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Electromechanical impedance-based (EMI) techniques using piezoelectric transducers are promising for structural damage identification. They can be implemented in high frequency range with small characteristic wavelengths, leading to high detection sensitivity. The impedance measured is the outcome of harmonic and stationary excitation, which makes it easier to conduct inverse analysis for damage localization and quantification. Nevertheless, the EMI data measurement points are usually limited, thus oftentimes resulting in an under-determined problem. To address this issue, damage identification process can be converted into a multi-objective optimization formulation which naturally yields multiple solutions. While this setup fits the nature of damage identification that a number of possibilities may exist under given observations/measurements, existing algorithms may suffer from premature convergence and entrapment in local extremes. Consequently, the solutions found may not cover the true damage scenario. To tackle these challenges, in this research, a series of local search strategies are tailored to enhance the global searching ability and incorporated into particle swarm-based optimization. The Q-table is utilized to help the algorithm select proper local search strategy based on the maximum Q-table values. Case studies are carried out for verification, and the results show that the proposed memetic algorithm achieves good performance in damage identification.

 

Piezoelectric transducers are widely employed in vibration control and energy harvesting. The effective electro-mechanical coupling of a piezoelectric system is related to the inherent capacitance of the piezoelectric transducer. It is known that passive vibration suppression through piezoelectric LC shunt can be enhanced with the integration of negative capacitance which however requires a power supply. This research focuses on the parametric investigation of a self-sustainable negative capacitance where the piezoelectric transducer is concurrently used in both vibration suppression and energy harvesting through LC shunt. The basic idea is to utilize the energy harvesting functionality of the piezoelectric transducer to aid the usage of negative capacitance in terms of power supply. Specifically, the power consumption and circuitry performance with respect to negative capacitance circuit design is analyzed thoroughly. Indeed, the net power generation is the difference between available power in the shunt circuit and the power consumption of the negative capacitance circuit. There exists complex tradeoffs between net power generation and the vibration suppression performance when we use different resistance values in the negative capacitance circuit. It is demonstrated through correlated analytical simulation and experimental study that the proper selection of the resistance values in the negative capacitance circuit can result in vibration suppression enhancement as well as improved net power generation, leading to a self-sustainable negative capacitance scheme.

 

Tunable piezoelectric metasurfaces have been proposed as a means of adaptively steering incident elastic waves for various applications in vibration mitigation and control. Bonding piezoelectric material to thin structures introduces electromechanical coupling, enabling structural dynamics to be altered via tunable electric shunts connected across each unit cell. For example, by carefully calibrating the inductive shunts, it is possible to implement the discrete phase shifts necessary for gradient-based waveguiding behaviors. However, experimental validations of localized phase shifting are challenging due to the narrow bandgap of local resonators, resulting in poor transmission of incident waves and high sensitivity to transient noise. These factors, in combination with the difficulties in experimental circuitry synthesis, can lead to significant variability of data acquired within the bandgap operating region. This paper presents a systematic approach for extracting localized phase shifts by taking advantage of the inherent correlation between the incident and transmitted wavefronts. During this procedure, matched filtering greatly reduces noise in the transmitted signal when operating in or near bandgap frequencies. Experimental results demonstrate phase shifts as large as −170° within the locally resonant bandgap, with an average 28% reduction in error relative to a direct time domain measurement of phase, enabling effective comparison of the dispersive behavior with corresponding analytical and finite element models. In addition to demonstrating the tunable waveguide characteristics of a piezoelectric metasurface, this technique can easily be extended to validate localized phase shifting of other elastic waveguiding metasurfaces.

 

Piezoelectric impedance sensing is promising for highly accurate damage identification because of its high-frequency active interrogative nature and simplicity in data acquisition. To fully unleash the potential, effective inverse analysis is needed in order to pinpoint the damage location and identify the severity. The inverse analysis, however, may be underdetermined since there exists a very large number of unknowns (i.e., locations and severity levels) to be solved in a finite element model but only limited measurements are available in actual practice. To uncover the true damage scenario, an inverse analysis strategy built upon the multi-objective optimization, which aims at matching the multiple sets of measurements with model predictions in the damage parametric space, can be formulated to identify a small set of solutions. This solution set then allows the incorporation of empirical knowledge to facilitate final decision-making. The main disadvantage of the conventional inverse analysis strategy is that it overlooks uncertainties that exist in both baseline structural modeling and actual measurements. To address this, in this research, we formulate a probabilistic multi-objective optimization-based inverse analysis framework, which is fundamentally built upon the differential evolution Markov chain Monte Carlo (DEMC) technique. The new approach can yield the Pareto optimal set (solutions) and the respective Pareto front, which are represented in a probabilistic sense to account for uncertainties. Comprehensive case studies with experimental investigations are conducted to demonstrate the effectiveness of this new approach.