Long term cost analysis of alternate fatigue management strategies for steel highway bridge welds
Bibliographic record
Abstract
In order for bridge managers to evaluate the consequences of adopting new fatigue retrofitting techniques and management strategies on the cost of maintaining their bridge infrastructure, simple predictive models are needed, which can be easily integrated with the analytical tools that are already being using to model other deterioration processes (e.g. corrosion, road surface wear). These models must be capable of predicting the effects of inspection and retrofitting events with a sufficient degree of accuracy to ensure that optimal management strategies are correctly identified. Considering the large number of fatigue-prone welds and structures that may be present in a road network, minimizing computational effort is also critical. In this paper, a simple Markov chain deterioration model, similar to those currently used in bridge management systems (BMSs) to model deterioration due to other processes, is used to determine critical cost ratios for selecting optimal fatigue management strategies for steel highway bridge welds, First, the model is briefly described. A study is then presented, wherein the long term costs associated with different management strategies are related to parameters such as the equivalent stress range, traffic volume, and intervention costs for a generic weld detail. The results of this study are used to establish critical cost ratio contour plots, which can be used for the selection of the optimal management strategy. A limited number of strategies are investigated, in order to demonstrate an application of the presented methodology. They are composed of different intervention types, including: inspection, repair, replacement, and the use of post-weld 'peening' treatments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".