Decision-Support Framework for Integrated Asset Management of Major Municipal Infrastructure
Bibliographic record
Abstract
"Canada's municipal infrastructure is at risk." This was the key finding of Canada’s first municipal infrastructure report card. Given the current state of risk for Canadian infrastructure, municipalities face challenging decisions for planning the integrated repair/renewal of road, water and sewer networks. Decision-making surrounding the assets in these networks requires data collection, analysis, the identification of decision variables and undertaking optimized decision-making processes. Currently there is a lack of tools available to simplify the decision making process for stakeholders. \nThe research objective is to establish a methodology and framework that facilitates decision-making processes used during corridor rehabilitation project planning. The proposed framework consists of three main models: (1) Risk assessment, (2) Performance evaluation and (3) Integrated decision support system (IDSS). \nThe risk model was developed using a mixed Delphi-Analytical Hierarchy Process approach. The impacts of four main consequences of failure with eighteen sub factors were considered. Road, water and sewer networks indices were amalgamated and grouped into an overall integrated risk index using K-means Clustering technique. The performance model considers nine factors that represent the asset performance. These factors were mapped using fuzzy logic technique to a Customer Driven Performance Measure (CDPM) index. The IDSS framework allows the setting of priorities for integrated corridor rehabilitation and implementing optimization via Integer Programming. Finally, these models were applied in a prototype tool using Visual Basic built on Microsoft Access, Excel and GIS platforms. A series of workshop interviews were conducted with various municipalities to collect the necessary information. Data provided by the City of Guelph was used in a case study in order to demonstrate the model features. \nResults show that Pipe/road size and accessibility factors had the highest impact on the integrated risk index. The road roughness rating and watermain breaks results show the highest impact on the CDPM index. Optimization outcomes demonstrated that corridor rehabilitation alternatives resulted in a ‘maximum risk reduced per dollar spent’. The developed models can be used by researchers and practitioners (municipal engineers and consultants) in order to prioritize corridor rehabilitation projects thereby easing the challenge faced by stakeholders regarding the future of municipal infrastructure.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".