Bridge probability damage detection method based on autoregression model and Gaussian process

Abstract

The invention provides a bridge probability damage detection method based on an autoregression model and a Gaussian process, and the method can achieve the processing of the response data of an acceleration sensor through a Gaussian process classifier and a Gaussian process regression machine, and obtains the damage position and damage degree information. The bridge probability damage detection method based on the autoregressive model and the Gaussian process has the advantages that implementation of the scheme does not depend on bridge undamaged state information and external excitation information, the implementation difficulty is low, the damage position and the damage degree can be recognized at the same time, and unreliable results are eliminated.

Description

technical field [0001] The invention relates to a bridge damage detection method, in particular to a bridge probability damage detection method based on an autoregressive model and a Gaussian process. Background technique [0002] As of the end of 2018, the number of highway and railway bridges in my country has exceeded one million. With the increase of service life, the internal condition of the bridge structure continues to deteriorate, resulting in a continuous decrease in the structural bearing capacity, which seriously endangers the normal performance of the bridge structure during operation. It is an important basis for bridge management and maintenance to realize bridge structure damage location and damage degree measurement by corresponding means. [0003] Bridge damage identification includes four levels of content: one is to identify damage; the other is to locate damage; the third is to identify the degree of damage; the fourth is to predict the remaining life of...

AI Technical Summary

Problems solved by technology

Due to the influence of environmental factors, there are still problems in the field of bridge damage identification, such as difficult identification of multiple damage states and difficulty in judging the reliability of prediction results.

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