河南城建学院学报2026，Vol.35Issue(2)：40-48,81,10.DOI:10.14140/j.cnki.hncjxb.2026.02.006
特征增强驱动下基坑临近构筑物沉降预测
Settlement prediction of foundation pit adjacent structures driven by feature enhancement
摘要
Abstract
To address the issue of pseudo-variations in automated monitoring data during deep foundation pit excavation,this study proposes a data feature enhancement method integrating exponential smoothing and variational mode decomposition(VMD)to improve the deformation warning accuracy of machine learning models.This study focuses on settlement monitoring data from a deep foundation pit in Pujiang County,Chengdu,proposing a data enhancement method that integrates exponential smoothing and Variational Mode Decomposition(VMD).The impact on the predictive performance of four models was systematically evaluat-ed:Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Transformer and Convolutional Neu-ral Network(CNN).Results indicate that VMD effectively filters high-frequency noise,elevating the sig-nal-to-noise ratio from 22.1 dB to approximately 41.0 dB.Following data enhancement,all models exhibit substantially improved performance.Specifically,the CNN model achieves sub-millimeter accuracy,with its Mean Squared Error(MSE)reduced to 0.000 1 and Mean Absolute Error(MAE)reaching 0.001 mm.The Transformer model demonstrates the highest sensitivity to enhanced data,showing a 99.92%improvement in MSE.Exponential smoothing effectively boosts model stability,while VMD further suppresses prediction outliers,reducing the Mean Absolute Percentage Error(MAPE)to below 5%.关键词
数据增强/注意力机制/深基坑/沉降变形/深度学习Key words
data augmentation/attention mechanism/deep foundation pit/settlement deformation/deep learning分类
建筑与水利引用本文复制引用
杜小虎,肖驰,白泉,李熠潇,刘瑞,周阳,邱洪志..特征增强驱动下基坑临近构筑物沉降预测[J].河南城建学院学报,2026,35(2):40-48,81,10.基金项目
国家自然科学基金项目(42101089) （42101089）
四川省科技计划资助项目(2024NSFSC0926) （2024NSFSC0926）
湖北省路桥集团有限公司科技研发项目(20253913) （20253913）
