基于HSS-MCC融合模型及SSA-BP神经网络开展深基坑超大变形预测研究  

Prediction Study on Super Large Deformation of Deep Foundation Pit Based on HSS-MCC Fusion Model and SSA-BP Neural Network

作　　者：倪小东[1] 张宇科 焉磊 王东兴 徐硕 王媛[1] NI Xiaodong;ZHANG Yuke;YAN Lei;WANG Dongxing;XU Shuo;WANG Yuan(School of Civil and Transportation Engineering,Hohai University,Nanjing 210098,China;Construction Project Quality Supervision Station of Jiangsu Province,Nanjing 210036,China)

机构地区：[1]河海大学土木与交通学院,江苏南京210098 [2]江苏省建设工程质量监督总站,江苏南京210036

出　　处：《湖南大学学报（自然科学版）》2024年第9期35-45,共11页Journal of Hunan University:Natural Sciences

基　　金：城市基础设施智能化浙江省工程研究中心开放基金资助项目(IUI2022-ZD-02)。

摘　　要：软土环境下深基坑开挖变形特性研究中,多采用硬化类弹塑性模型进行分析,如HSS模型和MCC模型.南京河漫滩软土地区,深基坑开挖时局部常发生较大变形,部分土体变形状态介于小应变与大应变之间,单一模型无法准确预测土体变形特征.同时,BP神经网络在基坑变形预测中得到广泛应用,但在训练过程中,权阈值易陷入局部最优解,影响预测的准确性.据此,依托南京地区典型软土深基坑工程,采用Midas中的HSS模型与MCC模型进行分析,比对两种模型的桩体变形量差异,并基于最小二乘准则对两模型进行线性融合,融合模型可对后续区段监测数据进行校准及补充.通过融合麻雀搜索算法对BP神经网络进行优化,在其训练过程中快速收敛,得到全局最优的权阈值,依托狭长基坑已开挖区段监测数据学习训练,进而依据后续区段浅部开挖揭露深部变形特征,预测结果与实测值吻合度较高.研究结果对软土地区深基坑大变形的预测研究具有重要参考价值.In the study of deformation characteristics of deep foundation pit excavation in a soft soil environment,the hardening elastic-plastic model is often used for analysis,such as the HSS model and MCC model.In the soft soil area of the Nanjing River floodplain,large local deformation often occurs during deep foundation pit excavation.Some soil deformation states are between small and large strains,so a single model cannot accurately predict the deformation characteristics of soil.At the same time,the BP neural network has been widely used for predicting foundation pit deformation prediction.However,in the training process,the weight threshold easily falls into the local optimal solution,which affects the accuracy of prediction.Based on this,relying on the typical soft soil deep foundation pit project in the Nanjing area,the HSS model and MCC model in Midas are used to analyze the difference in pile deformation between the two models,and the two models are linearly fused based on the least squares criterion.The fusion model can calibrate and supplement the monitoring data of the subsequent section.The BP neural network is optimized by fusing the sparrow search algorithm,and the global optimal weight threshold is obtained by fast convergence in the training process.Based on the monitoring data of the excavated section of the narrow and long foundation pit,the training is learned.The deep deformation characteristics are revealed according to the shallow excavation of the subsequent section.The predicted results are in good agreement with the measured values.The research results have important reference value for predicting the large deformation of deep foundation pits in soft soil areas.

关 键 词：深基坑 大变形 HSS模型 MCC模型 BP神经网络 麻雀搜索算法 

分 类 号：TU470[建筑科学—结构工程]