The adoption of deep neural network (DNN) to the prediction of soil liquefaction based on shear wave velocity

Soil liquefaction has been accepted as one of the factors causing natural disasters and engineering failures in the seismic. The mathematic prediction model for soil liquefaction is widely accepted, and the standard penetration (SPT) and cone penetration test (CPT) prediction model using the machine learning method is also developed. But for the V-s, the prediction model based on the machine learning method is limited. So, considering the advantage of the deep learning method, a multi-layer fully connected network (ML-FCN) was proposed to optimize the deep neural network (DNN) and adopted to train the predictionmodel based on the Vs and SPT dataset in this paper. The history dataset was divided into a training set, a validation set, and a testing set by a ratio of 6:2:2 for better evaluation. The SPT dataset was extracted to train a corresponding DNN prediction model. According to the comparison results, the model trained by ML-FCN DNN could predict the liquefaction potential with higher accuracy than the model proposed by Hanna et al. (Soil Dyn Earthq Eng 27(6):521-40, 2007), which is enough to be applied to real engineering, the parameter of V-s is essential to improve the model performance as for the three sets.

Automated summary

Soil liquefaction is recognized as a significant factor in causing natural disasters and engineering failures. The study introduces a multi-layer fully connected network (ML-FCN) to optimize deep neural networks (DNN) for predicting liquefaction potential based on Vs and SPT datasets, demonstrating higher accuracy compared to existing models through dataset training.