Real-time bridge structure damage state recognition method and system

Abstract

The invention discloses a real-time bridge structure damage state recognition method and system. The method comprises the steps of S1, obtaining to-be-tested data; s2, compressing the to-be-tested data at a first compression ratio to obtain first compressed data, classifying the compressed data by using a first classification algorithm to obtain a first classification result, and if the first classification result is damage, entering S3; and S3, compressing the to-be-tested data at a second compression ratio to obtain second compressed data, and classifying the compressed data by using a second classification algorithm to obtain a second classification result. Or the method comprises the steps of S1, S2, S3 and S4; and S4, compressing the to-be-tested data at a third compression ratio, andclassifying the compressed data by using a third classification algorithm to obtain a third classification result, wherein the second compression ratio is greater than the third compression ratio butless than the first compression ratio. Compared with a traditional method, the method has the advantages that massive high-dimensional bridge monitoring time series data can be more efficiently, flexibly and better analyzed, and the bridge damage state is evaluated in real time.

Description

technical field [0001] The invention relates to the field of bridge monitoring, in particular to a real-time bridge structure damage state identification method and system. Background technique [0002] With the increasing number of large bridge structures and the wide application of large bridge structural health monitoring systems, bridge structural health monitoring time series data presents significant "big data" characteristics such as huge data volume, various types, and rapid growth, and the cost of data storage and transmission is high. This results in lower computational efficiency and accuracy. Bridge structural health monitoring data, as a kind of high-dimensional numerical time series data, has the problem of high storage cost if it is stored locally. If it is stored remotely in the data center, data analysis cannot be performed in a timely and efficient manner. Traditional time series analysis-based bridge structural damage status identification methods are mos...

AI Technical Summary

Problems solved by technology

Traditional bridge structure damage state identification methods based on statistical models have complicated algorithms and high space-time consumption. Often, they can only judge whether the bridge is damaged, but cannot quantify the damage degree of the bridge structure, and cannot analyze data in a timely and effective manner. How to efficiently process the time series data of bridge structural health monitoring and identify the status of bridge structures in real time is an important problem that needs to be solved

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