Machine Learning Scheme of the Degree of Liquefaction Assessment only from the Health Monitoring Device Installed in Individual Wooden House

Go Kurihara, Akiyoshi Kamura, Tomohiro Mori

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Health monitoring devices have been developing in order to estimate the damage to house and foundation ground subjected to an earthquake, in japan. However, the devices cannot estimate the degree of liquefaction because it focuses only on evaluation for damage index of wooden house. In this study, an attempt was made to estimate the degree of ground liquefaction only from the health monitoring device. Concretely, using “GAINET”, a health monitoring device developed by a house builder, is placed on the ground surface of the soil container, and the output data such as acceleration response, damage degree of structure and the pore water pressure in the ground were measured as machine learning data by applying several 3D seismic motions. In this research, a machine learning scheme evaluating the classification of liquefaction damage degree is introduced and the possibility to evaluate the liquefaction damage only from the output data obtained from health monitoring device is shown.

Original languageEnglish
Title of host publicationLecture Notes in Civil Engineering
PublisherSpringer
Pages1099-1105
Number of pages7
DOIs
Publication statusPublished - 2020

Publication series

NameLecture Notes in Civil Engineering
Volume62
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Keywords

  • Damage evaluation
  • Liquefaction
  • Seismic motion

ASJC Scopus subject areas

  • Civil and Structural Engineering

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