隧洞围岩力学响应的物理信息神经网络模型及其工程应用

doi:10.3724/1000-6915.jrme.2025.0687

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Abstract Accurately and efficiently characterizing the mechanical response of surrounding rock is fundamental to ensuring safe tunnel excavation. To address the challenges associated with conventional analytical and numerical methods—such as complex model construction, limited generalizability, and difficulties in balancing computational efficiency with accuracy—this paper proposes a dual data- and physics-driven approach for analyzing the mechanical response of tunnel surrounding rock. First, based on the secondary stress superposition principle and the Verruijt-Booker governing equations, we construct a neural network approximation that outputs displacement and stress fields. This network architecture inherently embeds the governing equations, boundary conditions, and initial conditions, resulting in a mesh-free Physics-Informed Neural Network (PINN). During training, we optimize millions of network parameters to identify a high-dimensional function that simultaneously satisfies physical laws and data constraints, ultimately forming an intelligent surrogate solver for the system of partial differential equations. Second, to tackle issues such as highly variable geological conditions, diverse design scenarios, and sparse monitoring data, we introduce a Transfer Learning-Driven Physics-Informed Neural Network (TL-PINNs). By employing a pre-training and parameter transfer strategy, the model achieves rapid convergence within a high-dimensional parameter space involving multiple variables, including tunnel depth, diameter, and rock mass properties. Results demonstrate that the PINN model closely reproduces outcomes from classical Finite Element Method (FEM) simulations. Under constraints from 45 monitoring points and 6,000 Latin Hypercube Sampling (LHS) collocation points, the maximum absolute errors in horizontal and vertical displacements are 0.31 mm and 0.23 mm, respectively. Compared to the baseline PINN without transfer learning, the TL-PINNs model reaches the same loss level in only 5 000 iterations—a fraction of the 50 000 required by the conventional approach—while reducing training time from 3 664 seconds to 420 seconds, achieving up to a 66.1% reduction in absolute displacement error. As a mesh-free intelligent algorithm, the TL-PINNs framework offers high computational speed, strong interactivity, and excellent generalization capability. The proposed dual data- and physics-driven framework meets practical engineering requirements in terms of accuracy, efficiency, and robustness, providing theoretical guidance and a reference basis for future intelligent simulations in tunnel and underground engineering applications.

Key words： tunnelling engineering physical information neural networks numerical-physical coupling rockmass mechanical response transfer learning

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	HE Peng1
	JIANG Zihao1
	SHI Shaoshuai2
	3*
	WANG Fei1
	HU Jie4
	WU Weitao5
	ZHANG Panpan1

Cite this article:

HE Peng1,JIANG Zihao1,SHI Shaoshuai2, et al. Physical information neural network model for mechanical response of tunnel surrounding rock and its engineering applications[J]. , 2026, 45(8): 2391-2412.

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https://rockmech.whrsm.ac.cn/EN/10.3724/1000-6915.jrme.2025.0687 OR https://rockmech.whrsm.ac.cn/EN/Y2026/V45/I8/2391

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