Method for recognizing abnormal condition of bridge monitoring data based on fuzzy clustering

Abstract

The invention discloses a method for recognizing an abnormal condition of bridge monitoring data based on fuzzy clustering. The method comprises the following steps: 1) taking the monitoring data under the normal running state of a bridge as a to-be-analyzed training sample set; 2) classifying the monitoring data according to different temperature monitoring conditions; 3) adopting a pauta criterion for primarily screening all the classifications and removing noise and isolated points; 4) endowing all the samples with different fuzzy membership values; 5) performing fuzzy clustering on the primarily screened classifications according to different fuzzy membership; 6) respectively treating the training samples under different temperatures, thereby acquiring class centers and class boundaries under different temperatures; 7) comparing the class centers and class boundaries, thereby judging the running condition of the bridge. According to the invention, the fuzzy clustering technique is adopted for analyzing and processing mass bridge monitoring data. The alarm threshold value at a measured point is corrected on the basis of a statistics principle, so that the alarm threshold value can be more suitable for the practical running state of the bridge.

Description

Technical field [0001] The invention relates to the field of analysis and processing of bridge monitoring data, in particular to a method for identifying abnormal conditions of bridge monitoring data based on fuzzy clustering. Background technique [0002] In recent years, with the rapid development of the national economy and the construction of the national transportation network, the total number of highways and bridges has continued to increase. According to the 2015 Statistical Bulletin of the Development of the Transportation Industry, as of the end of 2015, there were 779,200 highway bridges, 3,894 extra-large bridges, and 79,512 large bridges across the country. However, for the bridges that have been built and put into operation, they will inevitably be affected by environmental, load, aging and other factors during their service, which will lead to degradation of structural performance and pose safety hazards. In addition, the increasing traffic volume also makes many b...

AI Technical Summary

Problems solved by technology

However, in practical applications, due to the difficulty in defining the boundary conditions of the calculation model, the model structure cannot be accurately constructed, and the influence of temperature on the bridge structure is underestimated, so the set threshold is not accurate enough, which makes the application of this method in bridge monitoring data early warning not yet satisfactory

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