人工神经网络在预测深基坑周边地表沉降变形中的应用研究

A Study on Application of Artificial Neural Network in Prediction of Ground Surface Settlement around Deep Foundation Pit

深基坑开挖引起的周边地表变形预测是一个复杂非线性问题,引起地表沉降的影响因素很多,各因素之间呈高度的非线性关系。传统的基坑用边地表沉降变形预测方法存在着一定的局限性,其预测精度有待提高,而人工神经网络是一种多元非线性动力学系统,可以灵活方便地对多成因的复杂未知系统进行高度建模,实现全面考虑各种主要影响因素的深基坑周边地表沉降变形预测。本文介绍了误差反向传播(BP)网络模型的结构、学习过程及其算法的改进,径向基函数(RBF)网络模型的结构及其学习过程;分析了影响深基坑开挖周边土体沉降变形的主要影响因素;以25个基坑工程的地表沉降实测资料为训练样本,建立了11个输入影响因素的BP神经网络模型和RBF神经网络模型,通过对样本的学习训练过程及对5个检验样本的预测精度,说明了人工神经网络用于预测基坑周边地表沉降的可行性和准确性。

The prediction of ground surface settlement due to excavation of a deep foundation pit is a complex and non-linear problem,in which there are many influential factors of a highly non-linear relationship.The traditional theory of settlement prediction has some limitations and need further improvement on its accuracy in practical application.The artificial neural network is a non-linear dynamic system of multiple variables,and can conveniently and flexibly simulate any unknown system of complex polygene so that the ground surface settlement around a deep foundation pit can be predicted with all main influential factors being taken into account properly.This article introduces the module,learning process,improved algorithm of the back propagation（BP）network,as well as the module with its learning process of the radial basis function（RBF）network.We analyze the major factors influencing the ground surface settlement due to excavation of the deep foundation pit.Twenty-five samples of actual measurements of ground surface settlement around deep foundation pits have been taken for the training of the neural networks to build 11 models of BP neural network and RBP neural network with the imported factors.The training process and the predicting precision of five validating samples show the feasibility and accuracy of the artificial neural network for the prediction of ground surface settlement around deep foundation pits.