Structural Redundancy, Robustness, and Disproportionate Collapse Analysis of Highway Bridge Superstructures
Bibliographic record
Abstract
Performance-based design and system-level assessment methods are becoming the preferred approaches for evaluating the safety of structures. This is particularly important for highway bridges where, because of their exposure to long-term deterioration as well as sudden localized failures, the generally conservative traditional member-oriented approach does not necessarily lead to an accurate evaluation of the actual structural system’s safety levels nor, consequently, to the efficient allocation of the limited resources available for infrastructure management. The objective of this paper is to quantify the effect of damage size and location on bridge elements and how this affects the performance of the entire superstructure system. The paper also presents a simplified equation for estimating the structural robustness of typical highway girder bridge superstructures as a function of the damage type. A numerical example is presented to illustrate alternative approaches for how these concepts could be implemented during the design and safety assessment of highway bridges. In particular, the analysis showed that the occurrence of damage directly under the live load reduced the ultimate capacity of the system in the range of 70%–95%. This reduction was between 40% and 70% when the damage was located away from the loaded zone.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 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".