Vulnerability analysis of civil works focused on the geological-geotechnical state in the El Plateado sector of the city of Loja
Bibliographic record
Abstract
The assessment of geological-geotechnical vulnerability in urban areas represents a critical challenge for the development of safe infrastructure.In this study, we focus on the El Plateado sector of Loja, Ecuador, implementing a methodology that integrates geophysical, geomechanical, and geographic information system (GIS) techniques to comprehensively characterize ground susceptibility.Our research applied Electrical Resistivity Tomography (ERT) to diagnose subsurface conditions, complemented by geomechanical Atterberg boundary testing and triaxial testing to determine fundamental geotechnical properties.The analysis considered critical variables such as lithology, proximity to mass movements, and building typology to generate a spatial vulnerability model.The results showed a complex stratigraphy of sands, silts, and clays, with subsurface saturation at approximately 10m depth.Geotechnical characterization revealed high plasticity SP-CH and low plasticity SP-ML soils, with mechanical properties of moderate consistency.However, local geological factors suggest the need to implement improvement strategies before any construction intervention.Vulnerability zoning yielded a vulnerability index above 70%, which implies significant restrictions for urban development and demands detailed geotechnical studies before future interventions.Our work aims to contribute to local geological knowledge and offer a replicable methodology for territorial vulnerability assessments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".