Structural Health Monitoring Sensor Network Optimization through Bayesian Experimental Design
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4, Issue 2
Abstract
Structural health monitoring (SHM) may be exploited to estimate the mechanical properties of existing structures and check for potential damage. Among commonly used methodologies for property characterization, the Bayesian approach holds the lead because it is endowed with the particular advantage of quantifying associated uncertainties. These uncertainties arise owing to diverse factors including (1) sensor accuracy and positioning, (2) environmental influences, and (3) modeling errors. In minimizing the influence of sensor-related uncertainties, an optimal design may be adopted for the SHM campaign to maximize the information content of the measurements. Here, a procedure based on Bayesian experimental design is proposed to quantify the expected utility of the sensor network. The positions of the used sensors are selected in a way that maximizes the Shannon information gain between the prior and posterior probability distributions of the parameters to be estimated. In order to numerically solve the resulting optimization problem, surrogate models based on polynomial chaos expansion (PCE) and stochastic optimization methods are used. The use of surrogates allows one to reduce the computational cost of the associated model runs. The method is applied to a large-scale example, namely the Pirelli Tower in Milan.
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Acknowledgments
The first author acknowledges the financial support by IDEA League through a Ph.D. Student Grant. The authors also acknowledge the Chair of Risk, Safety and Uncertainty Quantification and the Computational Science and Engineering Laboratory at ETH Zürich for having provided the MATLAB-based softwares UQLab and CMA-ES, used in the implementation of the method. Authors are indebted to Gianluca Barbella and Federico Perotti, who provided the numerical model of the Pirelli Tower.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4 • Issue 2 • June 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Jun 9, 2017
Accepted: Nov 22, 2017
Published online: Mar 30, 2018
Published in print: Jun 1, 2018
Discussion open until: Aug 30, 2018
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