Temperature and strain correlation-based bridge damage identification method

Abstract

The invention discloses a temperature and strain correlation-based bridge damage identification method. The method comprises the steps of 1) performing abnormal value removal on bridge health monitoring data and extracting a strain component influenced by temperature; 2) fitting temperature and strain component data by using a linear regression model, and extracting a corresponding regression coefficient; 3) performing normal check and normal conversion operation on the regression coefficient, and then performing statistics on a mean value mu and a standard deviation sigma; and 4) preprocessing latest monitoring data, performing fitting by using the linear regression model to obtain a regression coefficient, and according to a 3 sigma criterion, judging whether a bridge is damaged or not.The method is simple and efficient in principle, and does not involve excessively complex algorithms; a large amount of data (the required data directly comes from an existing bridge health monitoringsystem, additional equipment such as a sensor and the like does not need to be added, and the bridge operation cost is reduced) collected by the monitoring system can be effectively utilized; and thecontinuously accumulated monitoring data can further improve the effect of the method.

Description

technical field [0001] The invention relates to the technical field of damage detection and identification of bridge structures, in particular to a bridge damage identification method based on the correlation between temperature and strain. Background technique [0002] Bridge construction is a key part of national infrastructure construction and an important part of the transportation network system. With the rapid development of the country's economic construction, the road traffic volume in most parts of the country is increasing day by day; since the bridge was put into use, It has been in the erosive environment of the external environment and natural disasters for a long time. In addition, under the influence of negative factors such as the long-term cumulative fatigue effect of the vehicle load on the bridge deck, the destructive effect of accidental loads, and the increasingly frequent occurrence of overweight loads, the bridge structure is unavoidable. The ground wi...

AI Technical Summary

Problems solved by technology

However, most of the research results are mainly based on theoretical analysis, model tests and simulation analysis. In actual measurement, the bridge will be affected by various external factors, resulting in large noise in the monitoring data. In addition, due to equipment failure during the monitoring process Faults may also lead to the loss of monitoring data, and many damage methods have high requirements for data integrity or are sensitive to noise, and perform poorly on measured data

Images