Life-Cycle Cost Analysis Framework to Support Data Procurement Strategies for Infrastructure Assets
Bibliographic record
Abstract
The collection of infrastructure performance data is critical for agencies to cost-effectively maintain and preserve their existing assets. This paper presents a decision-support tool developed to support a state planning agency seeking to select a cost-effective data procurement strategy; specifically, the agency is considering whether to continue collecting and processing infrastructure performance data in-house or outsource to a third-party vendor. The probabilistic tool integrates uncertainty estimation via statistical methods and elicitation of expert judgement with Monte Carlo simulations to compute the probabilistic life-cycle cost of alternative data procurement strategies. For this particular case study, the expected cost to continue data collection and processing activities in-house is higher than the cost to outsource such activities. More importantly, the case study results lead to other important insights and contributions that are more generalizable to other contexts. For example, the labor resources required to collect, process, and maintain infrastructure condition data is the largest driver of total life-cycle costs for in-house data collection. Furthermore, because the decision to outsource data collection is made well before an agency selects a vendor, and such cost estimates are frequently unknown, there is a higher level of uncertainty and potential risk associated with outsourcing these activities. These conclusions, as well as the methods and framework presented in this paper, should assist planning agencies as they develop their data procurement strategy.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".