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.

本研究提出了一种确定大地震液化破坏程度的新方法。我们提出了一种仅基于地面地震记录的人工神经网络 (ANN) 模型，并将液化程度“DDL”定义为损坏指数。该人工神经网络模型根据从三维振动台试验获得的地震记录，预测超孔隙水压力增加的程度作为正确的输出标签。所提出的模型实现了高精度，训练数据的结果表明 ANN 模型适合用作液化评估系统。此外，为了评估所提出的 ANN 模型在现实世界中的适用性，来自三个实际地震记录的波数据集作为验证数据输入到 ANN 中。