The need for low order models capable of performing damage identification has become apparent in many structural dynamics applications where structural health monitoring and damage prognosis programs are implemented. These programs require that damage identification routines have low computational requirements and be reliable with some quantifiable degree of accuracy. Response surface metamodels (RSMs) are proposed to fill this need. Popular in the fields of chemical and industrial engineering, RSMs have only recently been applied in the field of structural dynamics and to date there have been no studies which fully demonstrate the potential of these methods. In this thesis, several RSMs are developed in order to demonstrate the potential of the methodology. They are shown to be robust to noise (experimental variability) and have success in solving the damage identification problem, both locating and quantifying damage with some degree of accuracy, for both linear and nonlinear systems. A very important characteristic of the RSMs developed in this thesis is that they require very little information about the system in order to generate relationships between damage indicators and measureable system responses for both linear and nonlinear structures. As such, the potential of these methods for damage identification has been demonstrated and it is recommended that these methods be developed further. / Master of Science

Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) technologies aim at improving the availability, reliability, maintainability, and safety of systems through the development of fault diagnostic and failure prognostic algorithms. In complex engineering systems, such as aircraft, power plants, etc., the prognostic activities have been limited to the component-level, primarily due to the complexity of large-scale engineering systems. However, the output of these prognostic algorithms can be practically useful for the system managers, operators, or maintenance personnel, only if it helps them in making decisions, which are based on system-level parameters. Therefore, there is an emerging need to build health assessment methodologies at the system-level. This research employs techniques from the field of design-of-experiments to build response surface metamodels at the system-level that are built on the foundations provided by component-level damage models.