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

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 d...

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Bibliographic Details
Published inSoils and foundations Vol. 61; no. 3; pp. 658 - 674
Main Authors Kamura, Akiyoshi, Kurihara, Go, Mori, Tomohiro, Kazama, Motoki, Kwon, Youngcheul, Kim, Jongkwan, Han, Jin-Tae
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.06.2021
Subjects
Artificial neural network
Classification Problems
Liquefaction
Machine learning
Seismic records
Shaking table test
Shaking table test
Artificial neural network
Liquefaction
Classification Problems
Machine learning
Seismic records
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Summary: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.
ISSN:0038-0806
DOI:10.1016/j.sandf.2021.01.014