BP神经网络在深基坑工程支护方案优选的应用

Application of Back Propagation Neural Network in the Optimized Selection of Supporting Plan of Deep Excavation

深基坑变形属于非线性大变形问题 ,与施工工序、加载和卸载路径有着密切关系。本文利用人工神经网络的非线性映射功能 ,建立深基坑支护方案与多种影响因素之间的关系 ,解决深基坑十分复杂的非线性大变形问题。通过对大量样本的学习 ,形成了优选深基坑支护方案的网络模型。经成功和失败样本的检验 ,结果证明该模型是可行的 。

The deformation of deep excavation is a matter of nonlinear large deformation,which is closely related to construction process, loading and unloading ways. In this paper, the relationships of supporting plan of deep excavation with the many influence factors have been established by use of the nonlinear mapping function of artificial neural network, so that the very complicated problem of nonlinear large deformation of deep excavation is solved. A neural network model using for the optimized selection of supporting plan of deep excavation has been built by learning from a great deal of deep excavation cases. This model, tested by successful and unsuccessful deep excavation cases, has been proven that the model is feasible, and offers reference to the support engineering of future deep excavations.