中国铁道科学2023，Vol.44Issue(6)：25-33,9.DOI:10.3969/j.issn.1001-4632.2023.06.03
基于神经网络的既有站房结构位形反演法
Configuration Inversion Algorithm for the Existing Long-Span Spatial Structures of Existing High-Speed Railway Station
摘要
Abstract
In response to the limitations of traditional structural configuration detection due to insufficient sample capacity in establishing comprehensive existing structural models,a neural network-based structural configuration inversion algorithm is proposed for identifying existing large-span station structures.Based on the linear elastic assumption of large-span spatial structures and the correlation between configuration deviations and differences in structural systems and stiffness,a structural configuration base vector space is constructed by superimposing equivalent random loads.Using configuration base vectors and measured configurations at sampled nodes as input,a backpropagation(BP)neural network is trained to obtain the actual overall structure configuration.The performance of the algorithm is validated and analyzed using the large-span spatial structure of the Pingshan high-speed railway station in Shenzhen as an example.The results indicate that the number of random loads and training samples are the primary factors influencing the accuracy and convergence speed of the inversion algorithm.For large-span spatial structures of the same type as the Pingshan station building,when the number of base vectors exceeds 200 and 300 sets of training samples are selected,the inversion effect with low error and fast convergence speed can be obtained.The proposed neural network-based structural configuration inversion algorithm can effectively infer the true overall configuration of the structure based on a small number of measured node configurations.关键词
既有站房/结构位形/检测/反演算法/BP神经网络/随机荷载/基向量Key words
Existing station building/Structural configuration/Detection/Inversion algorithm/BP neural network/Random load/Base vector分类
建筑与水利引用本文复制引用
李浩,邹勇..基于神经网络的既有站房结构位形反演法[J].中国铁道科学,2023,44(6):25-33,9.基金项目
国家自然科学基金资助项目(51678431) （51678431）
