Bridge monitoring data modal identification method and device

Abstract

The embodiment of the invention provides a bridge monitoring data modal identification method and device, and the method comprises the steps: simplifying an original Hankel matrix, and obtaining a simplified Hankel matrix; creating an autocorrelation matrix according to the simplified Hankel matrix; determining a modal order according to the autocorrelation matrix; calculating a projection matrixaccording to the simplified Hankel matrix, and weighting the projection matrix to obtain a weighted projection matrix; singular value decomposition and the modal order are carried out on the weightedprojection matrix to obtain an observable matrix; and identifying modal parameters according to the observable matrix. An autocorrelation matrix is created through the simplified Hankel matrix, and the model order is accurately selected by utilizing the relative difference between eigenvalues of the autocorrelation matrix, so that an unimportant mode in an estimation result is eliminated, and pseudo-mode interference caused by noise and calculation errors is avoided.

Description

technical field [0001] The invention relates to the technical field of bridge engineering, in particular to a mode recognition method and device for bridge monitoring data. Background technique [0002] The bridge deck on the bridge will be damaged to varying degrees over time. Crack damage can be directly observed with the eyes, but if there is damage inside the bridge deck, it is difficult to observe. If it is not treated in time, Dangers can easily occur, so bridge inspection is very important. [0003] The most important content of bridge detection is to identify bridge loss or deterioration degree through monitoring data. The modal parameter identification of bridge monitoring data is one of the key links to determine the damage location and damage degree of the bridge and accurately evaluate the remaining life. The data-driven random subspace identification algorithm in technology is one of the two implementations of random subspace identification. The algorithm uses...

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Problems solved by technology

[0004]The decomposition of high-dimensional matrices requires a lot of memory and computing time, which makes it unsuitable for situations where large amounts of data need to be processed or high computational efficiency is required

Secondly, when using subspace identification technology to extract modal parameters, the system order of the identification model must be selected. In order to avoid underestimating the system order, the system order is usually overestimated. Due to the use of redundant models, it is inevitable that There will be pseudo-mode interference caused by noise and calculation errors

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