ABSTRACT

The present work aims at exploring the application of nonlinear principal component analysis in dimensionality reduction and prediction of response spectra. The evaluation is performed based on log10 scaled response spectra at 91 spectral periods corresponding to 13552 records available in the NGA-West2 database. The non-linear principal component analysis performed on the data showed that 91 spectral periods can be addressed with just 3 principal components. Further, an artificial neural network (ANN) model is developed to predict these three principal components with magnitude, distance, shear wave velocity and focal mechanism as input. The inter- and intra-event residuals obtained for the response spectra predicted using the developed model are comparable with the existing ground motion prediction equations (GMPEs) from the same database. The developed model is also observed to capture all the prominent attenuation features of ground motions. Hence, the study indicates that the response spectra can be described with just three uncorrelated variables.

摘要

本工作旨在探索非线性主成分分析在降维和响应谱预测中的应用。评估是基于对应于 NGA-West2 数据库中可用的 13552 条记录的 91 个光谱周期的 log10 标度响应光谱进行的。对数据进行的非线性主成分分析表明，只需 3 个主成分即可解决 91 个光谱周期。此外，还开发了一个人工神经网络 (ANN) 模型来预测这三个主要分量，其中幅度、距离、剪切波速度和焦点机制作为输入。使用开发的模型预测的响应谱获得的事件间和事件内残差与来自同一数据库的现有地面运动预测方程 (GMPE) 具有可比性。还观察到开发的模型捕获了地震动的所有显着衰减特征。因此，该研究表明，响应谱可以仅用三个不相关的变量来描述。