New life‐cycle costing approach for infrastructure rehabilitation
Why this work is in the frame
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Bibliographic record
Abstract
Purpose Several rehabilitation planning methods are reported in the literature for public infrastructures, such as bridges, pavements, sewers, etc. These methods, however, are limited to specific types of infrastructures. The purpose of the present research is to develop a novel and generic method for Maintenance and Rehabilitation Planning for Public Infrastructure (M&RPPI), which aims at determining the optimal rehabilitation profile over a desired analysis period. Design/methodology/approach The M&RPPI method is based on life‐cycle costing (LCC) with probabilistic and continuous rating approach for condition states. The M&RPPI uses a new approach of “dynamic” Markov chain to represent the deterioration mechanism of an infrastructure and the impact of rehabilitation interventions on such infrastructure. It also uses genetic algorithm (GA) in conjunction with Markov chains in order to find the optimal rehabilitation profile. A case study is presented with a comparison between the traditional Markov decision process (MDP) and the newly developed method. Findings The new method, which generates lower LCC, is found practical in providing a complete M&R plan over a required study period, compared to a stationary decision policy with the traditional MDP. In addition, GA is found useful in the optimization process and overcomes the computational difficulties for large combinatorial problems. Research limitations/implications The implementation of the developed models is limited to only four alternatives/actions. However, the developed models and framework are superior for MDP. Practical implications The developed methodology and model play essential roles in the decision‐making process. Originality/value The new method is beneficial to researchers and practitioners. It is developed for a single facility; however, it provides a major step towards a broader infrastructure management system and capital budgeting problems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it