Risk management of multi-state multi-component bridge systems using partially observable Markov decision processes
Bibliographic record
Abstract
Infrastructure systems play a critical role in providing continuous services to societies. Exposure to stressors such as aging, demand loads, and environmental factors threatens the functionality and safety of infrastructure systems, highlighting the necessity for proper decision-making frameworks. Toward this goal, in the light of imperfect asset condition state evaluation, this paper presents a stochastic framework based on partially observable Markov decision process (POMDP) for the determination of optimal maintenance actions. A feature of this approach is its ability to effectively and accurately manage large scale, multi-state multi-component bridge systems. To overcome the dimensionality curse of the decision-making for such large systems without losing accuracy, the “counting process” state reduction technique is applied and conformed in a novel way. Further, to significantly reduce the computational runtime while keeping the accuracy in a high level, a randomized point-based value iteration POMDP is utilized. The proposed framework is applied to a case study bridge system with four steel girders and one concrete deck. Results of 12 random runs showed acceptable convergence in the optimized average expected long-run reward. The applied framework provides optimal policies for the concrete deck and girders in each of the possible states. It is also concluded that the combination of the POMDP decision-making framework and the “counting process” technique gives rise to an efficient and accurate approach for the optimal management of large scale systems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".