Developing Information Model for Multi-Purpose Utility Tunnel Lifecycle Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it