基于多类数据处理方法联合分析的运营桥梁安全性预警分级

Classification of Operational Bridge Safety Warning based on Joint Analysis of Multiple Data Processing Methods

为合理实现运营桥梁的安全性预警分级,结合去噪处理的实测数据,以累计变形序列、速率序列和加速度序列分别构建相应的预警判据,实现运营桥梁安全性预警分级的多源信息融合,充分保证分级结果的准确性。结果表明:PSO-DVMD模型可有效剔除桥梁变形数据中的随机噪声,适用于桥梁变形数据的去噪处理;不同监测点或监测项目在不同判据条件下的预警等级存在一定差异,按不利原则综合确定桥梁的安全性预警等级。运营桥梁安全预警分级为运营桥梁安全性评价提供了一种量化分级标准,值得进一步推广应用研究。

In order to reasonably realize the safety early warning classification of operational bridges,combined with the measured data of denoising processing,the corresponding early warning criteria were constructed based on the accumulated deformation sequence,rate sequence and acceleration sequence,to realize the multi-source information fusion of the safety early warning classification of operational bridges,and fully ensure the accuracy of the classification results.The results show that PSO-DVMD model can effectively remove random noise from bridge deformation data and is suitable for denoising bridge deformation data.There are some differences in the early warning levels of different monitoring points or monitoring items under different criteria,and the safety early warning levels of bridges are determined comprehensively according to the unfavorable principle.The early-warning classification of operational bridges provides a quantitative classification standard for the safety evaluation of operational bridges,which is worthy of further popularization and application.