Mixed Skewness Probability Modeling and Extreme Value Predicting for Physical System Input/Output Based on Full Bayesian Generalized Maximum-Likelihood Estimation

Dynamic parameterization of the statistical characteristics of structural systems' measured input and output data is an important task for the digital twin modeling and intelligent risk assessment of transportation infrastructures. Characteristics of mixed skewness probability are common in str...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 73; S. 1
Hauptverfasser:	Zhang, Xiaonan, Ding, Youliang, Zhao, Hanwei, Yi, Letian, Guo, Tong, Li, Aiqun, Zou, Yang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Bayes methods Bayesian analysis Bridges Digital twin Digital twins Extreme value prediction Extreme values Intelligent monitoring Load modeling Maximum likelihood estimation Mixed probability modeling Modelling Parameterization Parameterization of dynamic system Predictive models Probabilistic logic Probabilistic models Probability theory Risk assessment Skewness Statistical analysis Transportation infrastructures Vehicle dynamics
ISSN:	0018-9456, 1557-9662
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Abstract	Dynamic parameterization of the statistical characteristics of structural systems' measured input and output data is an important task for the digital twin modeling and intelligent risk assessment of transportation infrastructures. Characteristics of mixed skewness probability are common in structural systems, and its extreme value represents the risk state closest to the critical limit. The generalized extreme value mixture model (GEVMM) can consider multiple factors that interfere with each other, based on which the generalized maximum likelihood estimation (GMLE) of full Bayesian is introduced. The proposed GMLE-GEVMM can conduct the modeling of mixed skewness probability (mainly including strong uni-factor and multi-factor statistical characteristics) by fusing the prior physical information for each parameter. A reliable paradigm for predicting the dynamic extreme value of practical engineering is presented. The proposed methods can overcome the probabilistic modeling problem for complex mixed skewness characteristics, and significantly improve the prediction accuracy of the extreme value of probability. The continuous monitoring data from a real bridge is used for validation. The modeling and predicting results verified the proposed methods' strong applicability and high accuracy for complex probabilistic system input and output characteristics from in-service structures.
AbstractList	Dynamic parameterization of the statistical characteristics of structural systems’ measured input and output data is an important task for the digital twin modeling and intelligent risk assessment of transportation infrastructures. Characteristics of mixed skewness probability are common in structural systems, and its extreme value represents the risk state closest to the critical limit. The generalized extreme value mixture model (GEVMM) can consider multiple factors that interfere with each other, based on which the generalized maximum likelihood estimation (GMLE) of full Bayesian is introduced. The proposed GMLE-GEVMM can conduct the modeling of mixed skewness probability (mainly including strong uni-factor and multifactor statistical characteristics) by fusing the prior physical information for each parameter. A reliable paradigm for predicting the dynamic extreme value of practical engineering is presented. The proposed method can overcome the probabilistic modeling problem for complex mixed skewness characteristics and significantly improve the prediction accuracy of the extreme value of probability. The continuous monitoring data from a real bridge is used for validation. The modeling and predicting results verified the proposed methods’ strong applicability and high accuracy for complex probabilistic system input and output characteristics from in-service structures. Dynamic parameterization of the statistical characteristics of structural systems' measured input and output data is an important task for the digital twin modeling and intelligent risk assessment of transportation infrastructures. Characteristics of mixed skewness probability are common in structural systems, and its extreme value represents the risk state closest to the critical limit. The generalized extreme value mixture model (GEVMM) can consider multiple factors that interfere with each other, based on which the generalized maximum likelihood estimation (GMLE) of full Bayesian is introduced. The proposed GMLE-GEVMM can conduct the modeling of mixed skewness probability (mainly including strong uni-factor and multi-factor statistical characteristics) by fusing the prior physical information for each parameter. A reliable paradigm for predicting the dynamic extreme value of practical engineering is presented. The proposed methods can overcome the probabilistic modeling problem for complex mixed skewness characteristics, and significantly improve the prediction accuracy of the extreme value of probability. The continuous monitoring data from a real bridge is used for validation. The modeling and predicting results verified the proposed methods' strong applicability and high accuracy for complex probabilistic system input and output characteristics from in-service structures.
Author	Guo, Tong Ding, Youliang Zhang, Xiaonan Yi, Letian Zhao, Hanwei Zou, Yang Li, Aiqun
Author_xml	– sequence: 1 givenname: Xiaonan orcidid: 0009-0006-1504-880X surname: Zhang fullname: Zhang, Xiaonan organization: Key Laboratory of Concrete and Pre-stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, China – sequence: 2 givenname: Youliang orcidid: 0000-0002-0774-426X surname: Ding fullname: Ding, Youliang organization: Key Laboratory of Concrete and Pre-stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, China – sequence: 3 givenname: Hanwei orcidid: 0000-0002-7622-7784 surname: Zhao fullname: Zhao, Hanwei organization: Key Laboratory of Concrete and Pre-stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, China – sequence: 4 givenname: Letian orcidid: 0000-0002-4260-6911 surname: Yi fullname: Yi, Letian organization: Internet of Things Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China – sequence: 5 givenname: Tong orcidid: 0000-0001-9228-4941 surname: Guo fullname: Guo, Tong organization: Key Laboratory of Concrete and Pre-stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, China – sequence: 6 givenname: Aiqun orcidid: 0000-0003-3565-3725 surname: Li fullname: Li, Aiqun organization: Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, China – sequence: 7 givenname: Yang orcidid: 0000-0001-6150-6126 surname: Zou fullname: Zou, Yang organization: Department of Civil and Environmental Engineering, The University of Auckland, Auckland, New Zealand
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References	ref12 ref15 ref14 ref10 ref17 ref16 ref19 ref18 ref46 ref45 ref47 ref42 ref41 ref44 ref43 (ref48) 2015 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref35 Stoneking (ref40) 2014 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 Guo (ref13) 2008; 38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 Zhao (ref11) 2018; 5 ref28 ref27 ref29
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SubjectTerms	Accuracy Bayes methods Bayesian analysis Bridges Digital twin Digital twins Extreme value prediction Extreme values Intelligent monitoring Load modeling Maximum likelihood estimation Mixed probability modeling Modelling Parameterization Parameterization of dynamic system Predictive models Probabilistic logic Probabilistic models Probability theory Risk assessment Skewness Statistical analysis Transportation infrastructures Vehicle dynamics
Title	Mixed Skewness Probability Modeling and Extreme Value Predicting for Physical System Input/Output Based on Full Bayesian Generalized Maximum-Likelihood Estimation
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