【摘要】 系统介绍C-SVC模型,将之运用于土石坝裂缝震害评价。采用层次分析法得到影响土石坝裂缝震害的六个训练属性:坝型、坝高、库容、施工质量、地震烈度、震中距,及训练标签:坝体裂缝,对训练属性和训练标签进行从定性到定量的赋值,建立了C-SVC的土石坝裂缝震害评价系统。选取汶川地震中100座水库土石坝的震害数据作为训练集,根据不同归一化方式下的预测分类准确率选择归一化预处理方式,采用K-fold交叉验证选取最佳核函数,结果表明:[-1,1]归一化预处理和多项式核函数适用于土石坝裂缝震害的预测。以绵阳市28座水库土石坝震害为实例,对水库土石坝进行裂缝震害评价,其中25座的震害评价结果与实际一致,这表明所建立的土石坝裂缝震害评价系统可以为除险加固的先后顺序提供参考意见。更多还原

【Abstract】 A model of support vector machine for classification(C-SVC) and its application to the evaluation on crack damages of earth and rock-fill dams are systemically described herein.An analytic hierarchy process was used to obtain six training factors of the damage,i.e.dam type,dam height,storage capacity,construction quality,seismic intensity,and epicentral distance,and the training tab,i.e.dam crack.A C-SVC evaluation system was developed by determining the factors and tab in a certain way from qualitative to the quantitative.This system uses the field data of 100 earth and rock-fill dams damaged by the Wenchuan earthquake as the training set,and adopts optimal normalization preprocessing obtained through a comparison of different classification accuracies in predictions by different normalizations and the best kernel function selected by a K-fold cross-validation technique.The results show that the normalized preprocessing of[-1,1]and the polynomial kernel function are applicable to prediction of dam crack.AppUcation of the system to 28 reservoirs in Mianyang city shows that the predictions for 25 of them are in good agreement with in-situ observations.This indicates that the C-SVC evaluation model can provide useful information for reinforcement of earth and rock-fill dams.更多还原