Development of a Risk Assessment Module for Bridge Management Systems in New Jersey
Bibliographic record
Abstract
Bridges are critical for the mobility of our society and its economic growth. Available funds for bridge repair, maintenance, and rehabilitation are limited. The Moving Ahead for Progress in the 21st Century Act (MAP-21) introduced several new parameters for improving the management of bridge assets, such as bridge element evaluation, life-cycle analysis, and risk-based performance indicators. Risk-based methods account for the uncertainties embedded into engineering variables and long-term evaluations. The objective of this paper is to identify, assess, and quantify structural risk components to bridges using probabilistic risk methodologies and data from the National Bridge Inventory database. The aim is to simplify the implementation of risk-based ranking procedures into bridge management system packages according to the MAP-21 vision. Therefore, machine learning techniques are employed to facilitate the introduction of probabilistic risk methods into bridge management systems. The procedure is described for seven hazards that are pertinent to bridges in New Jersey: overloading, fatigue, seismic, flooding, scour, vehicle and vessel collision. Risk values are computed in monetary terms to homogenize the comparison among bridges for different hazards. The analysis is performed on 5,534 bridges, showing that seismic events and fatigue resulting from truck overloading are the most dominant hazards in New Jersey, for which about 97.0% and 29.0% of bridges show some level of risk. The main limitation of the proposed framework is the lack of accurate data from bridge inventories necessary to thoroughly perform a fully structural probabilistic analysis of bridges and to minimize engineering judgment.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".