This research work is in the area of structural health monitoring and structural damage mitigation. It advances the method of parameter identification of structures with significant nonlinear response dynamics. The method integrates a nonlinear hybrid parameter multi-body dynamic system (HPMBS) modeling technique with a parameter identification scheme based on a polynomial interpolated Taylor series (PITS) methodology. This work advances the model based structural health monitoring state-of-the-art, by providing a tool to accurately estimate damaged structure parameters through significant nonlinear damage. The significant nonlinear damage implied includes effects from loose bolted joints, dry frictional damping, large articulated motions, etc. Note that currently most damage detection algorithms in structures are based on finding changed stiffness parameters and generally do not address other parameters such as mass, length, damping and joint gaps. The scope of work is the extension of damage detection practice from linear structure to nonlinear structures in civil and aerospace applications. To experimentally validate the developed methodology, we have built a nonlinear HPMBS structure. This structure is used as a test-bed to fine-tune the modeling and parameter identification algorithms. Also, it can be used to simulate bolted joints in aircraft wings, expansion joints of bridges, or the interlocking structures in a space frame. The developed technique has the ability to identify unique damage, such as systematic isolated and noise induced damage that exists in group members and isolated elements. Here, not just the damage parameters such as Young’s modulus are identified, but other structural parameters, such as distributed mass, damping and friction coefficients, can also be identified.

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