Susceptibility modelling of seismically induced effects (landslides and rock falls) integrated to rapid scoring procedures for bridges using GIS tools for the Lowlands of the Saint-Lawrence Valley
Bibliographic record
Abstract
Assessment of the seismic vulnerability of a bridge structure relies on its structural characteristics and site data. However, seismically induced effects, such as landslides or rock falls, are often unknown at a bridge site, making seismic risk assessment difficult. The objective of this paper is to develop a methodology to produce susceptibility map for landslides and rock falls for the Lowlands of the Saint-Lawrence Valley based on geological and slope models, groundwater table and proximity to watercourses. The methodology is inspired by the concepts of the methodology of Hazus and adapted to the specificity of the region of study. The final map of susceptibility to landslides shows a predominance of high level of susceptibility. The digital inventory of landslides is compared to the landslides susceptibility levels. Sixty-eight percentage (68%) of landslides from the inventory are located on areas evaluated as highly or very highly susceptible. Therefore, the method of attribution of susceptibility level is well correlated to the inventory of landslides. This map is used within scoring procedures for a rapid assessment of the seismic vulnerability of bridges giving a better classification of the most vulnerable installations, improving the effectiveness of mitigation measures and the efficiency of emergency planning.
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 | 0.000 | 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 | 0.001 | 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 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".