{"title":"An active learning Kriging-based Bayesian framework for probabilistic structural model exploration","authors":"","doi":"10.1016/j.jsv.2024.118730","DOIUrl":null,"url":null,"abstract":"<div><div>The Bayesian framework in structural health monitoring includes both modal identification and model exploration. Probabilistic model exploration, also named as model updating, can effectively estimate the structural parameters and quantify their uncertainties. However, it can be computationally intensive on application to real-world large-scale structures. Meta-models, e.g. Kriging models, can help tackle this challenge but they also introduce more uncertainties. In this paper, a novel Bayesian framework combining the active learning Kriging approach is proposed. The framework comprises three major components: the improved fast Bayesian spectral density approach for modal identification, the active learning Kriging method for meta-modelling, and the Bayesian structural model exploration. The Transitional Markov Chain Monte Carlo algorithm is implemented throughout the framework to sample the posterior distributions. The uncertainties from three aspects, i.e., (1) measurements, (2) meta-model construction and (3) finite element modelling, are considered in definition of the likelihood function adopted in both the active learning and model exploration processes. Compared with the ordinary Kriging model and adaptive Kriging approach using U function, the proposed active learning method significantly reduces the uncertainties of the Kriging predictor and improves its local prediction performance with fewer samples. The proposed framework is validated by a continuous test beam in the laboratory and applied to a real-world cable-stayed bridge using structural health monitoring data. A mode-matching criterion is used to overcome the difficulty of closely spaced modes in model exploration of the cable-stayed bridge. As the proposed framework is data-driven, no weighting hyperparameters are required. The active learning Kriging-based Bayesian framework can directly process structural dynamic time history response and conduct probabilistic model exploration with multiple uncertainties included, and therefore is promising in application to major structures.</div></div>","PeriodicalId":17233,"journal":{"name":"Journal of Sound and Vibration","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sound and Vibration","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022460X24004929","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
The Bayesian framework in structural health monitoring includes both modal identification and model exploration. Probabilistic model exploration, also named as model updating, can effectively estimate the structural parameters and quantify their uncertainties. However, it can be computationally intensive on application to real-world large-scale structures. Meta-models, e.g. Kriging models, can help tackle this challenge but they also introduce more uncertainties. In this paper, a novel Bayesian framework combining the active learning Kriging approach is proposed. The framework comprises three major components: the improved fast Bayesian spectral density approach for modal identification, the active learning Kriging method for meta-modelling, and the Bayesian structural model exploration. The Transitional Markov Chain Monte Carlo algorithm is implemented throughout the framework to sample the posterior distributions. The uncertainties from three aspects, i.e., (1) measurements, (2) meta-model construction and (3) finite element modelling, are considered in definition of the likelihood function adopted in both the active learning and model exploration processes. Compared with the ordinary Kriging model and adaptive Kriging approach using U function, the proposed active learning method significantly reduces the uncertainties of the Kriging predictor and improves its local prediction performance with fewer samples. The proposed framework is validated by a continuous test beam in the laboratory and applied to a real-world cable-stayed bridge using structural health monitoring data. A mode-matching criterion is used to overcome the difficulty of closely spaced modes in model exploration of the cable-stayed bridge. As the proposed framework is data-driven, no weighting hyperparameters are required. The active learning Kriging-based Bayesian framework can directly process structural dynamic time history response and conduct probabilistic model exploration with multiple uncertainties included, and therefore is promising in application to major structures.
期刊介绍:
The Journal of Sound and Vibration (JSV) is an independent journal devoted to the prompt publication of original papers, both theoretical and experimental, that provide new information on any aspect of sound or vibration. There is an emphasis on fundamental work that has potential for practical application.
JSV was founded and operates on the premise that the subject of sound and vibration requires a journal that publishes papers of a high technical standard across the various subdisciplines, thus facilitating awareness of techniques and discoveries in one area that may be applicable in others.