Maintenance mechanisms of rejuvenator-optimized asphalt emulsion in damaged porous asphalt mixture: Morphological, physicochemical, and rheological characterizations

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 464, P. 140185 - 140185

Published: Jan. 30, 2025

Language: Английский

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Physics and data hybrid-driven interpretable deep learning for moving force identification

Engineering Structures, Journal Year: 2025, Volume and Issue: 329, P. 119801 - 119801

Published: Feb. 3, 2025

Language: Английский

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A Comprehensive Investigation of Physics-Informed Learning in Forward and Inverse Analysis of Elastic and Elastoplastic Footing

Computers and Geotechnics, Journal Year: 2025, Volume and Issue: 181, P. 107110 - 107110

Published: Feb. 5, 2025

Language: Английский

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

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

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A novel approach for identifying sweet spots in tight reservoir fracturing engineering based on physical-data dual drive

Journal of Applied Geophysics, Journal Year: 2025, Volume and Issue: unknown, P. 105735 - 105735

Published: April 1, 2025

Language: Английский

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