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Probabilistic framework for assessing maximum structural response based on sensor measurements
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DOI：10.1016/j.strusafe.2016.03.003.png)
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Abstract
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A probabilistic framework for Bayesian inference combined with extreme values of Gaussian processes is proposed to assess the maximum of the response of an uncertain structure instrumented with sensors and subject to a stochastic load. The framework is applied to the analysis of the inter-story drift of a multi-story shear-type building under seismic hazard using measurements collected by accelerometers. A cascade of two dynamic systems is proposed to model the stochastic ground motion and the response of the structure. We present an approximate analytical solution to estimate the distribution of the maximum response, and verify the accuracy and limitations of this solution against simulation results. Finally, robustness of the proposed framework to system uncertainties, including uncertainties in the structural characteristics, ground characteristics, and input motion parameters, is investigated. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Probabilistic inference and estimation
Extreme value analysis
Dynamic Bayesian Network
Kalman smoother
Seismic loading
Structural health monitoring
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