Issues in Decision Support Tools for Sustainable Infrastructure Management
Bibliographic record
Abstract
There has been considerable and well-documented concern about the current state of public infrastructure - roads, bridges, water and waste systems, etc. The causes of these challenges - (1) aging and deteriorating infrastructure; (2) inadequate funding; (3) competing organizational objectives; (4) questionable maintenance, repair, rehabilitation and replacement practices in the past; (5) demographic and population shifts; and (6) new understandings about sustainability objectives - are common to many government and utility owners. These challenges necessitate that the infrastructure industry excel at developing and managing its infrastructure systems to their maximum potential. To meet these needs, the infrastructure domain requires improvements to the decision support tools that currently exist for sustainable infrastructure management. This paper reviews this problem with a particular focus on the Canadian context, and outlines a course of action to address the current needs. The proposal addresses three domains in the field of sustainable infrastructure management. First, it builds on work to develop comprehensive techniques to assess the sustainability of infrastructure systems. Second, it attempts to advance multi-objective optimization techniques and tools for predicting the long-term performance of infrastructure systems and optimal strategies under a variety of maintenance regime alternatives. Third, it develops data interoperability solutions to create an infrastructure data integrator as a computing platform for this work.
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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".