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The deep mounting of CFRP strips to masonry using a flexible adhesive was developed as a minimally-invasive and cost-effective out-of-plane seismic retrofitting technique for unreinforced masonry (URM) buildings. Previous experiments on one-way spanning full-scale walls confirmed the significant added value for out-of-plane lateral resistance and displacement capacity for the Flexible Deep Mounted (FDM) CFRP strips retrofitted walls with respect to unreinforced masonry. This out-of-plane performance could be further enhanced by the addition of a single-sided Fabric Reinforced Cementitious Matrix (FRCM) overlay to FDM CFRP strips retrofitted walls. The purpose of the current experimental campaign was primarily to investigate the out-of-plane performance of one-way spanning walls retrofitted with both FDM CFRP strip and a single-sided FRCM overlay. The results of the experiments confirmed the significant added value for out-of-plane lateral resistance of walls with the proposed retrofit combination with respect to the URM and solely FDM CFRP retrofitted walls. A cross section analysis using non-linear and linear material models for the used components was proposed. The model provided an overestimation of the lateral moment capacity for walls that were solely retrofitted with FDM CFRP strips for high mid-span displacement. For walls that were additionally reinforced with a single-sided FRCM overlay, the model provided a good approximation of both the internal moment versus curvature and the lateral moment versus mid span displacement relationships.

The deep mounting of CFRP strips to masonry using a flexible adhesive was developed as a minimally-invasive and cost-effective out-of-plane seismic retrofitting technique for unreinforced masonry (URM) buildings. Previous experiments on one-way spanning full-scale walls confirmed the significant added value for out-of-plane lateral resistance and displacement capacity for the Flexible Deep Mounted (FDM) CFRP strips retrofitted walls with respect to unreinforced masonry. This out-of-plane performance could be further enhanced by the addition of a single-sided Fabric Reinforced Cementitious Matrix (FRCM) overlay to FDM CFRP strips retrofitted walls. The purpose of the current experimental campaign was primarily to investigate the out-of-plane performance of one-way spanning walls retrofitted with both FDM CFRP strip and a single-sided FRCM overlay. The results of the experiments confirmed the significant added value for out-of-plane lateral resistance of walls with the proposed retrofit combination with respect to the URM and solely FDM CFRP retrofitted walls. A cross section analysis using non-linear and linear material models for the used components was proposed. The model provided an overestimation of the lateral moment capacity for walls that were solely retrofitted with FDM CFRP strips for high mid-span displacement. For walls that were additionally reinforced with a single-sided FRCM overlay, the model provided a good approximation of both the internal moment versus curvature and the lateral moment versus mid span displacement relationships.

The in-plane shear behaviour of a new seismic retrofit concept which combines two standalone retrofit measures for in-plane and out-of-plane strengthening of masonry walls was investigated. The in-plane reinforcement consists of a single-sided carbon fabric-reinforced cementitious matrix (FRCM) overlay, while the out-of-plane reinforcement consists of deep mounted carbon fibre reinforced polymer strips embedded in a viscous-elastic epoxy. An experimental program was undertaken in which clay brick masonry wallettes were subjected to the diagonal compression test to assess the effectiveness of the strengthening system on the in-plane behaviour. The obtained results showed that the single-sided carbon FRCM overlay increased the shear capacity with 80%, compared to the unstrengthened control specimens. Moreover, by testing two different FRCM overlay thicknesses it was found that a thicker matrix layer does not increase the shear capacity of wallettes. However, wallettes provided with a thicker FRCM overlay did show a higher level of ductility. Furthermore, the obtained experimental results showed that the presence of only the aforementioned out-of-plane reinforcement does not affect the in-plane strength of masonry wallettes loaded under shear, and even prevented the disintegration after reaching the failure load compared to the unstrengthened control specimens. Finally, an existing analytical model as well as the Eurocode 8 design provisions were compared to the found failure mechanisms and failure loads. The analytical model developed showed good correspondence with the experimental values for both the failure mechanism and failure load, with an experimental/model ratio (φ) of 0.98, while Eurocode 8 proved to lead to conservative values.

Assigning a label to each pixel in an image, namely semantic segmentation, has been an important task in computer vision, and has applications in autonomous driving, robotic navigation, localization, and scene understanding. Fully convolutional neural networks have proved to be a successful solution for the task over the years but most of the work being done focuses primarily on accuracy. In this paper, we present a computationally efficient approach to semantic segmentation, while achieving a high mean intersection over union (mIOU), 70.33% on Cityscapes challenge. The network proposed is capable of running real-time on mobile devices. In addition, we make our code and model weights publicly available.

Semantic image segmentation, the task of assigning a label to each pixel in an image, is a major challenge in the field of computer vision. Semantic image segmentation using fully convolutional neural networks (FCNNs) offers an online solution to the scene understanding while having a simple training procedure and fast inference speed if designed efficiently. The semantic information provided by the semantic segmentation is a detailed understanding of the current context and this scene understanding is vital for scene modification in augmented reality (AR), especially if one aims to perform destructive scene augmentation. Augmented reality systems, by nature, aim to have a real-time modification of the context through head-mounted see-through or video-see-through displays, thus require efficiency in each step. Although there are many solutions to the semantic image segmentation in the literature such as DeeplabV3+, Deeplab DPC, they fail to offer a low latency inference due to their complex architectures in aim to acquire the best accuracy. As a part of this thesis work, we provide an efficient architecture for semantic image segmentation using an FCNN model and achieve real-time performance on smartphones at 19.65 frames per second (fps) while maintaining a high mean intersection over union (mIOU) of 67.7% on Cityscapes validation set with our "Basic" variant and 15.41 fps and 70.3% mIOU on Cityscapes test set using our "DPC" variant. The implementation is open-sourced and compatible with Tensorflow Lite, thus able to run on embedded and mobile devices. Furthermore, the thesis work demonstrates an augmented reality implementation where semantic segmentation masks are tracked online in a 3D environment using Google ARCore. We show that the frequent calculation of semantic information is not necessary and by tracking the calculated semantic information in 3D space using inertial-visual odometry that is provided by the ARCore framework, we can achieve saving