Strategic assessment of bridge susceptibility to scour
Bibliographic record
Abstract
Scour-induced failures of bridges pose a global challenge, leading to significant economic and service losses. Compounded by infrequent inspections and inadequate consideration of hydro-geological factors in current scour risk assessments, this issue is particularly pressing in the context of climate change and associated hazards. Addressing the imperative for enhanced infrastructure resilience, this study introduces a framework for scour risk management. Utilizing Geographic Information Systems (GIS) datasets and applying the Analytic Hierarchy Process (AHP) to assess various weighted factors affecting scour risk, we have systematically mapped information layers encompassing structural, riverine, geological, and flood risk to conduct a strategic scour susceptibility assessment. The proposed approach is applied to the railway network in southeast England, identifying scour-susceptible bridges that can be prioritized for detailed inspections. Compared to the existing scores, the proposed scour risk scores for approximately 30 railway bridges in the region were adjusted, with 22 transitioning from medium to high priority. Our proposed methodology, exemplified by this case study, offers asset managers deeper insights into the determinants of scour susceptibility of bridges and facilitates informed decision-making for prioritizing scour-mitigation measures across the network.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".