Strategic assessment of bridge susceptibility to scour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it