SVR-Based Framework for Predicting Stability of Circular-Failure Slopes with Small Sample Size

Reliable prediction of the factor of safety (Fs) of circular-failure soil slopes is critical to geotechnical practice. Data-driven models developed on small slope-stability datasets are, however, prone to overfitting, data leakage, and optimistic bias, which can lead to overestimated predictive performance. This study presents a small-sample-oriented, leakage-aware support vector regression (SVR) framework with a radial basis function (RBF) kernel for Fs prediction. A database of 80 published circular-failure slope cases was compiled, and six predictors were adopted: soil unit weight, slope height, pore pressure ratio, cohesion, internal friction angle, and slope angle. To improve reliability under limited-data conditions, preprocessing, hyperparameter tuning, and performance evaluation were all embedded within a repeated nested cross-validation framework. The proposed SVR model was benchmarked against the back-propagation neural network (BPNN) and radial basis function neural network (RBFNN) models under identical validation partitions and evaluation settings. The results indicated that SVR achieved the best predictive performance among the three candidate models. For case-level illustration, a single representative hold-out split was reported in addition to the repeated nested cross-validation results, on which the SVR model attained an R2 of 86.56%, an RMSE of 0.07497, an MAE of 0.0666, and an MRE of 5.29%. In this test subset, all SVR predictions exhibited relative errors below 10%, indicating more stable predictive behaviour than the benchmark models. The main contribution of this study is thus a validated SVR framework for small-sample conditions.