Cracks have been proven to significantly impact soil slope stability, whereas their mechanisms influencing rock slope stability have not been effectively investigated. Within the upper-bound theorem framework, the non-linear Hoek-Brown yield envelope is divided into multiple segments using a series of tangent lines to establish a three-dimensional (3D) multi-segment failure mechanism and a discontinuity surface is introduced into the first segment to account for the presence of a pre-existing crack at the slope crest. A novel physics-informed neural network (PINN) is then developed to calculate the seismic acceleration induced by seismic waves. The PINN-based framework has the advantage of describing the spatiotemporal characteristics of seismic waves while adhering to the geometrical constraints of slopes. To acquire the space-dependent seismic force-generated work rates, a slice integration strategy is introduced, followed by the derivation of the stability number with seismic action. Determining the critical stability number of cracked rock slopes involves identifying a critical failure mechanism characterized by cracks that most adversely affect slope stability in terms of depth and location. This process is a multivariable optimization scheme supported by the innovative Marine Predators Algorithm (MPA). Results indicate that seismic excitation and the existence of cracks considerably narrow the stress distribution range on the Hoek-Brown strength envelope, leading to a reduction in rock slope stability. The PINN model can provide a more realistic distribution characteristic of seismic loadings within slopes, taking into account the slope geometry, rock properties, and seismic wave characteristics. Considering the powerful transformation capabilities of Fourier analysis, the proposed PINN-based seismic analysis framework demonstrates the potential for incorporating more realistic seismic data into an analytical framework of slope stability analysis.

中文翻译：

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基于物理信息神经网络的裂缝岩质边坡地震稳定性

裂缝已被证明对土质边坡稳定性有显著影响，而其影响岩石边坡稳定性的机制尚未得到有效研究。在上界定理框架内，使用一系列切线将非线性 Hoek-Brown 屈服包络线划分为多个段，以建立三维 （3D） 多段破坏机制，并将不连续面引入第一段，以解释坡顶处预先存在的裂缝的存在。然后开发了一种新的物理信息神经网络 （PINN） 来计算地震波引起的地震加速度。基于 PINN 的框架具有描述地震波时空特征的优势，同时遵守斜坡的几何约束。为了获得与空间相关的地震力产生的工作速率，引入了一种切片积分策略，然后通过地震作用推导稳定性数。确定开裂岩石边坡的临界稳定性数涉及确定一种临界破坏机制，其特征是裂缝在深度和位置方面对边坡稳定性最不利。此过程是由创新的 Marine Predators 算法 （MPA） 支持的多变量优化方案。结果表明，地震激励和裂纹的存在大大缩小了 Hoek-Brown 强度包络线上的应力分布范围，导致岩石边坡稳定性降低。考虑到边坡几何形状、岩石特性和地震波特性，PINN 模型可以提供边坡内地震载荷的更真实的分布特征。 考虑到傅里叶分析的强大转换能力，所提出的基于 PINN 的地震分析框架展示了将更真实的地震数据纳入边坡稳定性分析框架的潜力。