Prediction of Active Earth Pressure in Constrained Backfill Retaining Walls using Support Vector Regression and Traditional Datafit based Non-Linear Regression
Bibliographic record
Abstract
Constrained Backfill Retaining Walls (CBRWs) are increasingly utilized in geotechnical engineering, particularly in mountainous regions and densely populated urban areas where space limitations demand innovative design solutions.These walls exhibit unique geometric configurations, leading to complex earth pressure behaviors that deviate significantly from those observed in conventional retaining walls.Accurate prediction of the coefficient of active earth pressure (Ka) is crucial for ensuring the stability and safety of CBRWs.However, traditional methods using data-fitting software, though often yielding high R² values (up to 0.99), may not provide reliable predictions in real-world applications.To address these shortcomings, this study investigates the application of machine learning (ML) techniques, specifically Support Vector Regression (SVR), as an advanced method for predicting Ka in CBRWs.The current investigation models three failure mechanisms involving single, double, and triple rigid blocks, depending on the distance between the rock face and the retaining wall, to generate a dataset of Ka, calculated through in-house MATLAB codes.These computations are both time-consuming and computationally intensive, highlighting the necessity for an efficient predictive model.The SVR model is employed to predict Ka, and its performance is compared against conventional data-fitting approaches.The input parameters for the predictions include -aspect ratio (b/h), internal frictional angle of the soil ( φ ), rock-face angle (η ), retaining wall inclination ( β ), backfill inclination ( ε ), interface friction angle between rock-face and soil ( δ ), and interface friction angle between retaining wall and soil (ψ ).The model effectiveness is evaluated using multiple error metrics.The SVR model achieved a coefficient of determination (R²) value of 97.4%, demonstrating strong predictive capability.In contrast, conventional data-fitting software exhibits limitations in accurately capturing the complex Ka behavior in CBRWs.The findings of this study underscore the potential of SVR to enhance the design and analysis of CBRWs by offering greater accuracy and computational efficiency compared to traditional methods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".