基于机器学习的深基坑三维土层重建

在深基坑施工过程中,需要通过少量钻孔数据来进行土层三维模型重建,以获取土质信息分布.提出一种基于机器学习的土层重建方法,首先设计土层生成算法来进行土层训练数据集的数据增强.然后根据钻孔信息数据结构设计了预测模型特征编码方法,作为预测模型的标准输入,通过搭建卷积神经网络模型,对土层结构进行特征提取,形成土层预测模型.随后,利用预测模型对待预测地块中的离散格点进行土层属性预测,获得土层体数据.最后,对土层体数据利用Marching Cubes算法生成封闭等值面,形成土层实体块,从而实现了对三维土层的重建.本模型能够适应不同层数、不同类型的地层,具备了实际工程应用的初步条件.

3D soil layer reconstruction of deep foundation pit based on machine learning

In the process of deep foundation pit construction, a small amount of borehole data is needed to reconstruct the 3D model of the soil layer to obtain the distribution of soil information. This paper proposes a method of soil layer reconstruction based on machine learning. First, a soil layer generation algorithm is designed to enhance the data of the soil layer training data set. Then, the prediction model feature coding method is designed according to the data structure of the borehole information. As the standard input of the prediction model, the convolutional neural network model is built to extract the features of the soil layer structure to form the soil layer prediction model. Subsequently, the prediction model is used to predict the soil layer attributes of the discrete grid points in the predicted block to obtain soil layer data. Finally, the Marching Cubes algorithm is used to generate closed isosurfaces for the soil layer data to form solid blocks of the soil layer, thereby realizing the reconstruction of the three-dimensional soil layer. This model can adapt to different layers and different types of strata, and has the preliminary conditions for practical engineering applications.