Estimation of California Bearing Ratio Using Soil Parameters: A Finite Element Coupled Regression Modelling Approach

Sensitivity-Driven Evolutionary Polynomial Regression for Tropical Subgrade Permanent Deformation

This study developed an interpretable Evolutionary Polynomial Regression framework to predict permanent deformation of tropical soil subgrades in semi-rigid pavement structures. The database combined repeated-load triaxial test parameters reported in Brazilian studies with controlled mechanistic-empirical simulations representing traffic demand, structural thickness, subgrade Poisson’s ratio, and soil properties. Candidate equations were generated through a hybrid evolutionary search combining Genetic Algorithm and Differential Evolution, and final models were selected by jointly considering statistical performance, parsimony, and Monte Carlo sensitivity consistency. Six predictors were retained: number of axle-load repetitions, percentage passing the No. 200 sieve, optimum moisture content, clayeyness coefficient, laterization index, and equivalent pavement thickness. Model assessment used three repeated random train-test partitions. The selected equations contained three polynomial terms and achieved testing coefficient-of-determination values from 0.943 to 0.951, root mean square errors from 0.214 to 0.238 mm, and mean absolute errors from 0.163 to 0.169 mm. Sensitivity and Shapley analyses showed physically consistent trends, including increased deformation with traffic loading and laterization index, and reduced deformation with equivalent pavement thickness.

A hybrid ANN–FEM framework for high accuracy slope stability prediction

Purpose. To develop a hybrid methodology that integrates Artificial Neural Networks (ANN) with Finite Element Method (FEM) simulations for the rapid and accurate prediction of slope stability. Methodology. A dataset of 1,000 FEM simulations was generated by systematically varying seven key input parameters: slope geometry (height and angle) and soil properties (cohesion, friction angle, unit weight, pore water pressure ratio, and reinforcement type). An ANN model with a (7-10-1) feedforward architecture was trained on this data. Findings. The model demonstrated exceptional predictive performance, achieving a near-perfect correlation coefficient (R  0.999997) and an extremely low mean squared error (MSE = 3.6828  10-6), showing close agreement with the FEM-computed factors of safety (FOS). A comprehensive sensitivity analysis based on analysis of variance identified the pore water pressure ratio as the dominant controlling parameter, contributing approximately 77 % to the variability of FOS, followed by cohesion with a contribution of about 13 %. Complementary correlation analysis revealed that cohesion exhibits the strongest linear correlation with FOS (r = 0.83), whereas the pore water pressure ratio shows a relatively weak linear correlation, highlighting its pronounced nonlinear and interaction-driven influence on slope stability. These results demonstrate that the proposed ANN–FEM hybrid framework provides a powerful, efficient, and reliable tool for slope stability assessment and parametric analysis. The methodology is particularly well suited for engineering applications requiring rapid decision-making, large-scale evaluations, and uncertainty analysis. Originality. The core originality of this research is its development of a robust ANN–FEM hybrid framework applied to a large, systematically generated dataset of 1,000 slope simulations. Unlike many studies, it comprehensively incorporates seven critical input variables, including the often underrepresented pore water pressure. Furthermore, its scientific rigor is enhanced by a dual interpretability strategy that combines analysis of variance for quantifying parameter contribution and correlation heatmaps for distinguishing linear effects from nonlinear ones, providing deeper insight into slope stability mechanisms. Practical value. This study provides engineers with a fast and reliable tool to predict slope safety in seconds instead of running time-consuming FEM simulations, making it highly valuable for real-time decision-making and large parametric studies. Practically, it also shows that controlling pore water pressure (through drainage) is the most effective risk-reduction strategy, while cohesion offers a predictable way to improve slope stability in design.

Predictive Modelling and Optimization of Slope Stability Using Numerical Simulations and Machine Learning Techniques

The current study combines numerical modelling and machine learning to identify the stability of applications in nail reinforced slope study. PLAXIS LE was used to develop different slopes having different soil properties including various values for cohesion (5, 10, 15 kPa), angle of internal friction (20°, 25°, 30°), unit weight (17, 18, 19 N/m³), and slope angle (30°, 35°, 40°, 45°, 50°, 60°, 70°). Safety Factors (FOS) prediction models such as Random Forest (RF), Linear Regression (LR), and K-Nearest Neighbors (KNN) have been developed using the parameters included in the study. The Random Forest model has shown a superior performance among the other models with the lowest Mean Absolute Error (MAE: 0.053) and Mean Squared Error (MSE: 0.006), taking into consideration the highest value of R² (0.957) and Adjusted R² (0.951) to indicate a better predictive accuracy. With R² values of 0.903 and 0.920, respectively, Linear Regression and KNN also showed considerable strength of results. The results mentioned above show the bright future of machine learning models with Random Forest in predicting slope stability and contribute to refining nail reinforcement strategies. It shall also provide an input for developing cost-effective and robust slope rehabilitation measures in a geotechnically unfriendly environment.

Data-Driven Mechanical ROP Prediction: Construction and Validation of Committee Machine Model

With the continuous development of drilling technology, accurately predicting mechanical penetration rates is particularly important for improving operational efficiency and reducing costs. Existing methods often struggle to provide reliable predictions when faced with complex geological conditions and variable drilling environments because they primarily rely on traditional models and fail to adequately consider various influencing factors and their nonlinear relationships. To ad-dress these issues, this paper proposes a mechanical penetration rate prediction model based on committee machines. This model effectively captures the variability characteristics of mechanical penetration rates by integrating multiple expert models while employing wavelet filtering methods to denoise the data to enhance data quality. In the application case, this paper collects relevant drilling parameter data based on a vertical well in a specific block. The evaluation of the model shows that it performs excellently in key indicators such as mean square error, coefficient of determination, root mean square error, and mean absolute error, particularly demonstrating a high predictive capability and stability by explaining 97.19% of data variability. The advantage of the constructed model lies in its strong ensemble learning ability, which not only enhances the prediction accuracy of mechanical penetration rates but also helps to deepen the understanding of the dynamic changes in the drilling process, providing effective support for subsequent drilling optimization and resource development.

Pseudo-static bearing capacity of skirted footings on sandy slopes using artificial neural network and random forest regression models

This study investigates the pseudo-static bearing capacity of skirted strip footings on cohesionless slopes using finite-element limit analysis and data-driven prediction models. A total of 216 numerical simulations were performed by varying soil strength, slope angle, seismic coefficient, footing location and skirt depth. The results showed that the inclusion of vertical skirts significantly enhances footing performance under seismic loading. However, increasing the horizontal seismic coefficient from 0 to 0.4 caused a considerable reduction in bearing capacity. Artificial neural network (ANN) and random forest regression (RFR) models were developed using 70% of the data set for training and 30% for testing. Both models achieved high prediction accuracy with coefficient of determination (R2) values greater than 0.90. The ANN model outperformed the RFR model, achieving a maximum R2 value of 0.97. Sensitivity analysis indicated that skirt depth and width of the footing are the most influential parameter, contributing approximately 50.49% to the overall footing response. The proposed models provide a rapid and reliable approach for estimating the seismic bearing capacity of skirted foundations on sandy slopes.

Robust Evaluation Framework for Compression Index Prediction Models