This thesis describes research into developing a client/server ar- chitecture for a mobile Augmented Reality (AR) application. Following the earthquakes that have rocked Christchurch the city is now changed forever. CityViewAR is an existing mobile AR application designed to show how the city used to look before the earthquakes. In CityViewAR 3D virtual building models are overlaid onto video captured by a smartphone camera. However the current version of CityViewAR only allows users to browse information stored on the mobile device. In this research the author extends the CityViewAR application to a client-server model so that anyone can upload models and annotations to a server and have this information viewable on any smartphone running the application. In this thesis we describe related work on AR browser architectures, the system we developed, a user evaluation of the prototype system and directions for future work.
After a high-intensity seismic event, inspections of structural damages need to be carried out as soon as possible in order to optimize the emergency management, as well as improving the recovery time. In the current practice, damage inspections are performed by an experienced engineer, who physically inspect the structures. This way of doing not only requires a significant amount of time and high skilled human resources, but also raises the concern about the inspector’s safety. A promising alternative is represented using new technologies, such as drones and artificial intelligence, which can perform part of the damage classification task. In fact, drones can safely access high hazard components of the structures: for instance, bridge piers or abutments, and perform the reconnaissance by using highresolution cameras. Furthermore, images can be automatically processed by machine learning algorithms, and damages detected. In this paper, the possibility of applying such technologies for inspecting New Zealand bridges is explored. Firstly, a machine-learning model for damage detection by performing image analysis is presented. Specifically, the algorithm was trained to recognize cracks in concrete members. A sensitivity analysis was carried out to evaluate the algorithm accuracy by using database images. Depending on the confidence level desired,i.e. by allowing a manual classification where the alghortim confidence is below a specific tolerance, the accuracy was found reaching up to 84.7%. In the second part, the model is applied to detect the damage observed on the Anzac Bridge (GPS coordinates -43.500865, 172.701138) in Christchurch by performing a drone reconnaissance. Reults show that the accuracy of the damage detection was equal to 88% and 63% for cracking and spalling, respectively.
