Intelligent Image Correlation Using Genetic Algorithms for Measuring Surface Displacements and Strain Profiles

Abstract

This paper presents an intelligent digital image correlation technique that uses genetic algorithms to estimate surface displacements and strains. Speckle patterns are spray painted on the surface of interest and pictures taken before and during loading. Subpixel resolution, required for measuring displacements and strains accurately, is obtained by using interpolation methods. An innovative procedure based on genetic algorithms (GAs) is used that directly gives two displacements and four deformation gradients for a subset being investigated. The genetic algorithms guarantee a solution based on an innovative calibration procedure. The focus of this paper is the description of the GA routine used for the search process as well as the calibration scheme. Results are presented for measuring rigid-body displacement and uniform normal strain as proof of concept. Some potential applications for this work are to extract surface displacements and strains on aircraft, spacecraft and reusable launch vehicles, submarine and ship hulls, civil infrastructures such as bridges, dams and buildings, and biomedical imaging applications.