基于GRNN的砂土液化危害等级评价模型研究

影响砂土地震液化的因素复杂且具有随机性和不确定性.神经网络方法不仅能考虑定量因素,而且能考虑定性因素的影响,因而神经网络方法适用于解决非确定性的砂土地震液化评价问题.在分析广义回归神经网络的基本原理和算法基础上,建立了砂土液化危害等级评价的广义回归神经网络模型.然后用收集到的工程实例样本对该模型进行训练和检验,并与BP神经网络判别结果进行对比.结果表明,广义回归神经网络性能良好、预测精度高,是砂土地震液化危害等级评价的一种有效方法.

Study on Hazard Degree Evaluation of Sand Liquefaction Based on the Generalized Regression Neural Network

The factors which control and affect sand liquefaction,are random and uncertain.Because artificial neural network can consider both quantitative and qualitative factors,it is suitable for solving uncertain problem of the evaluation of sand soil liquefaction.A generalized regression neural network model for evaluating sand liquefaction hazard degree was established based on the basic principles and algorithms,and was trained and checked with the collected sand liquefaction examples and compared with results ...