland, a focused research project of the BIM with experiments in total five (5) pilot projects has been performed. A main idea of the new BIM based process has been to utilize mobile laser scanning method for initial data acquirement, the use of novel 3-D analyze and modelling methods for point cloud processing, the use of new type optimization method for the planning of geometric and structural improvements needed for the existing uneven road surfaces, and further, through the creation of 3-D machine control models apply the newest 3-D machine control systems for continuous 3-D control of practical construction work using milling machine and asphalt paver. Finally, an evaluation is made between the new process model and the traditional way of working. Keywords: Automation, Roads, Information modeling, Mobile Laser Scanning, Maintenance DOI: https://doi.org/10.22260/ISARC2013/0136 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".