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| Structural damage identification using ABC combined with improved PMC sampling |
| FANG Sheng’en1,2, CHEN Shan1 |
1.School of Civil Engineering, Fuzhou University, Fuzhou 350108, China;
2.National and Local United Research Center for Seismic and Disaster Informatization of Civil Engineering, Fuzhou University, Fuzhou 350108, China |
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Abstract In order to avoid sampling being immersed in low-probability areas and to raise sampling efficiency, the population Monte Carlo (PMC) sampling algorithm was improved and then combined with the approximate Bayesian calculation (ABC) and stochastic response surface (SRS) to propose a probabilistic damage identification method.Firstly, PMC algorithm was embedded in ABC, and sample variance in each iteration step was used to perturb a particle swarm, and obtain adaptive weight coefficients.An error function was constructed to measure the similarity between simulated and measured samples, and replace the likelihood function.Then the explicit expression for structural stochastic response was established using SRS to greatly improve the calculation efficiency of response statistical features.Finally, obtained statistical values of parametric posterior probability distribution were taken as damage indexes.According to indexes’ changes before and after damage, damage locations and degrees were judged.Damages of a test reinforced concrete beam under a single working condition and multiple working conditions were identified, respectively.It was shown that the proposed method can be used to effectively improve the calculation efficiency of Bayesian inference process under the condition of ensuring parametric posterior distribution’s estimation accuracy.
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Received: 03 August 2018
Published: 28 February 2020
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