Geotechnics for Sustainable Infrastructure Development pp 1099-1105 | Cite as
Machine Learning Scheme of the Degree of Liquefaction Assessment only from the Health Monitoring Device Installed in Individual Wooden House
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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.
KeywordsDamage evaluation Seismic motion Liquefaction
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In conducting this research, we received various instructions from Professor Motoki Kazama of the Department of Civil Engineering, Graduate School of Engineering, Tohoku University. Mr. Hiroki Fujimaru who are 4th-year undergraduates of the university and Mr. Hiroaki Kabuki who are technical staff supported the experiment. Also, GAINET was lent to Misawa Home Research Institute. We would like to express my gratitude here.
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