三次样条插值对人工神经网络预测软土固结精确度的影响  

The Effect of Cubic Spline Interpolation on the Prediction of Soft Soil Settlement with Artificial Neural Network

作　　者：王彦之[1] 王天剑[2] 

机构地区：[1]中南大学地球科学与信息物理学院,长沙410083 [2]贵州财经大学仿真重点实验室,贵阳550004

出　　处：《四川理工学院学报（自然科学版）》2017年第3期67-72,共6页Journal of Sichuan University of Science & Engineering(Natural Science Edition)

基　　金：国家自然科学基金项目(F030203)

摘　　要：软土固结会引起漫长的地基沉降。人工神经网络(ANN)是预测地基沉降的一种常用工具。为了进行预测,需要使用一定的前期沉降观测数据训练ANN。采用两类训练方法:一类是直接使用观测数据训练网络,这是普通方法;一类是在观测数据中借助三次样条插值(CSI)技术进行等时距插值,然后一并利用观测数据和插值训练网络。三次样条插值是通过求解三弯矩方程组,在曲线的非连续数据点之间形成填充数据的技术。借助Mat Lab的函数Spline,可以完成插值计算过程。结果发现,在不同固结阶段进行预测,引入CSI插值训练的网络预测准确度均高于直接用观测数据训练的网络。这一发现,对于工程实践具有重要意义。The consolidation of soft soil may cause a long-term settlement. BP artificial neural network （BP-ANN） is a general technique for the prediction of settlement. Teaching the BP-ANN with a certain amount of settlement data is indispen- sible for the purpose of prediction. Two types of network teaching methods are employed ： one involving the use of pure meas- urement records, and the other involving the use of both the measurement records as well as data produced by cubic spline interpolation （CSI）. CSI is a technique for producing data between recorded data interval on a curve by means of solving tridi- agonal linear equation. The results show that the prediction by the latter is consistently better than the prediction with the former. The finding is of great practical significance in engineering.

关 键 词：人工神经网络 三次样条插值 固结沉降 精确度 预测 

分 类 号：TU433[建筑科学—岩土工程]