摘要
Abstract
To mitigate safety risks induced by excessive additional deformation of existing structures in closely aligned underpass projects—such as inadequate structural bearing capacity and uneven rail transit lines—this study investigates the disturbance deformation mechanism and develops a prediction model for the overlying existing structures during new station construction.Using the case of a newly built metro station at zero distance beneath an existing station as the engineering context,the zero-clearance excavation method and an automated monitoring scheme are first introduced.Subsequently,based on field monitoring data,the disturbance-induced deformation response of the existing station structure is analyzed,covering vertical and horizontal displacement,track bed settlement,rail level difference,and track gauge variation.Finally,a structural deformation prediction model integrating a Long Short-Term Memory(LSTM)neural network,an Adaptive Weighted Particle Swarm Optimization(AWPSO)algorithm,and Prior Information Constraints(PIC)is constructed and validated.The results demonstrate that the proposed AWPSO-LSTM-PIC model effectively incorporates prior information constraints and a hyperparameter optimization module,exhibiting outstanding predictive performance in small-sample,multi-task prediction scenarios.The average coefficient of determination(R²)reaches 0.938,reflecting considerable interpretability and generalization capability.关键词
密贴下穿/既有结构/变形规律/时序预测/先验信息约束/自适应惯性权重Key words
closely underpassing/existing structures/deformation law/time series prediction/prior information constraint/adaptive inertia weight分类
建筑与水利
