岩土力学2026，Vol.47Issue(3)：1096-1109,14.DOI:10.16285/j.rsm.2025.0257
改进的密集链接卷积神经网络算法在静力触探试验土层分类中的应用研究
Improved densely connected convolutional networks for soil layer classification from cone penetration test data
摘要
Abstract
To address the limitation of traditional machine learning methods,which primarily focus on text data and lack the capability to recognize and analyze image data,this study proposes an improved densely connected convolutional networks(DenseNet)based soil layer classification model using key parameter curve images from cone penetration test(CPT)data.First,key parameter curve images were generated from CPT data and compiled into a dataset.Second,the Optuna optimization framework and the squeeze-and-excitation(SE)attention module were integrated into the DenseNet model.Evaluation metrics including loss function,accuracy,and receiver operating characteristic curve(ROC)were adopted to assess model performance.Finally,the improved DenseNet model was applied to practical engineering projects to validate its generalization capability.The results show that the proposed model achieved a recognition accuracy of 0.920 9 on the self-built CPT image dataset from the Yellow River alluvial plain in Shandong Province,demonstrating high accuracy and strong robustness.Compared with current mainstream deep learning models and the baseline DenseNet,the improved model exhibited superior performance in soil layer identification.The model was further validated using data from 50 boreholes across five regions(Binzhou,Dezhou,Dongying,Heze,and Liaocheng),achieving a stratification accuracy exceeding 0.82 in all cases.Compared with conventional dual-bridge CPT classification charts,the improved model demonstrated clear advantages.The proposed method offers an effective solution for soil layer classification and provides valuable insights for future research in this field.关键词
DenseNet/SE模块/Optuna优化框架/CPT曲线图像/数据增强/土层分类Key words
DenseNet/SE module/Optuna optimization framework/CPT curve image/data enhancement/classification of soil layers分类
建筑与水利引用本文复制引用
李仁杰,江晓童,吕颖慧,王立波,江浩,张夏滔,张西文..改进的密集链接卷积神经网络算法在静力触探试验土层分类中的应用研究[J].岩土力学,2026,47(3):1096-1109,14.基金项目
山东省自然科学基金项目(No.ZR2023ME070). This work was supported by the National Natural Science Foundation of Shandong(ZR2023ME070). （No.ZR2023ME070）
