摘要
为了克服传统机器学习往往只关注文本数据而对图像数据的识别与分析的不足,基于静力触探试验(static cone penetration test,简称CPT)测试关键参数曲线图像数据集提出一种改进的密集链接卷积神经网络(densely connected convolutional networks,简称DenseNet)算法的土层分类模型。首先,利用CPT测试数据生成关键参数曲线图像,并整理成数据集;其次,将Optuna优化框架与压缩和激励(squeeze and excitation,简称SE)注意力模块嵌入到DenseNet模型,并引入损失函数、准确率、受试者工作特征曲线(receiver operating characteristic curve,简称ROC)等评估指标对模型性能进行评价;最后,将改进的DenseNet模型运用到对实际工程中的预测中,验证模型的泛化能力。研究结果表明:构建的改进DenseNet算法的土层分类模型在自建的山东黄河冲积平原地区CPT图像数据集上识别准确率达到0.9209,具有较高的识别准确率和较强的鲁棒性,且相较于当前主流深度学习模型和未改进前模型土层识别效果都有所提升;将改进模型应用于实际工程项目中,验证了滨州、德州、东营、菏泽、聊城5个地区共50个钻孔数据,分层准确率均在0.82以上。与双桥静力触探分类图相比,改进模型更具优势,提出的改进的Dense Net模型为土层分类问题提供了一种有效的解决方案,并为该领域的进一步研究提供了有益的参考。
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 0.9209 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.
作者
李仁杰
江晓童
吕颖慧
王立波
江浩
张夏滔
张西文
LI Ren-jie;JIANG Xiao-tong;LYU Ying-hui;WANG Li-bo;JIANG Hao;ZHANG Xia-tao;ZHANG Xi-wen(Shandong Electric Power Engineering Consulting Institute Corp.,Ltd.,Jinan,Shandong 250013,China;School of Civil Engineering and Architecture,University of Jinan,Jinan,Shandong 250022,China)
出处
《岩土力学》
北大核心
2026年第3期1096-1109,共14页
Rock and Soil Mechanics
基金
山东省自然科学基金项目(No.ZR2023ME070)。
