Neural Networks for Inverse Problems in Damage Identification and Optical Imaging

• Published in: AIAA journal 2003-04, Vol.41 (4), p.732-740
• Main Authors: Kim, Yong Y, Kapania, Rakesh K
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Computer science; control theory; systems; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Image processing systems; Investigative techniques, diagnostic techniques (general aspects); Learning and adaptive systems; Measurement and testing methods; Measurement methods and techniques in continuum mechanics of solids; Medical sciences; Miscellaneous. Technology; Neural networks; Optics; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Physics; Solid mechanics; Structural and continuum mechanics
• ISSN: 0001-1452; 1533-385X
• DOI: 10.2514/2.2004

Artificial neural networks (ANNs) are employed in solving inverse problems in damage detection in structures, as well as detection, using optical imaging, of an anomaly in a light-diffusive media, such as a human tissue. Both of these problems, namely, identifying the damage parameters in a damaged structure and identifying the representative properties in a tissue, require solving highly complex inverse problems. The neural networks (NNs) for both problems are similar, and a method found suitable for solving one type of problem can be applied for solving the other type of problem. In the damage identification problem, the natural frequencies of a damaged beam model obtained from analytical and numerical methods were used to identify damage parameters by employing feedforward backpropagation, and also radial basis NNs. In the optical imaging problem, the tissue under investigation was illuminated by a number of near-infrared light sources placed around the circumference of the tissue. Both the location and the size of the anomaly were identified by studying the influence of the anomaly on the light intensity received at the boundary of the tissue. The near-infrared light measurements are assumed to be available at a number of light detector positions, also along the circumference of the tissue. NNs were used to determine the location and the size of the anomaly in a tissue. The direct problem for the case of optical imaging was solved using the finite element method to generate the training and testing sets for NNs.