Bayesian Intelligent Structural Health Monitoring System: Theory and Implementation

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Status	已發表Published
	Bayesian Intelligent Structural Health Monitoring System: Theory and Implementation
	Kuok, S.C.; Yuen, K. V.
	2013-06-01
Source Publication	Proceedings of HKIE Control, Automation and Instrumentation Division
Pages	85-93
Publication Place	Hong Kong
Publisher	City University of Hong Kong Press
Abstract	The objective of this paper is to demonstrate the implementation of intelligent structural health monitoring system (ISHMS) from a Bayesian probabilistic perspective. ISHMS is to integrate instrumentation technologies and data processing methodologies for structural health assessment and damage diagnosis. Reliable ISHMS provides the baseline for decisions on preventive and corrective maintenance. Bayesian inference offers the feasibility for ISHMS because it provides a rigorous framework for parametric identification and uncertainty quantification. Taking such advantage, this paper presents a Bayesian probabilistic framework for ISHMS. In-field measurement of an SHM project of a residential reinforced concrete building is utilized to demonstrate the efficacy and applicability of the Bayesian ISHMS. The structural behavior under severe wind loading and various ambient conditions are examined. These successful applications indicate the enormous potential of the Bayesian probabilistic framework for the development of a reliable ISHMS.
Keyword	Bayesian inference structural health monitoring ambient
Language	英語English
The Source to Article	PB_Publication
PUB ID	9572
Document Type	Conference paper
Collection	GRADUATE SCHOOL DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Corresponding Author	Yuen, K. V.
Recommended Citation GB/T 7714	Kuok, S.C.,Yuen, K. V.. Bayesian Intelligent Structural Health Monitoring System: Theory and Implementation[C], Hong Kong:City University of Hong Kong Press, 2013, 85-93.
APA	Kuok, S.C.., & Yuen, K. V. (2013). Bayesian Intelligent Structural Health Monitoring System: Theory and Implementation. Proceedings of HKIE Control, Automation and Instrumentation Division, 85-93.

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