District-scale numerical analysis of settlements related to groundwater lowering in variable soil conditions
Bibliographic record
Abstract
This study presents a novel framework in which numerical modelling contributes to the performance of district-scale, subsidence-induced damage assessment in cities where ground settlements affect entire quarters. Therein, the implementation of expeditious procedures offers geotechnical engineers the possibility of contributing beyond the typical site scale. For this purpose, several “typified” hydro-geomechanical-loading (HGL) models, which represent (simplified) scenarios of masonry buildings undergoing settlements, were set up to account for different predisposing or triggering factors (i.e., soil heterogeneity, loading conditions, and groundwater variations) of settlement occurrence in built-up areas. These models exploit multi-source, wide-area input datasets encompassing the hydro-mechanical properties of geomaterials, in situ investigations and measurements (e.g., groundwater levels in wells), and innovative remote sensing data (i.e., DInSAR techniques). With reference to a district in Rotterdam City (the Netherlands), which was built on “soft soils”, the numerical simulations of different scenarios (i) provide an overview of the comparative role of predisposing or triggering factors on settlement occurrence and (ii) allow assessments of the expected induced damage to masonry buildings over 30 years with the exploitation of fragility curves. Considering the widespread diffusion of such geohazards, the proposed approach could help prioritise (rather expensive) maintenance work to the built heritage within sustainable strategies for subsidence risk mitigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".