Affiliation:
1. School of Civil Engineering, Tongji University, Shanghai, China
2. Shanghai Engineering Research Center of Industrialized and Prefabricated Municipal Civil Engineering, Shanghai Urban Construction Design & Research Institute (Group) Co., Ltd, Shanghai, China
Abstract
During the service life of bridge structures, they will be affected by various physical and environmental changes, which comprehensively manifest on different time scales. In other words, structural effects will have different variation patterns at different time scales, which will be reflected in the multi-scale characteristics of structural mechanical properties over time. However, most of the existing bridge performance indicators are defined on a time scale corresponding to a single factor. Therefore, it is necessary to analyze, process, and apply bridge monitoring signals differently at different time scales and components of different factors. This article proposes a signal decomposition and reconstruction method that extracts signals of different components from strain monitoring signals of bridge structures. On this basis, different feature extraction and indicator recognition are carried out for different extracted components. Among them, for component A related to bridge vibration, this paper proposes a strain mode identification method based on statistical stability. This method is based on the assumption that the strain mode under random noise has approximate time invariant characteristics, and noise is eliminated through statistical averaging. The component B of the vehicle induced effect is obtained through empirical mode decomposition (EMD) and bandpass filtering, and the lateral collaborative performance of the assembled beam bridge is characterized based on the correlation coefficient of component BA bridge hinge joint damage identification method based on temperature strain is proposed for component C of temperature effect. The effectiveness and feasibility of the above workflow were verified through real bridge data.
Funder
China Railway Engineering Design and Consulting Group Co., Ltd
National Key Research and Development Program of China
Project of Ministry of Transport “Research on Key Technologies of Intelligent Highway With Large Traffic Volume”
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities