摘要
接头张合变形预测有助于提高沉管隧道系统运营安全性。针对现有模型预测精度不佳及适用性不足等问题,提出一种新的沉管隧道接头张合变形预测方法。首先通过麻雀搜索算法(SSA)对长短时记忆网络(LSTM)的超参数进行自适应组合优化,构建SSA-LSTM模型实现张合变形信息的特征学习与初步预测;在此基础上采用Ljung-Box(LB)检验对残差序列进行分析,并引入小波神经网络(WNN)进一步提取残差序列中的有效信息,得到残差序列修正值;最后将SSA-LSTM模型预测结果与残差序列修正结果进行叠加重构,得到张合变形预测值。依托港珠澳大桥沉管隧道工程对所提方法进行验证,结果表明:所提模型整体预测性能优异,可以充分挖掘张合变形序列中的有效信息,在考虑张合变形序列局部特征中更具优势;最终预测决定系数(R^(2))达到了0.9994、均方根误差(RMSE)为0.0072 mm、平均绝对误差(MAE)为0.0066 mm,相比LSTM、SSA-LSTM和传统的SVR、BP、XGBoost模型具有更高的预测精度和稳定性,能更好地描述接头张合变形趋势。研究成果可为深入探究接头张合变形发展变化规律提供方法参考。
The prediction of joint opening-closing deformations helps improve the operational safety of immersed tunnel systems.Addressing issues such as the poor accuracy and limited applicability of existing models,a novel method for predicting the joint opening-closing deformations of immersed tunnels is proposed.Firstly,the Sparrow Search Algorithm(SSA)is used to adaptively optimize the hyperparameters of the Long Short-Term Memory(LSTM)network,constructing an SSA-LSTM model to achieve feature learning and preliminary prediction of the deformation information.Based on this,the Ljung-Box(LB)test is employed to analyze the residual sequences,and a Wavelet Neural Network(WNN)is introduced to further extract effective information from the residual sequences,obtaining corrected values for the residual sequences.Finally,the prediction results from the SSA-LSTM model and the corrected residual sequence results are superimposed and reconstructed to obtain the predicted values of the joint opening-closing deformations.The proposed method is validated based on the immersed tube tunnel project of the Hong Kong-Zhuhai-Macao Bridge.The results show that:The proposed model exhibits excellent overall prediction performance,effectively mining useful information from the deformation sequences and having an advantage in considering the local features of the deformation sequences.The final prediction achieve an R^(2) of 0.9994,RMSE of 0.0072 mm,and MAE of 0.0066 mm,which are higher in accuracy and stability compared to LSTM,SSA-LSTM,and traditional SVR,BP,and XGBoost models.This method can serve as a means to deeply explore the development and variation patterns of joint opening-closing deformations.
作者
李书亮
李科
郭鸿雁
陈建忠
Li Shuliang;Li Ke;Guo Hongyan;Chen Jianzhong(Hong Kong-Zhuhai-Macao Bridge Authority,Zhuhai,Guangdong 440400,P.R.China;China Merchants Chongqing Communications Technology Research and Design Institute Co.,Ltd.,Chongqing 400067,P.R.China)
出处
《地下空间与工程学报》
北大核心
2025年第5期1763-1770,共8页
Chinese Journal of Underground Space and Engineering
基金
国家重点研发计划(2019YFB1600702)。
关键词
沉管隧道
接头张合变形预测
麻省搜索算法
长短时记忆网络
小波神经网络
immersed tube tunnel
joint opening-closing deformation prediction
sparrow search algorithm
long short-term memory
wavelet neural network
