Integrated Decision-Support Framework for Municipal Infrastructure Asset
Bibliographic record
Abstract
Integration planning of Infrastructure systems reveals a changeling decisions facing Canadian municipalities for planning repair/renewal of road network, water distribution network, wastewater distribution network. Decision-making for these networks requires the incorporation of a massive amount of data collection, building business processes, identifying decision variables and optimization. The objective of this research is to establish a methodology to facilitate decision making process that ensures reliable and optimum decision regarding corridor rehabilitation for road, water and wastewater network. This proposed framework employs the following tasks: (1) analyze risk; (2) conduct performance evaluation; (3) assess the current physical condition of the pipe and road segment; (4) collecting data and performing data gap analysis; (5) document a conceptual business process diagrams; (6) develop decision analysis trees; and (7) implementing optimization of repair/renewal cost and defining the best replacement interval via genetic algorism (GA). In order to demonstrate the model features, a case study has been utilized from the City of Guelph, ON, Canada. The model is developed via genetic algorism (GA) using GIS platform. The results assist in setting priorities for integrated corridor rehabilitation and anticipated to generate a capital planning program for the city's infrastructure. In conclusion, this framework helps Canadian municipalities evaluate and select feasible optimal assets for integrated corridor rehabilitation.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".