Existing unreinforced masonry (URM) buildings are often composed of traditional construction techniques, with poor connections between walls and diaphragms that results in poor performance when subjected to seismic actions. In these cases the application of the common equivalent static procedure is not applicable because it is not possible to assure “box like” behaviour of the structure. In such conditions the ultimate strength of the structure relies on the behaviour of the macro-elements that compose the deformation mechanisms of the whole structure. These macroelements are a single or combination of structural elements of the structure which are bonded one to each other. The Canterbury earthquake sequence was taken as a reference to estimate the most commonly occurring collapse mechanisms found in New Zealand URM buildings in order to define the most appropriate macroelements.
Seismic behaviour of typical unreinforced masonry (URM) brick houses, that were common in early last century in New Zealand and still common in many developing countries, is experimentally investigated at University of Canterbury, New Zealand in this research. A one halfscale model URM house is constructed and tested under earthquake ground motions on a shaking table. The model structure with aspect ratio of 1.5:1 in plan was initially tested in the longitudinal direction for several earthquakes with peak ground acceleration (PGA) up to 0.5g. Toppling of end gables (above the eaves line) and minor to moderate cracking around window and door piers was observed in this phase. The structure was then rotated 90º and tested in the transverse (short) direction for ground motions with PGA up to 0.8g. Partial out-of-plane failure of the face loaded walls in the second storey and global rocking of the model was observed in this phase. A finite element analysis and a mechanism analysis are conducted to assess the dynamic properties and lateral strength of the model house. Seismic fragility function of URM houses is developed based on the experimental results. Damping at different phases of the response is estimated using an amplitude dependent equivalent viscous damping model. Financial risk of similar URM houses is then estimated in term of expected annual loss (EAL) following a probabilistic financial risk assessment framework. Risks posed by different levels of damage and by earthquakes of different frequencies are then examined.
The seismic response of unreinforced masonry (URM) buildings, in both their as-built or retrofitted configuration, is strongly dependent on the characteristics of wooden floors and, in particular, on their in-plane stiffness and on the quality of wall-to-floor connections. As part of the development of alternative performance-based retrofit strategies for URM buildings, experimental research has been carried out by the authors at the University of Canterbury, in order to distinguish the different elements contributing to the whole diaphragm's stiffness. The results have been compared to the ones predicted through the use of international guidelines in order to highlight shortcomings and qualities and to propose a simplified formulation for the evaluation of the stiffness properties.
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.
