Genetic Algorithm-Markovian Model for Predictive Bridge Asset Management
Bibliographic record
Abstract
Rapid or unexpected bridge deterioration can lead to partial collapse, which can subsequently hinder transportation activities and result in economic and human losses. Heavily adopted by the research community, Markov chain-based deterioration models assume that bridge conditions exhibit stationary transitions over time. This assumption requires a significantly large, and often difficult to obtain, number of historical records. As such, Markov chain-based deterioration models have been developed within classical nonlinear optimization frameworks that might result in local optimal solutions. Therefore, to enhance the model capability to simulate the temporal state transition, this study develops a Markovian-based deterioration model embedded within a genetic algorithm (GA) framework—a class of evolutionary computing techniques, to overcome local optimality issues. To demonstrate its applicability, the developed model was applied to a relevant data set of previously rehabilitated and unrehabilitated concrete and steel bridges. The developed GA-Markovian model was able to replicate the actual state probabilities for the unrehabilitated bridges within both the calibration and validation periods. The model performance was slightly lower for the previously rehabilitated bridges due to the inherited nonstationary transition. The model developed in the present study can be used to guide effective rehabilitation and replacement strategies, prioritize available resources, and devise data-driven predictive bridge asset management policies and standards.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".