Timber-based hybrid structures provide a prospective solution for utilizing environmentally friendly timber material in the construction of mid-rise or high-rise structures. This study mainly focuses on structural damage evaluation for a type of timber-steel hybrid structures, which incorporate prefabricated light wood frame shear walls into steel moment-resisting frames (SMRFs). The structural damage of such a hybrid structure was evaluated through shake table tests on a four-story large-scale timber-steel hybrid structure. Four ground motion records (i.e., Wenchuan earthquake, Canterbury earthquake, El-Centro earthquake, and Kobe earthquake) were chosen for the tests, with the consideration of three different probability levels (i.e., minor, moderate and major earthquakes) for each record. During the shake table tests, the hybrid structure performed quite well with visual damage only to wood shear walls. No visual damage in SMRF and the frame-to-wall connections was observed. The correlation of visual damage to seismic intensity, modal-based damage index and inter-story drift was discussed. The reported work provided a basis of knowledge for performance-based seismic design (PBSD) for such timber-based hybrid structures.
Unreinforced masonry (URM) structures comprise a majority of the global built heritage. The masonry heritage of New Zealand is comparatively younger to its European counterparts. In a country facing frequent earthquakes, the URM buildings are prone to extensive damage and collapse. The Canterbury earthquake sequence proved the same, causing damage to over _% buildings. The ability to assess the severity of building damage is essential for emergency response and recovery. Following the Canterbury earthquakes, the damaged buildings were categorized into various damage states using the EMS-98 scale. This article investigates machine learning techniques such as k-nearest neighbors, decision trees, and random forests, to rapidly assess earthquake-induced building damage. The damage data from the Canterbury earthquake sequence is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. On getting a high accuracy the model is then run for building database collected for Dunedin to predict expected damage during the rupture of the Akatore fault.
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.
This thesis describes the strategies for earthquake strengthening vintage clay bricks unreinforced masonry (URM) buildings. URM buildings are well known to be vulnerable to damage from earthquake-induced lateral forces that may result in partial or full building collapse. The 2010/2011 Canterbury earthquakes are the most recent destructive natural disaster that resulted in the deaths of 185 people. The earthquake events had drawn people’s attention when URM failure and collapse caused about 39 of the fatality. Despite the poor performance of URM buildings during the 2010/2011 Canterbury earthquakes, a number of successful case study buildings were identified and their details research in-depth. In order to discover the successful seismic retrofitting techniques, two case studies of retrofitted historical buildings located in Christchurch, New Zealand i.e. Orion’s URM substations and an iconic Heritage Hotel (aka Old Government Building) was conducted by investigating and evaluating the earthquake performance of the seismic retrofitting technique applied on the buildings prior to the 2010/2011 Canterbury earthquakes and their performance after the earthquakes sequence. The second part of the research reported in this thesis was directed with the primary aim of developing a cost-effective seismic retrofitting technique with minimal interference to the vintage clay-bricks URM buildings. Two retrofitting techniques, (i) near-surface mounted steel wire rope (NSM-SWR) with further investigation on URM wallettes to get deeper understanding the URM in-plane behaviour, and (ii) FRP anchor are reported in this research thesis.
