Cited by Multimodal framework integrating multiple large language model agents for intelligent geotechnical design

Transfer Learning-Enhanced Finite Element-Integrated Neural Networks DOI

Ning Zhang, Kunpeng Xu, Zhen‐Yu Yin

et al.

International Journal of Mechanical Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 110075 - 110075

Published: Feb. 1, 2025

Language: Английский

Citations

Physics-informed neural networks for solving steady-state temperature field in artificial ground freezing DOI

Kai-Qi Li,

Zhen‐Yu Yin, Ning Zhang

et al.

Canadian Geotechnical Journal, Journal Year: 2025, Volume and Issue: 62, P. 1 - 17

Published: Jan. 1, 2025

Artificial ground freezing (AGF) is a widely used technique for soil stabilization and waterproofing. Numerous studies have been devoted to solving the heat transfer problems in AGF while encountering limitations handling complex geometries boundary conditions being computationally intensive. Recently, using machine learning methods predict temperature fields has gained attention, demonstrating potential achieve higher accuracy than conventional models. However, these are typically limited by need large, labeled datasets, which time-consuming difficult obtain. In this study, we address challenges applying physics-informed neural networks (PINNs) solve steady-state problem AGF, focusing on distribution around single pipe. By embedding conduction equation into loss function, PINNs reduce extensive data. To enhance efficiency, employed, results compared against finite element method. Results show that high accuracy, particularly larger domains with moderate gradients, providing competitive performance more configurations involving steeper gradients. This approach offers promising alternative modeling geotechnical applications, implications reducing computational costs design.

Language: Английский

Citations

Physics-Informed Neural Network-Based Discovery of Hyperelastic Constitutive Models from Extremely Scarce Data DOI

Hyun Su Moon,

Donggeun Park,

Hanbin Cho

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

Transformer-based deformation measurement of underground structures from a single-camera video DOI

Haitao Xu,

Jianing Yin,

Ning Zhang

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 172, P. 106070 - 106070

Published: Feb. 17, 2025

Language: Английский

Citations

Multimodal framework integrating multiple large language model agents for intelligent geotechnical design DOI

Haitao Xu, Ning Zhang, Zhenyu Yin

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 176, P. 106257 - 106257

Published: May 12, 2025

Language: Английский

Citations