UM
Novel Sparse Bayesian Learning and Its Application to Ground Motion Pattern Recognition
Mu, He-Qing; Yuen, Ka-Veng
2017-09
Source PublicationJOURNAL OF COMPUTING IN CIVIL ENGINEERING
ISSN0887-3801
Volume31Issue:5
AbstractA novel sparse Bayesian learning for correlated error (SBL-CE) algorithm is proposed to automatically search for an optimal model class with relevance features in regression problems of pattern recognition based on measured data and extracted features. The proposed SBL-CE algorithm is designed to overcome the disadvantage in the traditional optimal model searching approach for ground motion pattern recognition, which requires a huge or even intractable computational effort to examine a large number of different combinations of extracted features. The proposed SBL-CE algorithm introduces sophisticated hyperparameterization on the regression parameter vector in the ground motion prediction model, aiming to conduct a continuous optimal model search even when the number of extracted features is large. In addition, the prediction error independence assumption in the traditional learning approach is relaxed, so the derived optimization strategy can be applied to ground motion pattern recognition. The proposed SBL-CE algorithm is then used to analyze a database of strong ground motion records in the Tangshan region of China. It is shown that the model by the proposed SBL-CE algorithm is superior compared to the traditional models because it is capable of properly recognizing the pattern of ground motion in the target seismic region with high accuracy and robustness. (C) 2017 American Society of Civil Engineers.
KeywordBayesian learning Strong ground motion Maximum likelihood Model class selection Uncertainty quantification
DOI10.1061/(ASCE)CP.1943-5487.0000668
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Civil
WOS IDWOS:000417340500002
PublisherASCE-AMER SOC CIVIL ENGINEERS
The Source to ArticleWOS
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Cited Times [WOS]:6   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
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GB/T 7714
Mu, He-Qing,Yuen, Ka-Veng. Novel Sparse Bayesian Learning and Its Application to Ground Motion Pattern Recognition[J]. JOURNAL OF COMPUTING IN CIVIL ENGINEERING,2017,31(5).
APA Mu, He-Qing,&Yuen, Ka-Veng.(2017).Novel Sparse Bayesian Learning and Its Application to Ground Motion Pattern Recognition.JOURNAL OF COMPUTING IN CIVIL ENGINEERING,31(5).
MLA Mu, He-Qing,et al."Novel Sparse Bayesian Learning and Its Application to Ground Motion Pattern Recognition".JOURNAL OF COMPUTING IN CIVIL ENGINEERING 31.5(2017).
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