Structural damage recognition method based on Bayesian model

Abstract

The invention discloses a structural damage recognition method based on a Bayesian model. The method comprises the steps of firstly, using an empirical mode decomposition method to decompose obtainedsingle-point structure response through observation to obtain a time-varying intrinsic mode function to construct a likelihood function of the Bayesian model, using the likelihood function of the Bayesian model of the time-varying intrinsic function based on the single-point system structure response for an update method of the Bayesian model, and using a gradient Markov chain Monte Carlo algorithm in the design of the Bayesian model update method; direct sampling from model posterior probability distribution in which sampling is difficult to conduct is avoided, sampling is conducted in a series of simpler intermediate probability distribution which converges to the posterior probability distribution, through the method, an intermediate probability density function can be automatically selected, normalized parameters in a posterior probability distribution formula of model parameters are directly obtained, and the calculation efficiency is greatly improved.

Description

technical field [0001] The invention belongs to the field of structural health monitoring, and mainly relates to a structural damage identification method based on a Bayesian model. Background technique [0002] As urban land resources have become increasingly scarce in recent decades, large-scale construction of high-rise buildings and super high-rise buildings has become an important direction for the development of contemporary domestic and foreign construction industries. During the service period of high-rise buildings and super high-rise buildings, due to long-term load and environmental effects, their structural materials will continue to age as time goes by, component damage will continue to accumulate, and the bearing capacity of the structure will continue to decrease, resulting in a decrease in the performance of the building structure. Even damage, a serious threat to people's lives and property safety. Therefore, it is of great practical significance to identif...

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Problems solved by technology

However, the traditional Bayesian method is often unable to solve the normalized parameters in the formula of the posterior probability distribution of the model parameters. It is necessary to use the Markov chain Monte Carlo method to solve the approximate solution of the posterior probability distribution. With the complexity of the structural model And the increase of the number of unknown parameters will greatly increase the calculation and difficulty of the method, and the inability to obtain the expression of the likelihood function will greatly limit the practicability of the method

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