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

• Main Authors: Akiyoshi Kamura, Go Kurihara, Tomohiro Mori, Motoki Kazama, Youngcheul Kwon, Jongkwan Kim, Jin-Tae Han
• Format: Article
• Language: English
• Published: Elsevier 2021-06-01
• Series: Soils and Foundations
• Subjects: Liquefaction; Machine learning; Artificial neural network; Shaking table test; Seismic records; Classification Problems
• Online Access: http://www.sciencedirect.com/science/article/pii/S0038080621000470

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.