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Open access
Aug 2026
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 geo...
F. Benayoun, M. Feligha, S. Bekkouche et al.
· Naukovyi Visnyk Natsionalnoh... · 0 citations
Open access
Aug 2026
Machine Learning Surrogate Modeling for Accurate Prediction of GAA ‐ MOSFET Electrical Characteristics
Machine learning regression models were employed to predict the electrical characteristics of GAA‐MOSFETs using a dataset of 459 simulation points. The models considered input features including gate voltage, drain voltage, channel length, silicon thickness, and metal work functions, with the logarithm of the drain...
M. Belkhiria, F. Echouchene, M. A. Albedah et al.
· International journal of num... · 0 citations
Nov 2026
Hybrid PINN–GPR Method for Probabilistic Prediction of Concrete Creep Behavior
A novel hybrid method integrating physics-informed neural network (PINN) and Gaussian process regression (GPR) that enforces the B4 creep model as a physics-informed constraint by embedding its governing equations into the loss function, effectively incorporating physical knowledge into data-driven training.
Zhiren Tao, Jian-Xin Peng, Shijie Liao et al.
· Journal of materials in civi... · 0 citations
Open access
Sep 2026
Predictive Modelling of Steel Corrosion: A Comparative Study of Statistical and Machine Learning Methods with a Focus on Copper Behavior
Accurately predicting corrosion rates in low-alloy steels is a significant challengein materials engineering due to the intricate and nonlinear interaction between environmental exposure conditions and alloying elements. Conventional statistical corrosion models are by and large based on linear assumptions, and thereby...
Mohanad S. Hasan, A. Bader, Saad Shauket Sammen
· Applied Chemistry for Engine... · 0 citations
Open access
Aug 2026
A Robust Surrogate-Assisted Framework for Simultaneous Multi-Parameter Optimization in Microstrip Antenna Design
Conventional optimization still poses a significant challenge when it comes to producing the designs required for modern communication technologies. Previous studies have mainly focused on effectiveness and computational efficiency, but have not sufficiently addressed robustness, an essential factor in surrogate-assist...
Agusriandi, A. Affandi, Eko Setijadi
· Engineering, Technology &... · 0 citations
Open access
Aug 2026
Reliability-Based Slope Stability Analysis Using Particle Swarm-Optimized Neural Network: Benchmarking Against Conventional Probabilistic Methods Using a Lebanese Case Study
Probabilistic slope stability analysis requires tools that are both computationally efficient and accurate for uncertainty propagation. This study develops a reliability-oriented surrogate framework coupling a multilayer perceptron artificial neural network with particle swarm optimization (ANN–MLP–PSO). The model was...
Shaza Soleiman, M. Rahhal
· Infrastructures · 0 citations