A MATLAB GUI‐Based Calculation Platform for Soil Arching Effect to Assist Teaching and Learning in Soil Mechanics
Bibliographic record
Abstract
ABSTRACT The soil arching effect is a key concept in soil mechanics education. It is widely recognized as an important principle in geotechnical engineering, characterized by stress redistribution due to relative soil displacement, which impacts the safety and stability of geotechnical structures. Despite advances in classical theories and numerical methods, the complexity of models and formulas still presents significant challenges for students and engineers in understanding and application. To address this challenge, this study introduces a practical and educational solution by developing a computer‐aided calculation platform for the soil arching effect, designed by Hunan Provincial Engineering Research Center of Advanced Technology and Intelligent Equipment for Underground Space Development in Hunan University, aimed at enhancing soil mechanics education through an intuitive MATLAB graphical user interface. The primary contribution of this study is the development of a platform that integrates seven theoretical models, enabling users to calculate key parameters, such as the soil arching ratio, by inputting soil properties and unloading width. The platform features real‐time data visualization and interactivity, allowing users to easily select models, input parameters, and obtain results quickly, thereby facilitating comparative analysis across different theoretical frameworks. Compared to conventional teaching methods, the platform simplifies complex calculations and deepens students’ understanding of the soil arching effect. Results from student surveys indicate a remarkable improvement in comprehension and analytical skills, with high satisfaction regarding the platform's usability and educational value.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".