Exploring the possibility of assessing the damage degree of liquefaction based only on seismic records by artificial neural networks

Akiyoshi Kamura, Go Kurihara, Tomohiro Mori, Motoki Kazama, Youngcheul Kwon, Jongkwan Kim, Jin Tae Han

研究成果: Article査読

抄録

This study presents a new approach to determine the damage degree of liquefaction caused by a large earthquake. We propose an artificial neural network (ANN) model based only on the seismic records of ground and define the degree of liquefaction “DDL” as a damage index. This ANN model predicts the degree of excess pore water pressure increase as the correct output label based on the seismic records obtained from the three-dimensional shaking table test. The proposed model achieved high accuracy, and the outcomes from training data indicated that the ANN model is suitable to function as a liquefaction assessment system. Further, to evaluate the applicability of the proposed ANN model in the real world, the datasets of waves from three actual seismic records were input to the ANN as validation data. The DDL judgment obtained was a good fit with the real phenomena observed.

本文言語English
ページ(範囲)658-674
ページ数17
ジャーナルSoils and Foundations
61
3
DOI
出版ステータスPublished - 2021 6

ASJC Scopus subject areas

  • 土木構造工学
  • 地盤工学および土木地質学

フィンガープリント

「Exploring the possibility of assessing the damage degree of liquefaction based only on seismic records by artificial neural networks」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル