Assessment of Ultimate Bearing Capacity Using Hybrid Gaussian Process Regression Approach
Bibliographic record
Abstract
This article introduces an innovative approach to predicting the ultimate bearing capacity Qu using Gaussian Process Regression (GPR) analysis. The research establishes a robust connection between Qu and crucial natural soil attributes, as well as the quantity of stabilizing additives, using GPR models. Specific GPR prediction models are meticulously crafted to ensure precise Qu estimations, addressing the complexities inherent in soil stabilization processes. To bolster the accuracy of these models, two meta-heuristic algorithms, Prairie Dog Optimization (PDO) and Dynamic Arithmetic Optimization Algorithm (DAOA), are seamlessly integrated into the study. These algorithms verify the models by analyzing Qu samples from different soil types collected during previous stabilization tests. The study presents three distinct predictive frameworks: the GPR–PDO model (GPPD), the GPR–DAOA model (GPDA), and a standalone GPR model, each providing valuable insights for precise Qu prediction under diverse soil conditions. Among these, the GPAS model emerges as a standout performer, achieving an impressive R² value of 0.996 and an exceptionally low RMSE value of 658.99, demonstrating superior predictive capability. The findings highlight the model's accuracy, robustness, and practical relevance for forecasting soil stabilization outcomes in engineering applications. This integrated approach not only provides reliable predictions for Qu but also emphasizes the role of meta-heuristic algorithms in improving model performance. By offering a precise, efficient, and adaptable methodology, the study presents significant implications for the construction industry, enabling engineers and designers to optimize soil stabilization strategies, reduce risk, and enhance overall project safety and cost-effectiveness. The combination of GPR modeling with advanced optimization techniques represents a forward step toward intelligent, data-driven geotechnical engineering practices.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".