This paper presents an investigation of the applicability of a Bayesian system identification theory for localizing damage in plate-like structures, while considering the uncertainties from modeling and measurement. Diagnostic Lamb waves are excited and received by a piezoelectric sensor network before and after damage to obtain scattered waves that contain characteristic information about the damage. After the time-of-flight (ToF) of the scattered waves in each actuator–sensor path is measured by a continuous wavelet transform (CWT), a Bayesian approach is developed to identify the damage location and wave velocity. By combining the prior information and the measured ToF data, Bayes’ theorem is used to update the probability distributions of the parameters about the damage location and wave velocity. In particular, a Markov chain Monte Carlo (MCMC) method is employed for sampling the posterior distributions of the unknown parameters. A numerical study for an aluminum plate and experimental studies for a stiffened aluminum panel and a composite laminate are conducted to validate the proposed Bayesian damage localization approach.

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In structural health monitoring of composite structures, one important task is to detect and identify the low-velocity impact events, which may cause invisible internal damages. This paper presents a novel approach for simultaneously identifying the impact location and reconstructing the impact force time history acting on a composite structure using dynamic measurements recorded by a sensor network. The proposed approach consists of two parts: (1) an inner loop to reconstruct the impact force time history and (2) an outer loop to search for the impact location. In the inner loop, a newly developed inverse analysis method with Bayesian inference regularization is employed to solve the ill-posed impact force reconstruction problem using a state-space model. In the outer loop, a nonlinear unscented Kalman filter (UKF) method is used to recursively estimate the impact location by minimizing the error between the measurements and the predicted responses. The newly proposed impact load identification approach is illustrated by numerical examples performed on a composite plate. Results have demonstrated the effectiveness and applicability of the proposed approach to impact load identification.

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