Stability assessment of homogeneous slopes loaded with mobile tracked cranes—An artificial neural network approach
Why this work is in the frame
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Bibliographic record
Abstract
Construction projects often involve the use of mobile crawler cranes to excavate, backfill, dredge or move material and equipment on or near slopes. Crane manufacturers often only provide guidelines for the safe operation of cranes with respect to over tipping. However, the complex interaction of many variables such as the crane, its load, the slope geometry and its geotechnical properties can create slope instability. In this study, an artificial neural network was developed to predict the stability of these slopes loaded by mobile cranes. The neural network was built and trained using a set of slope stability models that were constructed using the above parameters via Monte Carlo sampling. The trained network was capable of predicting the factor of safety of a loaded slope and the location of the critical failure surface with relatively low error. In addition, the quality of the network’s output was investigated using multiple metrics, such as the correlation ratio or the mean squared error and quite high correlation was achieved. Thus, the predicting capabilities of the network can be used with confidence to aid the positioning of mobile cranes on slopes without a need to perform slope stability analysis for each scenario.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it