AINeutralarXiv – CS AI · 6h ago5/10
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Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects
Researchers develop physics-informed neural networks (PINNs) to model electroosmotic soil consolidation with combined loading conditions. The study compares three neural network architectures, finding that hard-constraint boundary encoding significantly improves accuracy for complex time-dependent loading scenarios, achieving prediction errors under 0.5 kPa.