Piezoelectric Impedance-Based Structural Damage Identification Empowered With Tunable Circuitry Integration and Multi-Objective Optimization-Based Inverse Analysis

The piezoelectric impedance-based technique is increasingly recognized for its promise in structural health monitoring and damage identification. Built upon their self-sensing actuation capability, piezoelectric transducers can be integrated into host structures to acquire the system-level impedance information in a high-frequency range with a small wavelength. Furthermore, the frequency-sweeping harmonic excitations in impedance measurements lead to the potential for model-based inverse identification of damage location and severity. A major challenge in damage identification, however, is that the inverse analysis is generally underdetermined, as the measurement information may not be adequate to yield a unique solution. In this research, a new methodology of tunable sensing in conjunction with multi-objective optimization inverse analysis is established. Taking advantage of the two-way electromechanical coupling of piezoelectric transducers, tunable inductance is integrated into the measurement circuit. For the same damage scenario, by tuning the inductance to a series of values, a family of impedance measurements can be acquired. Meanwhile, the inverse analysis is cast into a multi-objective optimization problem, aiming at minimizing the difference between measurement and model prediction and achieving sparsity in damage index vector. A Q-learning-based multi-objective particle swarm optimization is synthesized to reach a small yet diverse solution set. We report the circuitry integration details as well as the algorithm enhancement with systematic case investigations. It is validated that the new methodology with enriched measurement can produce a smaller solution set encompassing the true damage scenario, thereby providing vital information for diagnoses and prognosis.