Inspection and Management of Bridges with Fracture-Critical Details
Bibliographic record
Abstract
This synthesis may be useful to bridge owners and consulting engineers engaged in the design, inspection, and management of bridges with fracture-critical details, as a guide to present specifications and engineering judgment. It focuses on the inspection and maintenance of bridges with fracture-critical members (FCMs), as defined in the AASHTO LRFD Bridge Design Specifications. The objectives of this report were to survey and identify gaps in the literature; determine practices and problems with how bridge owners define, identify, document, inspect, and manage bridges with fracture-critical details; and identify specific research needs. Among the areas examined were: inspection frequencies and procedures; methods for calculating remaining fatigue life; qualification, availability, and training of inspectors; cost of inspection programs; instances where inspection programs prevented failures; retrofit techniques; fabrication methods and inspections; and experience with FCM fractures and problems details. This synthesis contains information obtained from a survey distributed to bridge owners and consultant inspectors (72 state, provincial, and international departments of transportation and agencies), a literature search, and targeted interviews. Useful responses were received from 34 states and three Canadian provinces.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".