地震动
人工神经网络
计算机科学
运动(物理)
深层神经网络
模式识别(心理学)
人工智能
工程类
结构工程
作者
Duofa Ji,Jin Liu,Weiping Wen,Changhai Zhai,Wei Wang,Evangelos Katsanos
标识
DOI:10.1080/13632469.2021.1985017
摘要
This study aims to develop a reliable ground motion model (GMM) for CAV by using ground motion (GM) recordings from the PEER NGA-West2 database. A total of 17,684 GM recordings are chosen and randomly separated into the training, validation, and testing datasets. The DNN is advanced by incorporating the refined second-order (RSO) neuron. The effect of seismological and site-specific parameters on the predicted CAV is investigated. The comparative assessment of four existing models with the RSO-DNN model of this study highlights the superior prediction skill of the latter one since the RSO-DNN model is found to be associated with considerably less error.
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