基于CEEMDAN-SSA-ELM-LSTM模型的地铁车站深基坑支护桩水平变形预测  

Forecasting of Horizontal Deformation in Retaining Piles of Subway Station Deep Foundation Pits Based on the CEEMDAN-SSA-ELM-LSTM Model

作　　者：刘彦伟[1] 彭洁 任连伟[2] 高保彬[1] 郭佳奇[2] 王泽武 韩红凯 LIU Yanwei;PENG Jie;REN Lianwei;GAO Baobin;GUO Jiaqi;WANG Zewu;HAN Hongkai(School of Safety Science and Engineering,Henan Polytechnic University,Jiaozuo 454000,China;School of Civil Engineering,Henan Polytechnic University,Jiaozuo 454000,China;Zhengzhou Metro Group Co.,Ltd.,Zhengzhou 450000,China)

机构地区：[1]河南理工大学安全科学与工程学院,河南焦作454000 [2]河南理工大学土木工程学院,河南焦作454000 [3]郑州地铁集团有限公司,河南郑州450000

出　　处：《防灾减灾工程学报》2025年第1期34-46,共13页Journal of Disaster Prevention and Mitigation Engineering

基　　金：中国工程院院地合作项目(2023HENZDB5);区域创新发展联合基金(AQ20230304)资助。

摘　　要：灾害监测与预测是岩土工程领域至关重要的任务之一,但工程监测数据中的非平稳性和非线性一直是预测的难点。为应对此挑战,引入数据驱动算法极限学习机(ELM)、长短时记忆神经网络模型(LSTM),结合自适应噪声完备集合经验模态分解(CEEMDAN)和麻雀搜索算法(SSA),提出了一种改进的地铁车站深基坑变形组合预测模型。首先,通过CEEMDAN将支护桩水平位移序列分解为趋势项和波动项,降低数据的非平稳性。其次,为充分考虑分解序列差异的非线性特征,分别采用SSA优化后的ELM和LSTM模型对低频趋势项与高频波动项进行预测,并将结果叠加重构为最终预测值。最后,以郑州市某地铁车站深基坑为例,通过设置消融实验、对比实验和泛化性验证实验,系统评估了模型的准确性与实用性。结果表明:该模型在精度和稳定性方面显著优于其他模型,其中R2提升了2.88%~23.62%,RMSE和MAPE分别降低了6.63%~41.13%、8.08%~64.79%。这充分说明模型在应对数据非平稳性和捕捉非线性特征方面表现出色,具备良好的可靠性和广泛的应用前景,可为岩土工程中的灾害防治提供新的思路和技术支持。Disaster monitoring and prediction are critical tasks in geotechnical engineering.However,the inherent non-stationarity and non-linearity of engineering monitoring data have long posed challenges for accurate forecasting.In response to this challenge,this study proposes an improved combination prediction model for the deformation of deep foundation pits in subway stations.The model integrates data-driven algorithms,including Extreme Learning Machine(ELM)and Long Short-Term Memory(LSTM)neural networks,along with Complete Ensemble Empirical Mode Decomposition with Adap-tive Noise(CEEMDAN)and the Sparrow Search Algorithm(SSA).Initially,CEEMDAN was em-ployed to decompose the horizontal displacement sequence of the retaining piles into trend and fluctua-tion components,thereby reducing the data's non-stationarity.Furthermore,to fully capture the non-linear characteristics of the differences among each decomposed sequence,SSA-optimized ELM and LSTM models were employed to predict the low-frequency trend component and high-frequency fluc-tuation component,respectively.The results were then combined to reconstruct the final prediction values.Finally,the accuracy and practicality of the model were systematically evaluated through abla-tion,comparative and generalization validation experiments using a deep foundation pit example in Zhengzhou subway station.The results demonstrated that the proposed model exhibited superior per-formance in terms of both accuracy and stability when compared to other models.The R²improve-ments ranged from 2.88%to 23.62%,while the reductions in RMSE and MAPE were observed to be between 6.63%and 41.13%and between 8.08%and 64.79%,respectively.The model's efficacy in addressing data's non-stationarity and capturing nonlinear features is evident,offering high reliability and broad application prospects.The model provides novel insights to and technical support for disas-ter prevention in geotechnical engineering.

关 键 词：基坑工程 支护桩 变形监测 组合预测 深度学习 

分 类 号：TU443[建筑科学—岩土工程]