Fast and slow dynamics in the elastic response of damaged materials to external excitations show evidence of an anomalous (hysteretic) elastic behavior. Experimental observations may be used to detect and eventually characterize the damaged state of structural components. At the same time, reliable theoretical models might support data interpretation and development of new experiments. Here, we analyze the resonance frequency shift in undamaged and damaged concrete bars, comparing experimental results and theoretical predictions obtained from a Preisach-Mayergoyz based elastic model.

In this work quantitative results of applying nonlinear acoustic dynamics to study progressive damage in a polymer-based composite SMC (sheet molding compound) are presented. Via carefully controlled resonant plate experiment, nonlinear slow dynamics (SNLD) response of SMC in terms of relaxation time and frequency shift has been shown to be very sensitive to gradual damage induced using three-point bending tests. Besides, acoustic emission monitoring is used to characterize damage through the elastic energy released by SMC at every damage step. Interesting logarithmlike changes of the SNLD parameters as a function of the acoustic emission cumulated energy are found.

Acoustic Emission (AE) is revealed to be highly adapted to monitor materials and structures in materials research and for site monitoring. AE-features can be either analyzed by means of physical considerations (geophysics/seismology) or through their time/frequency waveform characteristics. However, the multitude of definitions related to the different parameters as well as the processing methods makes it necessary to develop a comparative analysis in the case of a heterogeneous material such as civil engineering concrete. This paper aimed to study the micro-cracking behavior of steel fiber-reinforced reinforced concrete T-beams subjected to mechanical tests. For this purpose, four-points bending tests, carried out at different displacement velocities, were performed in the presence of an acoustic emission sensors network. Besides, a comparison between the sensitivity to damage of three definitions corresponding to the b-value parameter was performed and completed by the evolution of the RA-value and average frequency (AF) as a function of loading time. This work also discussed the use of the support-vector machine (SVM) approach to define different damage zones in the load-displacement curve. This work shows the limits of this approach and proposes the use of an unsupervised learning approach to cluster AE data according to physical and time/frequency parameters. The paper ends with a conclusion on the advantages and limitations of the different methods and parameters used in connection with the micro/macro tensile and shear mechanisms involved in concrete cracking for the purpose of in situ monitoring of concrete structures.

This work presents a procedure for investigating local damages created in sandwich composites. The proposed analysis is based on the processing of the recorded acoustic emission hits. Unsupervised pattern recognition analyses (k-means algorithm) associated to Principal Component Analysis (PCA) are the tools used for the classification of the detected acoustic emission events. A cluster analysis of acoustic emission data is achieved and the resulting clusters are correlated to the created damage mechanisms within the sandwich composite. The study is validated on the constituents of the sandwich material (resin, polyvinyl chloride foam and skins) taken separately before investigating the sandwich composite during a three-point bending test. In addition to the advantages given by the use of the multivariable data analysis, results show the impact of the transducers positioning on the analysis of the involved damage mechanisms. An optimized positioning is proposed in order to improve the detection of the acoustic emission activity.