Damage identification method based on improved similar Bayesian calculation

A damage identification and Bayesian technology, applied in computing, special data processing applications, instruments, etc., can solve problems such as increased calculation, unsolvable regularization constants, and practical discount of Bayesian methods to improve solution efficiency , improve computing efficiency, and enhance practicality

Active Publication Date: 2015-02-25
FUZHOU UNIV
View PDF2 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the regularization constant in the parameter posterior probability distribution formula is often unable to be solved, and the Markov chain Monte Carlo method is required [17] to obtain an approximate solution for the posterior distribution
When the structural model is complex and contains many unkn

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Damage identification method based on improved similar Bayesian calculation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0027] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0028] The present invention combines approximate Bayesian calculations [18] , Monte Carlo Markov Chain Sampling [17] and the random response surface [19] 3 methods, an improved Bayesian damage identification method is proposed. First, the approximate Bayesian calculation is used to make the solution process of the parameter posterior probability distribution unnecessary to calculate the likelihood function of the parameter, which solves the problem that the likelihood function cannot be solved in practical engineering applications. The Monte Carlo Markov chain sampling first establishes a probability model similar to the problem to be solved, then performs random simulation or statistical sampling on the model, and then uses the sample to obtain the estimated value of its statistical characteristics, and uses it as the original problem...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a damage identification method based on improved similar Bayesian calculation. According to the damage identification method, a likelihood function of a parameter is not required to be calculated on solving of parameter posterior probability distribution by using similar Bayesian calculation, and the problem that the likelihood function cannot be solved in actual engineering application is solved; when Markov Chain Monte Carlo sampling is carried out, a probability model which is similar to a solved problem is established, then random sampling on the model is carried out, and a statistical characteristic estimated value can be obtained by using the sample and serves as an approximate solution of the original problem. Required calculated quantity on a complex engineering problem is quite high by using a Markov Chain Monte Carlo sampling method; by the damage identification method, a statistical characteristic value which is responded by a structure corresponding to a parameter sample is quickly calculated by a random response surface in a sampling process, a phenomenon that numerical solution is carried out by a finite element model is avoided, so that the calculation efficiency is greatly improved, and the problem that a Bayesian method cannot be implemented under the conditions of multiple parameters and large sample amount due to over large calculated quantity is solved.

Description

technical field [0001] The invention relates to a damage identification method based on improved approximate Bayesian calculation. Background technique [0002] Traditional actual engineering structures have been subjected to complex operating environments and external loads for a long time, and damages of different degrees and types will inevitably occur. With the growth of time, the existing damage will continue to accumulate, resulting in continuous degradation of structural performance. If the damage is not detected in time and effective reinforcement measures are not taken, catastrophic accidents may occur in the structure under extreme conditions. Damage identification as the core content of structural health monitoring system [1] , is one of the research hotspots in related fields in recent years. The existing damage identification methods can be classified into two categories: deterministic and uncertain methods. [2-4] . In the deterministic method, the paramete...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F19/00
Inventor 方圣恩董照亮姜绍飞林友勤
Owner FUZHOU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap