Correlation of SPT and Seismic Refraction Tests to Obtain Dynamic Soil Parameters. Palestina, Guayas-Ecuador
Bibliographic record
Abstract
Obtaining dynamic soil parameters is key to assessing hazards in civil works.Soil characterisation contemplates field measurements and interpretations that require a comprehensive analysis for the design to offer guarantees.The Palestina-Ecuador canton is affected by heavy rains in winter, cutting off more than 16,000 inhabitants.The integral study of effective techniques for constructing bridges is a priority for the sector's economic development.This work aims to obtain dynamic parameters through the correlation of tests (Standard Penetration Test) SPT and seismic refraction for the characterisation and viable design of civil works in the Palestina-Ecuador canton.The research methodology consists of three phases: 1) Approach to fieldwork by gathering basic information; 2) Field measurements (SPT tests and seismic lines) and laboratory; 3) Correlation and analysis P-wave seismic velocity (Vp) and SPT for determination of dynamic parameters.The subsoil presents Quaternary alluvial sediments, where soft clays and loosely compacted silts predominate.The execution of 140 SPT tests in the laboratory of eight perforations allowed the correlation with six seismic refraction lines in the four precincts of interest for the construction of bridges: El Carmen, La Corona, Las Peñas, and Lagarto estuary, the field tests were distributed.The technical analysis resulted in a linear regression equation that correlates N60-Vp and the establishment of in-situ dynamic parameters (Cohesion and internal friction angle).These calculations made it possible to formulate a correlation equation that presents a validated method for this canton, due to data correlation; providing a tool that allows a detailed and economic characterisation for the rural sector.However, it is necessary to establish other places of analysis to strengthen the proposed equation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".