Jun 2026· The Arabian journal for science and engineering· 0 citations· 64 references
Abstract
This paper presents a novel ensemble learning framework that integrates Adaptive Boosting Regression Threshold (AdaBoost.RT) with weighted extreme learning machines (WELMs) to improve seismic performance in intelligent control systems. In our approach, the Imperialist Competitive Algorithm (ICA) is used to optimize the relative error threshold of AdaBoost.RT, and WELMs are selected as base predictors due to their fast-training times and robustness in handling nonlinear phenomena. The proposed framework incorporates sample weights into the output weights of the ELMs and updates them iteratively, effectively capturing complex dynamic behaviors, including those arising from soil–structure interaction (SSI) under stochastic excitations. Comparative analyses with established ensemble learning techniques demonstrate that the presented optimized, fast, and efficient model achieves superior predictive accuracy, generalization capabilities, and computational efficiency. The methodology is validated through extensive simulations and experimental data, showcasing its potential for addressing smart control challenges in engineering applications. This research not only advances the state of the art in ensemble learning for active control systems but also contributes to the broader field of nonlinear dynamics by providing a reliable, efficient, and robust tool for system identification and vibration control in oscillating systems.
Purpose. This research aims to provide a physics-based ensemble machine learning framework that can reliably predict slope stability and distinguish stable from unstable slopes in static and seismic conditions. Standard analytical and numerical methods have significant processing costs and oversimplified assumptions that limit their usefulness.
Methods. The study analyzed 700 slope stability samples, including geotechnical and seismic factors such as slope height, slope angle, cohesion, internal friction angle, and peak ground acceleration. The proposed model now includes physics-based engineering elements, such as tan ϕ, c/H, and PGA/g, to account for geotechnical interactions. A linear meta-learner and Random Forest and Gradient Boosting regressors were used to develop a stacked ensemble framework. We assessed model strength and reliability.
Findings. The devised framework showcased exceptional prediction performance with an (R2) of 0.982, a mean absolute error of 0.02 and a root mean square error of nearly 0.03. Cross-validation showed consistent generalization. Random Forests classified slope stability conditions with 97.4% accuracy. The most important parameters for slope stability predictions were the internal friction angle and cohesiveness. Furthermore, the inclusion of carefully crafted physics-based features demonstrably improved robustness, accuracy and consistency.
Originality. This work introduces a pioneering, combined framework that fuses the mechanics of geotechnical soil with ensemble machine learning techniques, enhancing both interpretability and the certainty of slope stability predictions.
Practical implications. The framework may support rapid, cost-effective, and interpretable preliminary geotechnical risk assessment and design screening. However, its use in slope monitoring or early-warning applications requires further field validation and integration with monitoring data.
B. E. Elnaim, Mohammed Mnzool· Mining of Mineral Deposits· 0 citations
This paper presents an optimization‑driven framework for noise‑robust bearing fault diagnostics aimed at enhancing the reliability and design performance of rotating machinery systems. The proposed approach integrates an enhanced empirical ensemble Fourier decomposition (EEFD) with a support vector machine (SVM) whose hyperparameters are optimized using a quadratic interpolation particle swarm optimization with local search (QPSOL) algorithm.
To address signal degradation under harsh industrial environments, EEFD is employed to decompose vibration signals and extract high‑quality intrinsic components. A compact yet discriminative multi‑domain feature set, including Root Mean Square (RMS), Kurtosis, and Hjorth Mobility (HM), is constructed to characterize the dynamic behaviour of the system. The QPSOL algorithm is then utilized to optimize the SVM parameters, improving convergence accuracy and classification robustness.
Experimental validation under a 10 dB signal‑to‑noise ratio demonstrates that the proposed method achieves superior diagnostic accuracy and convergence performance compared with conventional optimization techniques. Beyond fault classification, the developed framework provides a basis for simulation‑driven design optimization and reliability‑oriented decision‑making in mechanical systems, enabling more effective predictive maintenance strategies and lifecycle performance improvement.
HungLinh Ao, B. Doan, HoaiQuoc Le· E3S Web of Conferences· 0 citations
This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soil structures. These models were developed to enhance both predictive accuracy and model interpretability by incorporating state-of-the-art optimization algorithms and explainable machine learning frameworks. A comprehensive evaluation was conducted using 10-fold cross-validation, and the proposed models were benchmarked against previously developed AI models from literature, as well as traditional and semiempirical approaches such as Rankine, Coulomb, and
K
-stiffness. Among the proposed models, ASSA-MBM consistently achieved the best performance, recording the lowest testing root mean squared error (0.617), the highest correlation coefficient (
R
=
0.918
), and the highest reference index (
RI
=
0.951
). Additionally, the ORF model offers transparency by generating mathematical regression equations, which are crucial in geotechnical engineering.
