Soil consolidation is closely related to seepage, stability, and settlement of geotechnical buildings and foundations, and directly affects the use and safety of superstructures. Nowadays, the unidirectional consolidation theory of soils is widely used in certain conditions and approximate calculations. The multi-directional theory of soil consolidation is more reasonable than the unidirectional theory in practical applications, but it is much more complicated in terms of index determination and solution. To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation. In the proposed method, (1) a fully connected neural network is constructed, (2) the computational domain, partial differential equation (PDE), and constraints are defined to generate data for model training, and (3) the PDE of two-dimensional soil consolidation and the model of the neural network is connected to reduce the loss of the model. The effectiveness of the proposed method is verified by comparison with the numerical solution of PDE for two-dimensional consolidation. Using this method, the excess pore water pressure could be predicted simply and efficiently. In addition, the method was applied to predict the soil excess pore water pressure in the foundation in a real case at Tianjin port, China. The proposed deep learning approach can be used to investigate the large and complex multi-directional soil consolidation.
翻译:土壤合并的多方向理论比实际应用中的单向理论更为合理,但在指数确定和解决方案方面则更为复杂。为了解决上述问题,我们提议采用一种深层次学习方法,利用物理知情神经网络(PINN)来预测二维土壤合并的超重孔水压。在拟议方法中,(1) 建立完全相连的神经网络,(2) 计算领域,部分差异方程式(PDE)和制约为模型培训生成数据,(3) 两维土壤合并的PDE和神经网络模型与减少模型损失相关联。拟议方法的有效性通过与PDE二维整合的数字解决方案(PINN)的比较得到验证。使用这种方法,超重水压力可以在实际和高效的基础上预测。在深度土壤研究中,将超重水压用于深度研究。此外,在深度研究中,在深度研究中,将过度水压用于深度分析。在深度研究中,采用的方法可以直接预测,在深度研究中采用。