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Physics-informed deep learning method for predicting tunnelling-induced ground deformations
Tunnelling-induced ground deformations inevitably affect the safety of adjacent infrastructures. Accurate prediction of tunnelling-induced deformations is of great importance to engineering construction, which has historically been dependent on numerical simulations or field measurements. Recently, some surrogate models originating from machine learning methods have been developed, showing satisfactory prediction performance with high computational efficiency. However, these purely data-driven models show weak robustness in the absence of sufficient training data. This study proposed a hybrid deep learning model integrating both data-driven and physics-based strategies to decrease calculation costs and eliminate the dependence on large numbers of training data. The underlying physical mechanism of ground deformation due to tunnel excavation is coupled into the deep learning framework to form a physics-informed neural network (PINN) model. The performance of the hybrid model is first assessed by comparing it with the classical Verruijt-Booker solution and a conventional purely data-driven model. The potential of the proposed PINN model for engineering applications is then illustrated. It is found that the proposed PINN model can reasonably reproduce ground deformation fields obtained numerically with only a small amount of training data. This paper provides a new paradigm for incorporating hybrid deep learning frameworks and field monitoring systems to predict ground deformation fields in real time.
Physics-informed deep learning method for predicting tunnelling-induced ground deformations
Tunnelling-induced ground deformations inevitably affect the safety of adjacent infrastructures. Accurate prediction of tunnelling-induced deformations is of great importance to engineering construction, which has historically been dependent on numerical simulations or field measurements. Recently, some surrogate models originating from machine learning methods have been developed, showing satisfactory prediction performance with high computational efficiency. However, these purely data-driven models show weak robustness in the absence of sufficient training data. This study proposed a hybrid deep learning model integrating both data-driven and physics-based strategies to decrease calculation costs and eliminate the dependence on large numbers of training data. The underlying physical mechanism of ground deformation due to tunnel excavation is coupled into the deep learning framework to form a physics-informed neural network (PINN) model. The performance of the hybrid model is first assessed by comparing it with the classical Verruijt-Booker solution and a conventional purely data-driven model. The potential of the proposed PINN model for engineering applications is then illustrated. It is found that the proposed PINN model can reasonably reproduce ground deformation fields obtained numerically with only a small amount of training data. This paper provides a new paradigm for incorporating hybrid deep learning frameworks and field monitoring systems to predict ground deformation fields in real time.
Physics-informed deep learning method for predicting tunnelling-induced ground deformations
Acta Geotech.
Zhang, Zilong (author) / Pan, Qiujing (author) / Yang, Zihan (author) / Yang, Xiaoli (author)
Acta Geotechnica ; 18 ; 4957-4972
2023-09-01
16 pages
Article (Journal)
Electronic Resource
English
Data-driven , Physics-based , PINN , Shield tunnelling , Tunnelling-induced deformations Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
Tunnelling Induced Ground Deformations and their Effects on Adjacent Piles
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Centrifuge model testing of tunnelling-induced ground and pile deformations
| British Library Online Contents | 2000
Centrifuge model testing of tunnelling-induced ground and pile deformations
| Online Contents | 2000