Data-driven and physics-informed neural network for predicting tunnelling-induced ground deformation with sparse data of field measurement

Tunnelling and Underground Space Technology, Год журнала: 2024, Номер 152, С. 105951 - 105951

Опубликована: Июль 5, 2024

Язык: Английский

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Two-stage surrogate modeling strategy for predicting foundation pit excavation-induced strata and tunnel deformation

Tunnelling and Underground Space Technology, Год журнала: 2024, Номер 151, С. 105845 - 105845

Опубликована: Июнь 5, 2024

Язык: Английский

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Physics-informed deep operator networks with stiffness-based loss functions for structural response prediction

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 144, С. 110097 - 110097

Опубликована: Янв. 25, 2025

Язык: Английский

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Knowledge-integrated deep learning for predicting stochastic thermal regime of embankment in permafrost region

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Май 1, 2024

The warming and thawing of permafrost are the primary factors that impact stability embankments in cold regions. However, due to uncertainties thermal boundaries soil properties, stochastic modeling regimes is challenging computationally expensive. To address this, we propose a knowledge-integrated deep learning method for predicting regime Geotechnical knowledge embedded training data through numerical modeling, while neural network learns mapping from boundary property fields temperature field. effectiveness our verified comparison with monitoring analysis results. Experimental results show proposed achieves good accuracy small coefficient variation. It still provides satisfactory as variation increases. an efficient approach predict heterogeneous embankments. can also be used other engineering investigations require modeling.

Язык: Английский

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Design optimization of quasi-rectangular tunnels based on hyperstatic reaction method and ensemble learning

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 1, 2024

Язык: Английский

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Data-driven methods

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 485 - 500

Опубликована: Янв. 1, 2025

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Computational Hydro-Thermal Design of Ground Freezing in Tunneling: Optimization of Pipe Layout and Real-Time Prediction

Опубликована: Янв. 1, 2025

Язык: Английский

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A deep operator network method for high-precision and robust real-time ocean wave prediction

Physics of Fluids, Год журнала: 2025, Номер 37(3)

Опубликована: Март 1, 2025

Real-time wave prediction is crucial for optimizing offshore renewable energy capture and ensuring the safety of floating structures. However, stochastic nonlinear nature waves presents significant challenges accurate robust predictions. This study proposes a model based on Deep Operator Network (DON-WP), which learns operator to map historical heights future heights. By leveraging this operator-learning framework, demonstrates strong generalization across function space, enabling it adapt previously unseen conditions. Specifically, branch net encodes data into functional representations, while trunk captures targets as evaluation points output function. These outputs are then combined through element-wise operations generate precise The model's ability robustness validated using tank experimental multiple sea states, its performance compared with Long Short-Term Memory network-based probabilistic (Deep-WP). Results show that DON-WP, trained single state, achieves over 30% higher accuracy most horizons up 60% improvement shorter steps Deep-WP, requires retraining each state. highlights DON-WP an effective approach dynamics modeling, potential advance systems enhance

Язык: Английский

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Exploration of deep operator networks for predicting the piezoionic effect

The Journal of Chemical Physics, Год журнала: 2025, Номер 162(11)

Опубликована: Март 17, 2025

The piezoionic effect holds significant promise for revolutionizing biomedical electronics and ionic skins. However, modeling this multiphysics phenomenon remains challenging due to its high complexity computational limitations. To address problem, study pioneers the application of deep operator networks effectively model time-dependent effect. By leveraging a data-driven approach, our significantly reduces time compared traditional finite element analysis (FEA). In particular, we trained DeepONet using comprehensive dataset generated through FEA calibrated experimental data. Through rigorous testing with step responses, slow-changing forces, dynamic-changing show that captures intricate temporal dynamics in both horizontal vertical planes. This capability offers powerful tool real-time phenomena, contributing simplifying design tactile interfaces potentially complementing existing imaging technologies.

Язык: Английский

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Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset

Frontiers in Earth Science, Год журнала: 2025, Номер 13

Опубликована: Апрель 25, 2025

Accurate prediction of water inrush volumes is essential for safeguarding tunnel construction operations. This study proposes a method predicting volumes, leveraging the eXtreme Gradient Boosting (XGBoost) model optimized with Bayesian techniques. To maximize utility available data, 654 datasets missing values were imputed and augmented, forming robust dataset training validation XGBoost (BO-XGBoost) model. Furthermore, SHapley Additive explanations (SHAP) was employed to elucidate contribution each input feature predictive outcomes. The results indicate that: (1) constructed BO-XGBoost exhibited exceptionally high accuracy on test set, root mean square error (RMSE) 7.5603, absolute (MAE) 3.2940, percentage (MAPE) 4.51%, coefficient determination (R 2 ) 0.9755; (2) Compared performance support vector mechine (SVR), decision tree (DT), random forest (RF) models, demonstrates highest R smallest error; (3) importance yielded by SHAP groundwater level ( h > water-producing characteristics W burial depth H rock mass quality index RQD ). proposed volume dataset, thereby aiding managers in making informed decisions mitigate risks ensuring safe efficient advancement projects.

Язык: Английский

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