Min-Yuan Cheng, Akhmad F. K. Khitam, Jia-Wang Liou· Journal of computing in civi...· 0 citations
Predicting soil sliding displacement plays an essential role in developing landslide early-warning systems. Even though physics-based approaches, e.g., trapezoidal numerical integration, have demonstrated promising predictive performance under ideal laboratory conditions, they are very susceptible to field disturbances such as sensor noise and drift. They fail to adequately model the nonlinear behavior and transition phenomena of soil sliding events. Machine learning algorithms (ML), which are promising solutions for predicting soil sliding displacement due to their ability to handle nonlinearity, suffer from dependence on dataset properties. Sample size, noise levels, and feature correlations play major roles in determining model accuracy. This research presents an XGBoost-based stacked ensemble learning algorithm called X-SEL for predicting soil sliding displacement using IMU sensor data from flume experiments. This study investigates the capability of an XGBoost-based ensemble learner for soil sliding displacement predictions using three experimental datasets. Three different datasets were used for validating the X-SEL framework that correspond to varying sliding regimes, sample sizes, and feature correlations. The X-SEL framework had the lowest average root mean square error (RMSE), equal to 0.2714, compared with the benchmark models among the three datasets examined. Nevertheless, other individual learners, including kNN and XGBoost produced competitive results. Hence, it can be concluded that X-SEL does not always have higher predictive power than each of its base-learners. Nevertheless, X-SEL proved superior compared with the traditional trapezoidal integration method in replicating smoothly transitioning non-linear displacements. Furthermore, SHAP analysis suggested that the meta-learner adaptively weights reliable base-learner models, and triaxial acceleration features contribute more to displacement prediction than gyroscopic features. The X-SEL framework developed in this paper serves as a proof-of-principle of utilizing inexpensive IMU sensors for predicting soil sliding displacement. Future studies will aim at expanding X-SEL beyond the flume environment, conducting uncertainty quantification, and designing practical tools for deployment purposes.
Shubham Kumar, Mirothali Chand, K. V. Uday et al.· Geoenvironmental Disasters· 0 citations
Differential settlement at the junction of existing and new embankments is a critical challenge in road-widening projects. This study develops a data-driven machine learning (ML) framework to accurately predict this settlement, overcoming the limitations of traditional finite element methods. A comprehensive dataset was generated with finite element simulations (iSight-ABAQUS) and used to train and evaluate eight ML algorithms. Among these, the Gradient Boosting model demonstrated superior performance, achieving an R² value approaching 1.0, indicating exceptional predictive accuracy and generalization. The finalized model was implemented in a Python-based framework, enabling rapid forecasting of road behavior. This ML approach facilitates accelerated design optimization, identifies key influencing parameters like the modulus of elasticity, and supports proactive maintenance planning. The research establishes a transformative, data-driven paradigm for pavement engineering, offering a tool for rapid performance prediction and damage assessment that significantly outperforms conventional simulation-based methods in speed and efficiency.
Shaista Jabeen Abbasi, Hu Minqjie, Xiaolin Weng et al.· Scientific Reports· 0 citations
To ensure the safe operation of aircraft engine bearings under extreme conditions such as high temperatures, high pressures, and high-speed rotation, and to address their susceptibility to failure, this study explores a machine learning-based bearing fault diagnosis method. The core of the research lies in enhancing diagnostic accuracy through effective feature engineering strategies: first, multidimensional features are extracted from both the time and frequency domains of bearing vibration signals; subsequently, key features are selected using variance analysis and the Gini coefficient, with Principal Component Analysis employed for dimensionality reduction to retain core information. Performance comparisons of models including One-Dimensional convolutional neural networks, logistic regression, random forests, and gradient-boosted trees demonstrated that random forests combined with Gini coefficient feature selection achieved optimal results. This approach attained an exceptionally high accuracy of 0.9872 on the test set while exhibiting robust generalisation capabilities. This research confirms that traditional machine learning models, optimized through manual feature engineering, can provide a ‘high-precision, low-risk’ solution for bearing fault diagnosis. It offers significant reference value for the intelligent operation and maintenance of aero-engines and other industrial equipment.
Qianxi Ye, Pengfang Gao· The 2026 International Confe...· 0 citations