Differentiable finite element method with Galerkin discretization for fast and accurate inverse analysis of multidimensional heterogeneous engineering structures

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 437, P. 117755 - 117755

Published: Jan. 22, 2025

Language: Английский

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Transfer Learning-Enhanced Finite Element-Integrated Neural Networks

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

Published: Feb. 1, 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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Data-driven and physics-informed neural network for predicting tunnelling-induced ground deformation with sparse data of field measurement

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 152, P. 105951 - 105951

Published: July 5, 2024

Language: Английский

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Prediction of stratified ground consolidation via a physics‐informed neural network utilizing short‐term excess pore water pressure monitoring data

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 17, 2024

Abstract Predicting stratified ground consolidation effectively remains a challenge in geotechnical engineering, especially when it comes to quickly and dependably determining the coefficient of () for each soil layer. This difficulty primarily stems from time‐intensive nature process challenges efficiently simulating this laboratory settings using numerical methods. Nevertheless, is crucial because governs settlement, affecting safety serviceability structures situated on or such ground. In study, an innovative method utilizing physics‐informed neural network (PINN) introduced predict consolidation, relying solely short‐term excess pore water pressure (PWP) data collected by monitoring sensors. The proposed PINN framework identifies limited PWP set subsequently utilizes identified long‐term efficacy demonstrated through its application case study involving two‐layer with comparisons made existing test. results demonstrate applicability both forward inverse problems. Specifically, accurately predicts dissipation known (i.e., problem). It successfully unknown only 0.05‐year comprising 10 points at 1‐year, 10‐year, 15‐year, even up 30‐year intervals Moreover, investigation into optimal sensor layouts reveals that installing sensors areas significant variations enhances prediction accuracy method. underscore potential leveraging PINNs conjunction consolidation.

Language: Английский

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1DCNN-based prediction methods for subsequent settlement of subgrade with limited monitoring data

European Journal of Environmental and Civil engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 26

Published: Jan. 16, 2025

Deep learning has attracted considerable attention in studies on soil deformation behaviour. However, its training process requires a large amount of data, while real engineering data often suffer from issues such as insufficient scale and irregular structure. This study proposes subgrade settlement prediction method for reclamation airports coastal areas with high advance capability precision. The employs one-dimensional variant the convolutional neural network (1DCNN). To overcome challenge limited irregularly model is trained high-fidelity synthetic dataset generated ABAQUS. effectiveness dependability approach are assessed by predicting real-world projects. Furthermore, conducts an analysis internal mechanism generalisation performance 1DCNN-based models. results indicate that proposed offers higher accuracy superior long-term forecasting compared to Asaoka method. Additionally, 1DCNN outperforms other two DL methods (BiLSTM ConvLSTM) terms accuracy. As input pre-monitored processed, models learn abstract features transition into output labels. rate emerges most critical factor influencing reliability should be adjusted priority achieve optimal performance. Overall, this provides potential methodology accurate subsequent development under staged loading conditions, utilising small pre-monitoring data.

Language: Английский

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Evaluating machine learning model for investigating surface chloride concentration of concrete exposed to tidal environment

Frontiers of Structural and Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 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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High-resolution multiphysics predictions and multifields reconstruction for chemical lasers enabled by operator neural networks

Engineering Analysis with Boundary Elements, Journal Year: 2025, Volume and Issue: 177, P. 106273 - 106273

Published: April 26, 2025

Language: Английский

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Domain-Decomposed Physics-Informed Neural Network for One-Dimensional Soil Consolidation Under Multi-Step Surcharge Loading

Published: Jan. 1, 2025

Language: Английский

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