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原文传递 Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data

题名：	Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data
其他题名：	Behmanesh,I.,and B.Moaveni.2015."Probabilistic identification of simulated damage on the Dowling Hall footbridge through Bayesian finite element model updating."Struct.Control Health Monit.22(3):463-483.https://doi.org/10.1002/stc.1684.
正文语种：	英文
作者：	Hua-Ping Wan
关键词：	Stress forecast;Bayesian modeling;Gaussian processes;Moving window;Structural health monitoring;Supertall structure
摘要：	The advancement in structural health monitoring (SHM) technology has been evolving from monitoring-based diagnosis to monitoring-based prognosis. The structural stress response derived by the measured strain data is increasingly used for structural condition diagnosis and prognosis because it can be directly used to indicate the safety reserve of a structural component or provide information regarding the load-carrying capacity of the whole structure. Therefore, accurate forecasting of structural stress responses is an essential step for the reliable diagnosis and prognosis of structural condition. For a large-scale, complex structure subjected to multisource effects such as live loads and environmental loads, its stress evolution is a typically nonlinear dynamic process. Moreover, the online monitoringderived stress data extracted from an SHM system are extremely massive. This arouses a strong demand for developing a computationally efficient and accurate method for forecasting structural stress responses. In this work, we propose the use of a Bayesian modeling approach with Gaussian processes (GPs), which allows for probabilistic forecasts of structural stress responses and has great capability of modeling the underlying nonlinear dynamic process. Although powerful for characterizing dynamic nonlinearity of structural stress responses, the conventional GP-based Bayesian modeling approach remains computationally intensive because of the massive stress data increasingly gathered by the monitoring system.We propose a moving window strategy to substantially shrink the size of training data, thus leading to a reduced-order GP model and effectively alleviating the high computational cost. The feasibility of the reduced-order GP-based Bayesian modeling approach is illustrated by using the real-time monitoring-derived stress data acquired from a supertall structure. Its performance is compared with the full GP-based Bayesian approach, and the comparison results indicate that the proposed approach holds higher computational accuracy and efficiency for stress response forecasts than the traditional method.
出版年：	2018
论文唯一标识：	P-26Y2018V144N09003
英文栏目名称：	TECHNICAL PAPERS
doi：	10.1061/(ASCE)ST.1943-541X.0002085
期刊名称：	Journal of Structural Engineering
拼音刊名(出版物代码)：	P-26
卷：	144
期：	09
页码：	3-14