Framework of smart multi-purpose utility tunnel information modeling for lifecycle management
Bibliographic record
Abstract
Multi-purpose utility tunnels (MUTs), as an alternative to buried and above-ground utilities, can accommodate multiple utilities, such as gas, water, and sewer pipes, and electrical and telecommunication cables. Despite MUTs’ advantages, their high initial construction cost and the need for coordination among utility companies hindered their wide usage. Building information modeling (BIM) can solve some of these problems by facilitating the design, construction, operation, and coordination of utility companies. However, BIM is mainly developed for buildings and extended to some civil structures. There is a lack of a comprehensive framework covering MUT components and information requirements for other use cases, and its integration with Geographic Information Systems and other technologies. This paper proposes an integrated framework for smart MUT information modeling (SMUTIM) by (1) gathering the information requirements for all the phases of lifecycle management, (2) analyzing the use cases of SMUTIM, and (3) extending the Industry Foundation Classes to SMUTIM. A case study demonstrates the benefits of the proposed framework.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".