Application of artificial neural networks for the prediction of traction performance parameters

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.

Deep Neural Networks for Inverse Identification of Soil Parameters

Predicting Buckling Load of Slender Hollow Rods Using Machine Learning: Model Comparison and Input Sensitivity Analysis

Predicting the critical buckling load of slender structural rods is essential for reliable and weight-efficient design of automotive steering and suspension linkages such as tie rods. This study evaluates the performance of four machine learning models such as artificial neural network (ANN), support vector regression (SVR), Gaussian process regression (GPR), and random forest (RF) in predicting the critical buckling load (Pcr) from geometric and material design parameters which are rod length (L), diameter (D), wall thickness (t), Young’s modulus (E), and initial geometric imperfection (δ₀). A dataset was generated using a parametric MATLAB code, and models were trained on an 80/20 train-test split with min-max normalized inputs. ANN and GPR achieved near-perfect predictive accuracy (R²=1.000), outperforming SVR and RF (R² = 0.92-0.96). Input sensitivity was assessed using permutation importance across all four models, complemented by Garson’s algorithm and the connection weight method. Rod length and diameter were consistently identified as the dominant parameters governing buckling resistance, jointly accounting for the majority of predictive importance; when length was held constant, diameter alone emerged as the leading parameter, followed by comparable contributions from wall thickness and Young’s modulus. Initial imperfection showed negligible influence according to all permutation-based methods, although the connection weight method disagreed sharply, illustrating a key limitation of weight-based sensitivity analysis relative to permutation-based approaches.

Comparative Analysis of Neural Networks and Decision Trees for Roughness Prediction

In the current context of the manufacturing industry, optimizing the cutting parameters to achieve a controlled surface roughness involves costly and time-consuming experimental efforts. The present study addresses this challenge by developing robust machine learning-based approximate functions for predicting surface roughness (Ra) resulting from toroidal milling on a five-axis CNC. The research includes an experimental design conducted under real production conditions on C45 steel. The relatively small experimental dataset was augmented, normalized, and then scripts were written for four prediction models: two artificial neural network architectures and two models based on decision trees. Their performance was analyzed based on MSE, RMSE, R2, and MRA metrics. The results obtained reveal significant differences between the models, highlighting solutions with high accuracy, excellent robustness, and superior generalization capacity for new data. The study highlights the high potential of prediction models in optimizing machining processes, providing an effective way to reduce costly physical experiments and increase productivity in industrial environments.

ANN Application for Predicting and Investigating Dynamic Response of a Slider-Crank Mechanism with an Elastic Element

Incorporating elastic elements into planar mechanisms offers significant advantages in terms of energy efficiency, impact load reduction, and the elimination of dynamic reaction forces during operation. However, the mathematical models describing the dynamics of these mechanisms typically involve complex numerical computational processes that require considerable time when directly applied to response analysis or design optimization tasks. In this study, a surrogate model based on Artificial Neural Networks (ANN) is developed to predict the dynamic response of a slider-crank mechanism with an attached elastic element. The ANN model is trained using data from prior publications and validated through standard error metrics. The results demonstrate that the proposed model achieves a prediction error of less than 6% and reduces computational time by a factor of more than 30 compared to the direct mathematical model. Comparative benchmarking against Support Vector Regression (SVR) and Polynomial Regression further validates ANN as the only surrogate model achieving consistent accuracy across all four output variables for this strongly nonlinear, multi-output dynamics problem. This approach opens a sustainable and promising avenue for addressing multibody dynamics problems in the future.