Framework Methodology for Risk-Based Decision Making for Transportation Agencies
Bibliographic record
Abstract
This study develops a framework for risk-based decision making for the design, operation, and maintenance of various types of transportation facilities and entities. This framework is grounded in current practice and risk management theories and operationalizes a decision-making framework that is applicable at multiple levels in an organization. The framework defines all crucial steps: technical components of risk assessment, communication logistics, and information systems. The approach is illustrated by two examples. The primary example demonstrates the framework through the context of allocating resources for the inspection and maintenance of a portfolio of signalized mast arms. A qualitative risk assessment method is used, informed by extensive inspection records from the Colorado DOT and finite-element models for mast arms, to recommend varied inspection frequencies based on current structural defects present. A secondary example uses a more quantitative risk assessment approach to inform seismic design decisions for bridges in Colorado. Through the literature review and presented examples, this study develops the resources and information necessary to implement a risk-based methodology in decision making within a transportation agency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".