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Conventionally, the assessment of reinforced concrete shear walls relies on manual visual assessment which is time-consuming and depends heavily on the skills of the inspectors. The development of automated assessment employing flying and crawling robots equipped with high-resolution cameras and wireless communications to acquire digital images and advance image processing to extract crack patterns has paved the path toward implementing an automated system which determines structural damage based on visual signals acquired from structures. Since there are few, if any, studies to correlate crack patterns to structural integrity, this article proposes to analyze crack patterns using a multifractal analysis. The approach is initially tested on synthetic crack patterns, and then it is applied to a set of experimental data collected during the testing of two large-scale reinforced concrete shear wall subjected to controlled reversed cyclic loading. The structural response data available for each specimen are used to link the multifractal parameters with the structural performance of the two specimens. A relationship between the multifractal parameters and the crack patterns’ evolution and mechanism is noted. The results show that as the crack patterns extend and grow, multifractal parameters move toward higher values. The parameters jump as the mechanical response shows severe stiffness loss. In this study, no attempt is made to automate the process of mapping cracks from images.
Ebrahimkhanlou et al. (Fri,) studied this question.