Numerous damage detection methods that use data obtained from contact sensors, physically attached to structures and human inspection methods have been developed. However, damage sensitive features used for these methods such as modal properties of steel and reinforced concrete structures are sensitive to environmental conditions such as temperature and humidity. Besides, human inspection is cost, labor extensive, and is controlled by the technical understanding of an individual. The

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... es of the contact sensor methods are difficult to address with a regression model or any other temperature compensation method, and these are primary causes of false alarms. In order to address some of these challenges of the traditional sensing system, a vision-based remote sensing system can be one of the alternatives as it gives the explicit intuitions of structural conditions. In addition, bolted connections are common engineering practices, and very few vision-based techniques are developed for loosened bolt detection. Thus, this thesis proposes an automated vision-based method for detecting loosened structural bolts using the Viola-Jones algorithm and support vector machines. The test images of bolt connections are taken with a digital single lens reflex camera. The Viola-Jones algorithm is trained on two datasets of images with and without bolts. The trained algorithm localizes and crops all the bolts on test images. The SVM is trained on another dataset of loose and tight bolts to generate decision boundary for classification of the loosened and tight bolts. The cropped bolt images are binarized to calculate bolt features as head dimensions and exposed shank length. The extracted features are fed into a trained support vector machine to classify the loosened and tight bolts. We test our method on images taken by digital single lens reflex and smartphone cameras. iii A special feeling of gratitude to my loving parents. Their words of encouragement, support, and prayers helped me to come this far. iv Acknowledgements