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

研究成果: Chapter

抄録

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.

本文言語English
ホスト出版物のタイトルLecture Notes in Civil Engineering
出版社Springer
ページ1099-1105
ページ数7
DOI
出版ステータスPublished - 2020

出版物シリーズ

名前Lecture Notes in Civil Engineering
62
ISSN(印刷版)2366-2557
ISSN(電子版)2366-2565

ASJC Scopus subject areas

  • Civil and Structural Engineering

フィンガープリント 「Machine Learning Scheme of the Degree of Liquefaction Assessment only from the Health Monitoring Device Installed in Individual Wooden House」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