Impact of Extreme Events on Transportation Infrastructure in Iowa: A Bayesian Network Approach
Bibliographic record
Abstract
Iowa’s roadway network is an important part of the state’s transportation infrastructure and plays a critical role in the functionality and economic development of the entire state. This network primarily consists of three interstate highways that pass through Iowa, connecting it to the neighboring states and eventually Canada. Various businesses are located near this roadway network and rely on it for everyday operation. In recent years, however, the growth of agricultural and biofuel industries has intensified the demand on the roads and bridges in Iowa. The state’s roads and bridges have also witnessed a number of flooding events, which have caused extensive traffic disruptions and economic losses. Thus, it is imperative to develop a fundamental approach to evaluate the impact of extreme events on the transportation infrastructure of Iowa and other similar states. Towards this goal, the current study investigates the existing condition of Iowa’s transportation infrastructure, possibility of occurrence of extreme weather events, and scenarios that may lead to the failure of transportation infrastructure components. For this purpose, the capabilities of Bayesian belief networks are utilized to quantify the effects of extreme precipitation and extreme temperature on the performance of transportation infrastructure and then predict the probability of damage to roads and bridges. This will be achieved through the identification of the most influential factors using a set of sensitivity analyses, assessment of overall vulnerability with evidence-based propagation analyses, and quantification of response to extreme weather events, taking into consideration climate projections.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".