Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects
arXiv:2606.00056v1 Announce Type: cross Abstract: This study develops a dimensionless multi-domain physics-informed neural network (PINN) framework for electro-osmotic radial consolidation considering smear effects and combined vacuum and surcharge loading. Three PINN-based models are investigated: a standard soft-constrained PINN (Std-PINN), a modified gated PINN (Mod-PINN), and a modified gated PINN with hard-constraint boundary encoding (Mod-HC-PINN). The models are evaluated against FEM reference solutions under four loading cases, including constant vacuum, exponential vacuum, exponential
The proliferation of complex engineering challenges requiring highly accurate and computationally efficient solutions is driving the development of advanced AI techniques like PINNs.
This research demonstrates a significant advancement in applying AI to complex civil engineering problems, indicating broader potential for physics-informed AI in industrial processes and infrastructure.
The ability to model and predict the behavior of soft soils under various consolidation methods with higher accuracy and efficiency using AI-driven methods improves infrastructure development and environmental management.
- · Civil engineering firms
- · Geotechnical contractors
- · AI/ML research community
- · Infrastructure development sector
- · Traditional numerical simulation software vendors (if slower to adapt)
Improved design and risk assessment for large-scale construction projects involving soft soils.
Reduced project costs and environmental impact due to more efficient and precise ground improvement techniques.
Accelerated adoption of physics-informed AI across other engineering disciplines, leading to a new era of AI-augmented design and analysis.
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Read at arXiv cs.LG