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

Author:

Kamura AkiyoshiORCID,Kurihara Go,Mori Tomohiro,Kazama MotokiORCID,Kwon Youngcheul,Kim Jongkwan,Han Jin-TaeORCID

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering

Reference42 articles.

1. Assessment and prediction of liquefaction potential using different artificial neural network models: A case study;Abbaszadeh-Shahri;Geotech. Geol. Eng.,2016

2. Detection and interpolation of abnormal observation of seismic sensors;Akasaka,1995

3. Generation and propagation of G waves from the Niigata earthquake of June 14, 1964. Part 1. A statistical analysis (PDF);Aki;Bullet. Earthquake Res. Inst.,1966

4. Association for Earthquake Disaster Prevention (AEDP-jp), 1998. Strong Motion Array Observation, No. 3, CD-ROM.

5. Belur, V.D., 1991. Nearest neighbor (NN) norms: NN pattern classification techniques, ISBN 0-8186-8930-7

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