地质科技通报2025，Vol.44Issue(2)：130-145,16.DOI:10.19509/j.cnki.dzkq.tb20240439
基于优化的多输出神经网络预测软弱土压缩和回弹指数研究
Prediction of the compression index and swell index of soft soils via an optimized multiple-output neural network
摘要
Abstract
[Objective]The compression index Cc and swell index Cs of soil are critical parameters for calculating soil settlement and swelling.Utilizing machine learning algorithms to predict these indices quickly and efficiently can significantly reduce testing duration and costs.[Methods]In this study,we introduce Piezocone Penetration Test(CPTU)in-situ data and quantify soil layer information using the Soil Behaviour Type(SBT)index Ic.We then combine laboratory data with CPTU data to develop a multi-output genetic algorithm-optimized backpropagation neural network(GA-BPNN)model.The input parameters for the multi-output GA-BPNN model were determined through correlation analysis.Using the TC304 standard site database,the prediction results from the multi-output GA-BPNN model were compared with those from the multi-output BPNN model and the single-output GA-BPNN model,verifying the effectiveness of the multi-output GA-BPNN model and obtaining pre-trained model parameters.For sites with limited data in Nanjing,the superiority of the multi-output BPNN model was further evaluated by analyzing the impact of pre-training and in-situ test data on model performance.A sensitivity analysis was also conducted to assess the robustness of the model.[Results]The results demonstrate that the pre-trained multi-output GA-BPNN model,derived from standard site data,can effectively predict the compression and swell indices under limited data conditions.When combined with in-situ test data,the multi-output GA-BPNN model exhibits high prediction accuracy for these indices,with predicted values closely matching measured data.The consistency of the predicted results aligns well with existing studies.[Conclusion]The pre-trained multi-output GA-BPNN model can efficiently predict the compression and swell indices of soft soil under limited data conditions.The proposed method shows significant potential for multi-parameter prediction in engineering practice,enhancing the efficiency and reliability of geotechnical engineering assessments.关键词
压缩指数/回弹指数/多输出/优化神经网络/GA-BPNN模型/软弱土Key words
compression index/swell index/multiple-output/optimized neural network/GA-BPNN model/soft soil分类
地质学引用本文复制引用
陈凯,林军,聂利青,段伟..基于优化的多输出神经网络预测软弱土压缩和回弹指数研究[J].地质科技通报,2025,44(2):130-145,16.基金项目
国家自然科学基金项目(52308355,51908250) （52308355,51908250）
2023江苏高校"青蓝工程"项目 （）
安徽省智能地下探测技术研究院开放课题(2022B1) （2022B1）
