The Analysis of the Correlation between SPT and CPT Based on CNN-GA and Liquefaction Discrimination Research

Abstract

The objective of this study is to investigate the methods for soil liquefaction discrimination. Typically, predicting soil liquefaction potential involves conducting the standard penetration test (SPT), which requires field testing and can be time-consuming and labor-intensive. In contrast, the cone penetration test (CPT) provides a more convenient method and offers detailed and continuous information about soil layers. In this study, the feature matrix based on CPT data is proposed to predict the standard penetration test blow count N. The feature matrix comprises the CPT characteristic parameters at specific depths, such as tip resistance q c , sleeve resistance f s , and depth H. To fuse the features on the matrix, the convolutional neural network (CNN) is employed for feature extraction. Additionally, Genetic Algorithm (GA) is utilized to obtain the best combination of convolutional kernels and the number of neurons. The study evaluated the robustness of the proposed model using multiple engineering field data sets. Results demonstrated that the proposed model outperformed conventional methods in predicting N values for various soil categories, including sandy silt, silty sand, and clayey silt. Finally, the proposed model was employed for liquefaction discrimination. The liquefaction discrimination based on the predicted N values was compared with the measured N values, and the results showed that the discrimination results were in 75% agreement. The study has important practical application value for foundation liquefaction engineering. Also, the novel method adopted in this research provides new ideas and methods for research in related fields, which is of great academic significance.