基于LightGBM-Informer的盾构隧道管片上浮长时间序列预测模型被引量：1

Long sequence time series model to predict uplift of segmental lining in shield tunnel based on LightGBM-Informer

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摘要基于机器学习预测施工期盾构刀盘前方管片上浮值,有助于及时调整盾构控制参数以缓解管片上浮病害。然而,已有模型在长时间序列预测问题上的性能不佳,难以精确预测盾构刀盘前方多环管片上浮值。通过考虑盾构控制、姿态参数及地层信息的影响,结合Boruta算法,确定模型输入特征;利用小波变换滤波器、完备自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)方法消除时间序列数据噪声,构建了一种基于LightBGM-Informer的盾构隧道施工期管片上浮预测模型。通过南京和厦门地区某地铁盾构隧道监测数据,验证了所提模型的准确性和适用性。结果表明,所提模型预测精度较循环神经网络(recurrent neural network,RNN)、长短时记忆网络(long short-term memory,LSTM)、门控循环单元(gated recurrent unit,GRU)、Transformer等模型有所提升,且在地质条件不同的数据集上具有良好的泛化性;随着预测序列长度的增加,该模型的性能优势更突出,可准确预测盾构刀盘前方1~2环未施工管片的上浮值。基于沙普利加和解释(Shapley additive explanations,SHAP)方法的特征重要性分析指出,土舱压力及盾头、盾尾垂直位移对管片上浮影响显著。所提模型可为复杂环境下富水地层盾构隧道管片施工智能化控制提供理论指导。 Utilizing machine learning to predict the uplift of shield tunnel linings ahead of the cutterhead during construction enables timely adjustments of control parameters,mitigating lining uplift issues.Nevertheless,existing models exhibit limited performance in long sequence time-series forecasting(LSTF)and face challenges in accurately predicting the uplift of multiple lining rings ahead of the shield cutterhead.Considering the impact of shield control,attitude parameters,and geological condition,and utilizing the Boruta algorithm to determine model input features,a shield tunnel segment uplift prediction model based on LightGBM-Informer was proposed.This model incorporates a wavelet transform filter and a complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method to eliminate noise in time series data.The accuracy and applicability of the proposed model were validated using the monitoring data from subway shield tunnel projects in Nanjing and Xiamen.The results demonstrate that the model exhibits enhanced prediction accuracy in comparison to other models,including recurrent neural network(RNN),long short-term memory(LSTM),gated recurrent unit(GRU),and Transformer.Additionally,it demonstrates robust generalization capabilities across diverse geological condition datasets.As the length of the prediction sequence increases,the performance advantages of the model become more pronounced,accurately predicting the uplift of 1−2 rings of linings ahead of the shield cutterhead.Feature importance analysis based on Shapley additive explanations(SHAP)method indicates that earth chamber pressure and vertical displacement at the shield head and tail have significant impacts on lining uplift.The model provides theoretical guidance for intelligent control of shield tunnel lining construction in complex,water-rich environments.

作者真嘉捷赖丰文黄明李爽许凯 ZHEN Jia-jie;LAI Feng-wen;HUANG Ming;LI Shuang;XU Kai(College of Civil Engineering,Fuzhou University,Fuzhou,Fujian 350108,China)

机构地区福州大学土木工程学院

出处《岩土力学》 EI CAS CSCD 北大核心 2024年第12期3791-3801,共11页 Rock and Soil Mechanics

基金国家自然科学基金(No.41972276,No.52408356) 福建省自然科学基金(No.2020J06013) 福建省“雏鹰计划”青年拔尖人才项目(No.00387088)。

关键词盾构隧道管片上浮长时间序列预测问题 Informer模型 SHAP方法 shield tunnel lining uplift long sequence time-series forecasting problem Informer model Shapley additive explanations(SHAP)method

分类号 TU457 [建筑科学—岩土工程]

作者简介第一作者:真嘉捷,男,1996年生,博士研究生,主要从事盾构法隧道智能化掘进方面的研究。E-mail:Ternuraa148@163.com;通讯作者:黄明,男,1983年生,博士,教授,博士生导师,主要从事岩土力学与工程方面的教学与研究工作。E-mail:huangming05@fzu.edu.cn。