The objective of this project is to effectively combine the qualities of different sensor types of a dynamic monitoring network to capitalize on the intrinsic redundancies of the measured data to identify the structural model parameters. Currently there is increasing activity in the area of structural health monitoring using newly emerging, dynamic sensor technologies. There is, however, no clear framework to best combine these heterogeneous measurement quantities for health monitoring purposes. In this project, this dual parameter and state estimation problem with different types of sensor measurements is formulated as a nonlinear estimation problem. In this study, the challenges that will be addressed in dealing with this nonlinear dual state and parameter estimation problem are: 1) the implementation of the approach to large structural problems with many unmeasured states and parameters to be identified and 2) determining the required sensor configurations and resolution to ensure "observability" such that the measured quantities are, indeed, useful and usable for this nonlinear estimation problem. The theoretical developments and the proposed identification approach will be experimentally validated with the laboratory model of a building structure and also with a leveraged data set from a major long-span bridge collected by the principal investigator.
This study is expected to provide a validated approach to maximize the return on the use of the heterogeneous sensor networks and an important practical tool to the structural engineering community for better health monitoring, management and maintenance of critical civil infrastructure system with improved life safety. The PI has an industry/agency outreach plan, and will rapidly introduce the dual state-parameter estimation concepts in a graduate course under development. The project will also provide advanced training to graduate and undergraduate students through their direct involvements in this project.
