Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Cumulative Absolute Velocity Based on Refined Second-order Deep Neural Network
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.
Prediction of Cumulative Absolute Velocity Based on Refined Second-order Deep Neural Network
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.
Prediction of Cumulative Absolute Velocity Based on Refined Second-order Deep Neural Network
Ji, Duofa (Autor:in) / Liu, Jin (Autor:in) / Wen, Weiping (Autor:in) / Zhai, Changhai (Autor:in) / Wang, Wei (Autor:in) / Katsanos, Evangelos I. (Autor:in)
Journal of Earthquake Engineering ; 26 ; 8021-8040
18.11.2022
20 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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