Stability Analysis and Geotechnical Characterization for Slope Urbanization Using Geotextiles, Drainage Systems and Deep Foundations in Southwestern Loja, Ecuador
Bibliographic record
Abstract
This study presents an integrated geotechnical and slope stabilization approach aimed at ensuring safe urban development at the crown of unstable slopes in the southwestern sector of Loja, Ecuador.The area, characterized by heterogeneous lithologies (claystones, sandstones, and conglomerates) and complex geomorphological and hydrological conditions, poses critical challenges to construction, especially due to low shear strength and high rainfall intensity.Field exploration, including four Standard Penetration Tests (SPT) and extensive geological mapping, identified zones with bearing capacities between 33.00 and 37.79 Tn/m².To enhance slope stability and enable construction of three-story housing structures, a multipronged mitigation strategy was designed.This included retaluzing, the application of high-tensile woven geotextiles (TR5000 HF), installation of horizontal drains to manage subsurface water flow, and the proposed inclusion of micropiles to transfer loads to deeper, more stable soil layers.Stability modeling through Plaxis 2D showed a significant increase in the safety factor from 0.98 (current condition) to 1.55 after intervention.The study confirms that such geotechnical reinforcements provide a cost-effective, environmentally friendly, and safe solution for urban expansion in topographically constrained Andean regions.
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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 | 0.000 |
| 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".