Developing a Regionally Adaptable CPT-SPT Correlation Using Linear Regression and Genetic Algorithms

Establishing a correlation between the Cone Penetration Test (CPT) and the Standard Penetration Test (SPT) is of significant importance for geotechnical engineering practice. A novel correlation between CPT-qt and SPT-N60 based on a genetic algorithm (GA) and linear regression was proposed in this study. Based on the soil behavior type index (Ic), a GA was first applied to divide the dataset into different Ic intervals. Subsequently, linear regression was performed separately for the data in each interval to establish a correlation between CPT-qt and SPT-N60. Concurrently, the Segmented Information Criterion (SIC) was introduced to perform dual-objective optimization of complexity and prediction accuracy. The results indicate that the proposed model achieved an R2 of 0.60 and an RMSE of merely 8.10. Specifically, the R2 values improved by 33% and 5% compared to the traditional models and the AI models, respectively. On the validation dataset, the proposed model achieved an R2 of 0.67 and an RMSE of 4.33, demonstrating higher accuracy compared to the traditional models. In summary, a method for investigating the CPT-SPT correlation is proposed in this study, characterized by simplicity, efficiency, and enhanced reliability. Additionally, a novel criterion (SIC) for mitigating overfitting is introduced. These two research findings can provide more reliable input parameters for SPT-based design, thereby supporting geotechnical engineering applications, and offer a valuable reference for relevant studies in other regions.