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Physics-informed Deep Learning Method for Predicting Tunnelling-induced Ground Deformations by Zilong Zhang; Qiujing Pan; Zihan Yang; Xiaoli Yang is a Engineering article available to read on EtoBox.

What is Physics-informed Deep Learning Method for Predicting Tunnelling-induced Ground Deformations about?

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 illustrate

Who reads Physics-informed Deep Learning Method for Predicting Tunnelling-induced Ground Deformations?

Author

Zilong Zhang; Qiujing Pan; Zihan Yang; Xiaoli Yang

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Engineering (Physical Sciences)