基于多模型Stacking融合的基坑测斜时序预测  

Time series prediction of horizontal displacement in foundation pits based on Stacking multi-model

作　　者：胡比澜 王洋洋 张永强[1] HU Bilan;WANG Yangyang;ZHANG Yongqiang(College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,China;SPIC Yunnan International Power Investment Limited Company,Kunming 650228,China)

机构地区：[1]浙江大学建筑工程学院,浙江杭州310058 [2]国家电投集团云南国际电力投资有限公司,云南昆明650228

出　　处：《浙江大学学报(工学版)》2025年第4期706-716,共11页Journal of Zhejiang University:Engineering Science

摘　　要：为了准确预测基坑倾斜变形,提出基于极致梯度提升(XGBoost)、长短期记忆(LSTM)和线性回归(LR)的堆叠多变量预测模型.利用XGBoost集成学习的优越性和双层LSTM算法预测传统基坑变形的准确度,提升模型的预测精度和泛化能力.在数据预处理阶段,引入K最近邻(KNN)插补算法增加可有效利用的数据总量,使用深度学习模型Informer的时间信息处理方式,改善传统算法中有监督学习忽略时间序列数据不同时间间隔的问题.以杭州某在建基坑为工程案例,插补616条缺失数据,将时间信息转为3列时间点特征信息,使用所提模型进行基坑变形预测分析.已有实测数据验证表明,所提模型在预测基坑最大测斜位移及该位移点处深度时的训练精度和泛化能力相比双层LSTM模型及XGBoost模型均有较大提升,使用时间点特征的XGBoost模型比LSTM模型更适合预测对时间因素敏感的指标.In order to accurately predict the lateral deformation of a foundation pit,a multivariable stacking prediction model based on extreme gradient boosting(XGBoost),long short-term memory(LSTM)and linear regression(LR)was proposed.By using the XGBoost’s advantage of ensemble learning and the accuracy of the two-layer LSTM algorithm in the traditional foundation pit deformation prediction,the prediction accuracy and the generalization ability of the model were improved.In the data pre-processing stage,the K-nearest neighbors(KNN)interpolation algorithm was introduced to increase the total amount of data that can be effectively utilized,and the processing method of time information in the deep learning model Informer was used to deal with the problem of ignoring the different time intervals of time series data by supervised learning in the traditional algorithm.Taking a foundation pit under construction in Hangzhou as a practical engineering case,616 missing data were interpolated,the time information was converted into three columns of time point feature information,and the proposed model was used for foundation pit deformation analysis.Existing measured data verified that the training accuracy and the generalization ability of the proposed model were greatly improved compared with both the two-layer LSTM model and the XGBoost model when predicting the maximum slope displacement of the foundation pit and the depth of the displacement point.The XGBoost model,which used time-point features,was more suitable for predicting time-sensitive indicators than the LSTM model.

关 键 词：时间序列分析 基坑测斜 双层LSTM 极致梯度提升(XGBoost) 堆叠算法 

分 类 号：TU433[建筑科学—岩土工程]