ABSTRACT

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.

摘要

本研究旨在通过使用来自 PEER NGA-West2 数据库的地震动 (GM) 记录为 CAV 开发可靠的地震动模型 (GMM)。总共选择了 17,684 条 GM 记录，并随机分成训练、验证和测试数据集。DNN 通过合并精炼的二阶 (RSO) 神经元得到改进。研究了地震学和特定地点参数对预测的 CAV 的影响。四个现有模型与本研究的 RSO-DNN 模型的比较评估突出了后者的卓越预测技巧，因为发现 RSO-DNN 模型与相当少的错误相关。