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作 者:徐翠翠 杨淑娟[1] 许卫晓[1] XU Cuicui;YANG Shujuan;XU Weixiao(School of Civil Engineering,Qingdao University of Technology,Qingdao 266520,China)
机构地区:[1]青岛理工大学土木工程学院,山东青岛266520
出 处:《防灾减灾工程学报》2023年第5期979-986,共8页Journal of Disaster Prevention and Mitigation Engineering
基 金:2021年度山东省重点研发计划(重大科技创新工程)第一批(2021CXGC011204);山东省自然科学基金面上项目(ZR2020ME246)资助。
摘 要:仪器地震烈度是指由仪器的观测记录计算而来的地震动强度,能直观反映地震的影响程度。建立高效准确的仪器地震烈度评估模型,在防震减灾和烈度速报中有着重要意义。以汶川地震的地震动记录以及与其对应的宏观调查烈度作为数据源,通过灰色关联分析确定了17种地震动参数与宏观调查烈度的关联程度。以与宏观烈度关联系数较高的谱烈度、累计绝对速度、均方根加速度等参数为模型的输入层,宏观烈度为输出层,网络迭代次数为1000,损失函数选择均方误差,指标函数选择平均绝对误差,优化算法选择适应性矩估计,建立了基于多层感知机的仪器地震烈度预测模型。最后利用芦山地震为例验证该模型的准确性,并与其他方法进行比较。结果表明,该方法误差率较低,模型预测结果良好。Instrumental seismic intensity refers to the intensity of ground motion calculated from the observation records of instruments,which can directly reflect the impact of an earthquake.Establishing an efficient and accurate instrumental intensity evaluation model is of great significance in earthquake prevention and mitigation,and rapid intensity reporting.In this paper,the ground motion records of Wenchuan earthquake and its corresponding macro investigation intensity are used as data sources,the correlation degree of 17 seismic parameters and investigation intensity is determined through grey correlation analysis.Spectral intensity,cumulative absolute velocity,root mean square acceleration and other parameters with high correlation coefficient with macroseismic intensity are selected as the input of the model,the macroscopic investigation intensity is taken as the output,with the number of model iterations is 1000.Mean square error is selected for the loss function of the model,mean absolute error is selected for the indicator function,and adaptive moment estimation is selected for the optimization algorithm,and the instrumental seismic intensity prediction model based on multilayer perceptron is established.Finally,the Lushan earthquake is used as an example to verify the accuracy of the model and compare it with other methods.The result shows that the method has a low error rate and the model predicts well.
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