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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)658-674
Number of pages17
JournalSoils and Foundations
Volume61
Issue number3
DOIs
Publication statusPublished - 2021 Jun

Keywords

  • Artificial neural network
  • Classification Problems
  • Liquefaction
  • Machine learning
  • Seismic records
  • Shaking table test

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Geotechnical Engineering and Engineering Geology

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