基于PPTSVD的桥梁移动荷载识别
Identification of Dynamic Axle Loads on Bridge Based on PPTSVD

为了改进时域法（time domain method，简称TDM）识别桥面移动荷载时存在的识别精度受测量噪声、响应类型及数量影响较大等缺陷，在截断奇异值分解（truncated singular value decomposition，简称TSVD）的基础上，提出了基于分段多项式截断奇异值分解（piecewise polynomial truncated singular value decomposition，简称 PPTSVD）识别桥梁移动荷载。采用简化欧拉梁模型，由反演车辆荷载作用下桥梁的弯矩响应和加速度响应识别桥面移动荷载，得到了不同噪声水平下TDM，TSVD与PPTSVD的识别结果。研究结果表明，与采用奇异值分解（singular value decomposition，简称SVD）进行常规降噪的TDM相比，采用TSVD识别移动荷载在识别精度和抗噪性能方面均有一定提高，且由TSVD改进的PPTSVD识别方法较前两种方法具有更加明显的优势；PPTSVD识别精度高、识别结果受响应类型及响应组合影响较小且具有良好的鲁棒性，更适用于桥梁移动荷载的现场识别。
A piecewise polynomial truncated singular value decomposition (PPTSVD) algorithm for the identification of moving force under noise and various sensor is developed based on the truncated singular value decomposition (TSVD). An Euler beam is introduced to simulate the passing vehicle. The bending moment and acceleration responses are collected from the simplified system under various noise. Traditional time domain method (TDM),TSVD and PPTSVD are used to identify the load history, respectively. The results show that the precision and robust noise immunity of TSVD is better than TDM using singular value decomposition (SVD). PPTSVD is superior to TDM and TSVD with higher identification accuracy and stronger robust noise immunity. Besides, it is less sensitive to the variety of sensor and their ways of combination. These advantages are beneficial to the application of PPTSVD in the field identification of dynamic axle loads on bridge.