Evaluating the Benefit of Using AI to Predict the Values of BCI and Cost of Bridge Infrastructures Under the Impact of Climate Change in Ontario
Bibliographic record
Abstract
Efficient bridge management is crucial for governments, yet this task is increasingly challenging due to the accelerated deteriorations due to the climate change.In Ontario, the bridge infrastructure network is composed of 5,053 bridges with records from 2000 to 2020.Biannually, inspectors perform a bridge inspection and assign a grade to each structure that helps track, plan, and budget for their maintenance or replacement.In this study, two models were developed by using two machine learning and two statistical algorithms in R language with the focus on each region of the Ontario province in Canada.The models were made to predict the Bridge Condition Index (BCI) for bridges, the Investment Cost (IC) for infrastructure projects, and the associated data (e.g., bridge condition data, traffic volume, climate data), which were collected from the Ministry of Transportation (MTO) in Ontario and the Canadian Government.The BCI model uses a multivariable linear equation with R 2 = 0.85, Mean Absolute Error (MAE)=1.78,Root Mean Square Error (RMSE) = 3.82 and a Relative Error (RE)= 2.4% and the Cost model uses a GBM (Gradient Boosting Machine) model with R 2 = 0.99, MAE =3.27, RMSE = 4.22, and RE = 46.31%.These models can provide information about the bridge's maintenance and its investment strategies; however, they are not effective between the years of 2000 and 2022 because of the insignificant differences in temperature and precipitation changes across different Representative Concentration Pathways (RCP).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".