Modeling of an Automatic Excavation Mechanism for a Parallelepiped Foundation - Application in Building Construction Preparation
Bibliographic record
Abstract
Foundation excavation and general earthworks are activities that involve the machine operator in a series of repetitive and tedious operations, suggesting opportunities for automation through by the introduction of robotic technologies with subsequent improvements in the design and machine use, especially, in dangerous environments working. Automation of excavation operations can be realized by an automatically controlled excavator system that is able to perform autonomously a planned digging work. In this study, a modeling of an excavation mechanism has been represented, where the purpose is to automatically excavate a parallelepiped-shaped foundation. From existing excavation machinery in the industry, the manipulator arm of a backhoe was chosen to do the modeling. Starting from the basic input geometric parameters of the foundation to be excavated, the system gives as outputs results: A simulation of the various mechanism components movement, in addition to the automatic excavation trajectory of the parallelepiped foundation. Finally, from the soils properties of the western region of Algeria that they were measured experimentally, a resistance simulation of the various components of the mechanism was carried, to test the reliability of the mechanism in terms of deformation and Von Mises stress.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".