Merging experimental design and structural identification around the concept of modified Constitutive Relation Error in low-frequency dynamics for enhanced structural monitoring

This project looks at a new way to do structural health monitoring (SHM). It combines experimental design with structural identification using the modified Constitutive Relation Error (mCRE) framework for low‑frequency dynamics. Typical model‑updating methods rely on many sensors, but in practice sensor networks are often sparse. That sparsity makes it hard to detect damage reliably. To overcome this, the research develops an optimal sensor placement (OSP) strategy specifically for mCRE‑based model updating. By linking mCRE to Bayesian inference, the method builds a modified Fisher information matrix that uses the idea of information entropy. This matrix lets us choose sensor locations that give the most information while keeping the system less

sensitive to measurement noise. The OSP approach is tested with numerical simulations of a structural model inspired by earthquake engineering. The project also provides step‑by‑step guidance on how to implement the mCRE functional, set up the optimization algorithms, and check how robust the sensor layout is when noise levels change. In the end, the work supports the creation of reliable, sparse sensor networks for identifying structures and locating damage in dynamic engineering systems.

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