Machine Learning Study of Displacements in Soil Nail Walls Using a Real Case Dataset

Bayesian-Optimized Ensemble Machine Learning for Predicting Settlement of Cohesionless Soil Under Loads

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.

Robust machine learning prediction of surface roughness in SKD61 turning

Accurate and reliable prediction of surface roughness (Ra) is essential for intelligent machining of hardened tool steels under limited-data conditions. This study investigates the influence of cutting and geometric parameters on Ra during the external turning of SKD61 steel using a Taguchi L27 experimental design. The investigated variables included cutting speed, feed rate (f), depth of cut, tool nose radius (r), and workpiece diameter. A comparative machine learning framework was developed to evaluate four prediction models, namely artificial neural network, extreme learning machine (ELM), support vector regression, and random forest regression, with polynomial regression serving as the benchmark. Model performance was first evaluated using repeated 5-fold cross-validation and subsequently assessed using an independent external dataset comprising previously unseen intermediate parameter combinations within the investigated parameter domain. Taguchi analysis and ANOVA identified f and r as the dominant factors affecting Ra. Among the investigated models, ELM achieved the highest prediction accuracy, yielding R2 = 0.9979 ± 0.0006 and MAE = 0.0397 ± 0.0065 µm during repeated validation, together with R2 = 0.9371 and MAE = 0.0845 µm on the independent external dataset. These results support the potential effectiveness of the proposed robustness-oriented evaluation framework for ML-assisted Ra prediction under limited-data machining conditions.

Interpretable machine learning for high-precision wall thickness prediction of hot-rolled seamless steel tubes

To improve the accuracy of axial wall thickness prediction and reduce reliance on manual measurements in hot-rolled steel tube production, a prediction method based on particle swarm optimisation (PSO) and a one-dimensional convolutional neural network (1D-CNN) was developed. A dataset was constructed from 131 industrial samples collected from the production line. An input variable set was established to characterise entry wall thickness, geometric and overall deformation indicators, pass schedule profile features, speed schedule profile and thermal conditions. PSO was then employed to optimise the key hyperparameters of the 1D-CNN. The proposed model was compared with several machine learning models. The results showed that the PSO-1D-CNN model achieved the best predictive performance, with a test root mean square error of 0.0281, a mean absolute error of 0.0217, and a coefficient of determination R 2 of 0.913. Further interpretability analysis using SHapley Additive exPlanations revealed that the centroid of the reduction distribution, the number of stands, the diameter-to-wall ratio, the tube temperature at the sizing exit, and the radial compression ratio were the most influential variables affecting the predictions. Finally, the proposed model was integrated into an online system. This system enables single tube wall thickness prediction, sawing parameter calculation, and batch visualisation for process adjustment and sawing decisions.

Machine Learning Prediction of the Ground Reaction Curve in Sand with the MATLAB GUI Platform

The proposed framework combined a curated database, neural network-based curve prediction, and hyperparameter optimization, providing a robust approach for evaluating the soil arching effect, providing a robust approach for evaluating the soil arching effect.

Experimental Investigation and Machine Learning-Based Prediction of Marshall Stability of Stone Mastic Asphalt Using Steel Slag

This study applies several machine learning models to predict the Marshall stability of Stone Mastic Asphalt mixtures incorporating steel slag as a partial replacement for conventional coarse aggregates, with CatBoost providing the best performance.