Skip over navigation


Characterization and Imaging
Structural and Material Imperfections

Maria Chierichetti Worcester Polytechnique Institute 508-831-6595

Stefano Gonella, University of Minnesota

(Mechanics of Solids and Structures)

The mechanical behavior of materials is highly influenced by the presence of imperfections and anomalies, such as inhomogeneity, inclusions, cracks, fabrication defects, regions of degraded material properties. Structural anomalies encompass inherent features of a solid as well as conditions resulting from abrupt morphological changes and failure, and span across multiple spatial scales. Due to their presence, the material behavior departs from nominal conditions. Their detection and characterization is therefore fundamental to acquire knowledge on the overall behavior of the assembly. The key to the detection of anomalies can be found in the identification, classification and interpretation of the features in dynamic response of the medium. In addition, imperfections can be exploited to achieve enhanced mechanical properties in novel designs.

Techniques traditionally developed for damage detection and localization could be further revisited by rethinking them as tools for structural imaging and characterization of materials with different degrees of complexity in the presence of anomalies and imperfections.

We welcome contributions in the area of anomaly detection and characterization in all classes of materials at multiple scales. In particular, we are looking for contributions that couple traditional mechanics-based approaches with recent developments in imaging techniques. We are particularly interested in the following areas:

  • Anomaly detection at different scales (from MEMS to large civil structures) of homogeneous and heterogeneous (micro-structured, porous, etc.) media.
  • Non-destructive inspection techniques for thin structures, such as guided-waves ultrasonics.
  • Online baseline-free structural health monitoring techniques.
  • Inverse problems for defect reconstruction and structural identification based on remote sensing of the operational response.
  • Distributed detection and decision making of structural events through data mining and machine learning approaches.
  • Supervised and unsupervised learning techniques for system characterization
  • Medical imaging and techniques for detection of abnormalities in biological tissues.
  • Design of structures based on the exploitation of inclusions and defects.


Submit an abstract