Physics-guided neural network-based framework for 3D modeling of slope stability

IF 5.3 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Zilong Zhang , Bowen Wang , Zhengwei Li , Xinyu Ye , Zhibin Sun , Daniel Dias
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引用次数: 0

Abstract

Physics-informed neural networks (PINN) have gradually attracted attention in the field of geotechnical engineering. This paper proposes a novel PINN-based framework for the three-dimensional (3D) stability analysis of soil slopes. Based on the fundamental theorem of plasticity and limit analysis, the partial differential equations (PDE) with regard to slope collapse are derived and integrated into the physics-guided loss function. A kinematically admissible failure mechanism that rigorously satisfies the Mohr-Coulomb associated flow rule is obtained by minimizing the loss function, thereby circumventing complex mathematical calculations. The discrete points generated by PINN are selected and refined through a series of procedures to represent the failure block of slopes using a two-dimensional matrix. The entire training process of the PINN-based framework is conducted without the need for any labeled data. The resulting discretized failure mechanism is meshfree and capable of accommodating spatially discrete data. A validation exercise is performed to verify the proposed framework by comparing it with the classical 3D rotational failure mechanism. To further consider the impact of external excitation on slope stability, a hybrid PINN framework is developed to assess the stability of slopes subjected to complex external environments. In addition to the PINN to generate a failure mechanism, a parallel PINN is employed to acquire the corresponding spatially discrete data of specified external excitations. The hybrid PINN framework for seismic stability assessment of slopes is demonstrated by way of example, indicating favorable feasibility and applicability of the developed approach. The proposed PINN-based framework provides innovative and promising avenues for 3D slope stability analysis.
基于物理引导神经网络的斜坡稳定性三维建模框架
物理信息神经网络(PINN)在岩土工程领域逐渐受到关注。本文提出了一种基于 PINN 的新型土质边坡三维(3D)稳定性分析框架。基于塑性基本定理和极限分析,推导出了有关边坡坍塌的偏微分方程 (PDE),并将其集成到物理引导的损失函数中。通过最小化损失函数,可获得严格满足莫尔-库仑相关流动规则的运动学容许破坏机制,从而避免了复杂的数学计算。PINN 生成的离散点经过一系列程序的选择和细化,用一个二维矩阵来表示斜坡的破坏块。基于 PINN 框架的整个训练过程无需任何标记数据。由此产生的离散失效机制是无网格的,能够容纳空间离散数据。通过与经典的三维旋转失效机制进行比较,验证了所提出的框架。为了进一步考虑外部激励对斜坡稳定性的影响,我们开发了一个混合 PINN 框架,用于评估受到复杂外部环境影响的斜坡的稳定性。除了生成破坏机制的 PINN 之外,还采用了并行 PINN 来获取指定外部激励的相应空间离散数据。通过实例演示了用于斜坡地震稳定性评估的混合 PINN 框架,表明了所开发方法的良好可行性和适用性。所提出的基于 PINN 的框架为三维斜坡稳定性分析提供了创新和前景广阔的途径。
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来源期刊
Computers and Geotechnics
Computers and Geotechnics 地学-地球科学综合
CiteScore
9.10
自引率
15.10%
发文量
438
审稿时长
45 days
期刊介绍: The use of computers is firmly established in geotechnical engineering and continues to grow rapidly in both engineering practice and academe. The development of advanced numerical techniques and constitutive modeling, in conjunction with rapid developments in computer hardware, enables problems to be tackled that were unthinkable even a few years ago. Computers and Geotechnics provides an up-to-date reference for engineers and researchers engaged in computer aided analysis and research in geotechnical engineering. The journal is intended for an expeditious dissemination of advanced computer applications across a broad range of geotechnical topics. Contributions on advances in numerical algorithms, computer implementation of new constitutive models and probabilistic methods are especially encouraged.
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