An integrated machine learning model for soil category classification based on CPT

Abstract

Abstract Soil classification is a critical issue in geological engineering, with the Cone Penetration Test (CPT) being an effective in-situ testing technique to record the variation of soil characteristics. Despite many studies that have been conducted on the relationship between CPT parameters and soil categories, analyzing soil in specific areas is essential due to the high uncertainty of geotechnical. In this study, we analyzed CPT parameters and soil categories based on geological soil layers in the Shanghai region. The CPT-based indirect method requires additional geotechnical parameters, which are limited due to the lack of advanced equipment to measure pore pressure in China. To satisfy practical application requirements, we proposed a new CPT-based direct method based on an integrated machine-learning model. By establishing multiple classification models and using Particle Swarm Optimization (PSO) to determine each model's weights, the results of multiple models were integrated to improve classification accuracy and robustness. The experimental results show that the integrated model has high accuracy and robustness in multiple engineering sites, with significant advantages over conventional CPT-based direct methods.