Implementation of subsurface utility engineering in Ontario: cases and a cost model
Bibliographic record
Abstract
This paper investigates a relatively new engineering service that is being introduced in Ontario: subsurface utility engineering (SUE). This service combines civil engineering, surveying, geophysics, and nondestructive excavation for the accurate mapping of underground utilities. This paper presents the results of a one-year study that investigated the use of SUE on large infrastructure projects in Ontario. The study involved performing a detailed cost analysis of nine successful SUE projects, four of which are presented in this paper. Potential cost savings were estimated for each case study and all indicated that SUE has a positive return on investment. In addition, two industry-wide surveys were conducted to investigate the effects of inaccurate utility information on projects. Results indicate that inaccurate utility information has a significant impact on project cost, schedule, and damage to existing utilities. Using the results of the case study analysis and the survey, a generic cost model for SUE was developed that relates project specific characteristics to costs that could be incurred because of inaccurate utility information. This investigation provides valuable insight to the application of a relatively new process in Canada following successful results in the United States.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".