Data-Driven Models for Infrastructure Climate-Induced Deterioration Prediction
Bibliographic record
Abstract
Infrastructure deterioration has been attributed to insufficient maintenance budgets, lacking restoration strategies, deficient deterioration prediction techniques, and changing climatic conditions. Considering that the latter adds more challenges to the former, there has been a growing demand to develop and implement climate-informed infrastructure asset management strategies. However, quantifying the impact of the spatiotemporally varying climate metrics on infrastructure systems poses a serious challenge due to the associated complexities and relevant modelling uncertainties. As such, in lieu of complex physics-based simulations, the current study proposes a glass box data-driven framework for predicting infrastructure climate induced deterioration rates. The framework harnesses evolutionary computing, and specifically multigene genetic programming, to develop closed-form expressions that link infrastructure characteristics to relevant spatiotemporal climate indices and predict infrastructure deterioration rates. The framework consists of four steps: 1) data collection and preparation; 2) input integration; 3) feature selection; and 4) model development and result interpretation. To numerically demonstrate its utility, the proposed framework was applied to develop deterioration rate expressions of two different classes of concrete and steel bridges in Ontario, Canada. The developed predictive models reproduced the observed deterioration rate of both bridge classes with coefficient of determination (R2) values of 0.912 and 0.924 for the training subsets and 0.817 and 0.909 for the testing subsets of the concrete and steel bridges, respectively. Attributed to its generic nature, the framework can be applied to other infrastructure systems, with available historical deterioration data, to devise relevant effective asset management strategies and infrastructure restoration standards under future climate scenarios.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".