【摘要】 基于支持向量机(SVM)原理,详细介绍了其用于桥梁群体震害预测的步骤和方法 ;通过选取唐山、海城、汶川等地震中的103座桥梁的震害资料作为学习样本,建立了桥梁的震害评估SVM模型;通过比较SVM模型与BP神经网络的震害预测能力,发现SVM模型具有更高的准确率。研究表明SVM模型具有良好的推广能力,可以应用于实际的桥梁震害预测工作。更多还原

【Abstract】 In order to evaluate the seismic fragility of bridges more scientifically and effectively,the paper introduces in detail the procedure and method of the seismic damage prediction of bridges group, based on support vector machine. The paper built a SVM model, by selecting learning samples from earthquake disaster data of 103 bridges in Tangshan earthquake, Haicheng earthaquake and Wenchuan earthquake etc. By comparing the earthquake prediction ability of the SVM model and the BP neural network, we can find that the accuracy of SVM model is higher.The study shows that the SVM model has good generalization capability and can be operated in practice.更多还原