Seepage Reconstruction of Large Civil Engineering Structures Based on Depth Learning

The aim of this study is to investigate a deep learning based seepage reconstruction method for large-scale civil engineering structures. Seepage reconstruction is an important issue in the field of civil engineering, which can help engineers understand the distribution of water flow in soil and guide engineering design and construction. Traditional seepage reconstruction methods rely on complex mathematical models and a large amount of measurement data, resulting in high computational complexity and difficulty in obtaining data. In this study, a seepage reconstruction method based on depth learning is proposed, which uses Convolutional neural network (CNN) and recurrent neural network (RNN) to learn the soil permeability characteristics and flow distribution law. By training a large number of seepage data samples, we can establish an efficient and accurate seepage reconstruction model. The experimental results indicate that the seepage reconstruction method based on deep learning can achieve significant advantages in computational efficiency and reconstruction accuracy. This study is of great significance for improving the seepage analysis technology and optimizing engineering design of large-scale civil engineering structures.