Developing Information Model for Multi-Purpose Utility Tunnel Lifecycle Management
Bibliographic record
Abstract
A Multi-purpose utility tunnel (MUT) is one of the civil infrastructures in urban areas, which accommodates several networks, such as electrical cables, gas, water, and sewer pipes, inside a tunnel. There are several benefits of MUTs compared to buried utilities. However, MUTs are not widely used at the time being due to the high initial construction cost and the need for coordination among utility owners. Building information modeling (BIM) is becoming the main coordination tool for building projects. BIM has been extended to civil infrastructures, such as bridges, roads, and sewer networks. However, BIM extension for MUT information modeling (MUTIM) is yet to be developed. This paper aims to investigate a method for extending BIM to MUT projects taking advantage of similar developments for other infrastructure systems. In addition, a systematic approach for MUTIM use cases is proposed. Five use cases of MUTIM were mentioned in this paper: (1) design review for checking compliance with standards and constructability; (2) 3D coordination for clash detection and resolution; (3) ergonomic design for human accessibility and comfort during construction, inspection and maintenance activities; (4) phase planning for construction and maintenance scheduling using 4D simulation; and (5) quantity takeoff for cost estimation. The first two MUTIM use cases are discussed in detail. A case study is developed to demonstrate the feasibility of the proposed approach. The presented MUTIM approach can improve MUT projects design and coordination efficiency, and reduce project cost, which are the main barriers for promoting MUTs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".