Uncertainty-aware deep reinforcement learning optimised construction fleet management: A digital twin approach

Reinforcement Loads Prediction of Geosynthetic-Reinforced Soil Structures Using Explainable and Nonexplainable Machine Learning Approaches

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.

Optimal sizing and deep reinforcement learning-based control of a battery energy storage system under renewable and load uncertainty in an isolated microgrid

A pose estimation method for aeroengine blade assembly based on geometric priors and reinforcement learning

Physics-informed reinforcement learning for energy-efficient minute-scale control of split-type air-conditioning systems: Experimental validation on real hardware

Deep reinforcement learning for dynamic multi-trip multi-AGV scheduling in product retrieval systems

Tri-driven graph reinforcement learning for dynamic root cause tracing in UF-RO water treatment processes