The Bridge Maintenance Management in the Context of Big Data

Abstract

The maintenance and management of bridge is crucial to their normal operation. The application of big data technology makes the processing of massive data in the process of bridge maintenance and management more timely and accurate. In order to evaluate the status of suspension bridge in operation period more accurately and timely, on the basis of summarizing the big data sources of bridge, wavelet separation method is used to separate the waveform of displacement data at the support of suspension bridge. Considering the influence of temperature on displacement data, the sections with inconsistent temperature and displacement curves were eliminated, and the data were divided into three continuous time periods for fitting analysis. The analysis results show that the fitting and analysis of the temperature beam end longitudinal displacement data in each continuous period can more accurately and timely evaluate the status of the key constraint devices of the bridge, and then provide data support for the bridge maintenance management.