MCE Ph.D. Thesis Seminar
Gates-Thomas 135
Computational modeling of the mechanics of elastic structural lattices: effects of lattice architecture and hierarchy
Pinaky Bhattacharyya,
Optimal Sensor Placement for Bayesian Parametric Identiļ¬cation of Structures,
Mechanical and Civil Engineering,
California Institute of Technology,
Abstract: There exists a choice in where to place sensors to collect data for Bayesian model updating of structures. It is desirable to use an available deterministic predictive model, such as a finite-element model, along with prior information on the uncertain parameters, to determine which optimal sensor locations should be instrumented in the structure.
The mutual information between the uncertain model predictions for the data and the uncertain model parameters is presented as a natural measure of reduction in uncertainty to maximize over sensor configurations. A combinatorial search over all sensor configurations is usually prohibitively expensive. A convex optimization method is developed to provide a fast sub-optimal, but possibly optimal, sensor configuration when certain simplifying assumptions can be made about the chosen stochastic model class for the structure. The optimization method is demonstrated to work for a 50-story uniform shear building, with 20 sensors to be installed. The stability of optimal sensor configurations under refinement of the mesh of the underlying finite-element model is investigated and related to the choice of prediction-error correlations in the model.
In order to solve the optimal sensor placement problem in the more general case, numerical estimation of mutual information between the model predictions for the data and the model parameters becomes necessary. To this end, a thermodynamic integration scheme based on path sampling is developed with the aim of estimating the entropy of the data prediction distribution.
Event Series
Mechanical and Civil Engineering Seminar Series
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