Development of a Program for Calculating Soil Settlements Using Classical Geotechnical Methods: A Case Study in the City of Pucallpa
Bibliographic record
Abstract
The calculation of soil settlements is essential to guarantee the stability of structures in varied soils.Although classical geotechnical methods are used, such as elasticity theory and Terzaghi consolidation, the manual calculation of complex settlements can be laborious, making the use of specialized software necessary.In this context, Settle Classic was developed, an innovative program that allows settlement calculations without requiring commercial licenses and has a user-friendly interface that makes it easy to learn theoretical principles.The program was used in a case study in Pucallpa to obtain the total settlement experienced by the soil when a university infrastructure is built on it, for which the soil properties, foundation data and external load were entered, obtaining a total settlement of 6.018 cm, which indicates that this settlement exceeds the permitted limit for slabs established for this project (5.04 cm).Likewise, when validating the program with Settle 3D, a margin of error of 0.36% was shown for the settlement calculation of the case study, which validated the precision and reliability of the developed program.Additionally, the layer method was carried out in the developed program, dividing a stratum into several sub-strata, observing that the total settlement presents a slight variation as the number of sub-strata increases, which provides a value closer to the reality.On the other hand, the calculation of immediate settlement in granular soils was validated with a manual calculation using elasticity theory giving a percentage error of 0.37%.Therefore, the program developed Settle Classic is recommended for the calculation of immediate and consolidation settlements as it provides reliable results and is convenient for civil engineering students to verify their manual calculations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".