基于PCA&ANN方法的黄土湿陷特性分析

Analysis of Loess Collapsibility Characteristics by Using PCA&ANN Approach

黄土的湿陷性是很重要的工程特性,其机理十分复杂。随着蒙西黄土地区的工程建设日益增多,其湿陷变形特性越来越受到重视。以蒙西地区典型黄土地区—包头地区为研究区域,统计了该地区近十个工程200多个黄土室内试验样本数据,运用数理统计方法分析黄土湿陷性的影响因素,确定了以含水率、干重度、孔隙比、饱和度、塑性指数、埋深作为黄土湿陷性预测的多元因素。在此基础之上,利用主成分分析法(PCA)消除影响因素之间的重合度,使重新组合的各因子之间相互独立,运用人工神经网络(ANN)进行预测模型训练,最后用实际案例工程数据验证对比预测模型的准确性,湿陷量的实测值和预测值的相对误差分别为-12.5%、-10.4%,湿陷等级均一致,结果表明该分析方法在类似地区的工程实践中,可作为重要参考依据。

The collapsibility of the loess is an important engineering characteristic,and the mechanism of collapsibility is very complicated.With the increasing of engineering construction in the Western Inner Mongolia,more and more attention has been paid to the collapsible deformation characteristics of the loess.This paper presents a case history of the loess study in the Bao-tou area,a typical loess area in Western Mongolia.The statistical analysis on more than 20o loess samples from ten projects are performed and the influencing factors of the loess collapsibility are evaluated by using the mathematical statistics.The natural water content,dry weight,void ratio,saturation,plasticity index and compression modulus are determined as the multiple fac-tors in the loess collapsibility prediction.On this basis,the principal component analysis(PCA)is used to eliminate the overlap among the influencing factors,so that the recombined factors are independent of each other.Then,the artificial neural network(ANN)is used to train the prediction model.Finally,the accuracy of the prediction model is verified by the actual project data.The relative errors of the measured and the predicted collapsibility are-12.5%and-10.4%respectively.The collapsibility grades are therefore consistent.The results show that the method for the loess collapsibility analysis can be used as an impor-tant reference in the engineering practice.