Natural catastrophes are increasing worldwide. They are becoming more frequent but also more severe and impactful on our built environment leading to extensive damage and losses. Earthquake events account for the smallest part of natural events; nevertheless seismic damage led to the most fatalities and significant losses over the period 1981-2016 (Munich Re). Damage prediction is helpful for emergency management and the development of earthquake risk mitigation projects. Recent design efforts focused on the application of performance-based design engineering where damage estimation methodologies use fragility and vulnerability functions. However, the approach does not explicitly specify the essential criteria leading to economic losses. There is thus a need for an improved methodology that finds the critical building elements related to significant losses. The here presented methodology uses data science techniques to identify key building features that contribute to the bulk of losses. It uses empirical data collected on site during earthquake reconnaissance mission to train a machine learning model that can further be used for the estimation of building damage post-earthquake. The first model is developed for Christchurch. Empirical building damage data from the 2010-2011 earthquake events is analysed to find the building features that contributed the most to damage. Once processed, the data is used to train a machine-learning model that can be applied to estimate losses in future earthquake events.
Between September 4, 2010 and December 23, 2011, a series of earthquakes struck the South Island of New Zealand including the city of Christchurch producing heavy damage. During the strongest shaking, the unreinforced masonry (URM) building stock in Christchurch was subjected to seismic loading equal to approximately 150-200% of code values. Post-earthquake reconnaissance suggested numerous failures of adhesive anchors used for retrofit connection of roof and floor diaphragms to masonry walls. A team of researchers from the Universities of Auckland (NZ) and Minnesota (USA) conducted a field investigation on the performance of new adhesive anchors installed in existing masonry walls. Variables included adhesive type, anchor diameter, embedment length, anchor inclination, and masonry quality. Buildings were selected that had been slated for demolition but which featured exterior walls that had not been damaged. A summary of the deformation response measured during the field tests are presented. AM - Accepted Manuscript
Unreinforced masonry (URM) cavity-wall construction is a form of masonry where two leaves of clay brick masonry are separated by a continuous air cavity and are interconnected using some form of tie system. A brief historical introduction is followed by details of a survey undertaken to determine the prevalence of URM cavity-wall buildings in New Zealand. Following the 2010/2011 Canterbury earthquakes it was observed that URM cavity-walls generally suffered irreparable damage due to a lack of effective wall restraint and deficient cavity-tie connections, combined with weak mortar strength. It was found that the original cavity-ties were typically corroded due to moisture ingress, resulting in decreased lateral loadbearing capacity of the cavity-walls. Using photographic data pertaining to Christchurch URM buildings that were obtained during post-earthquake reconnaissance, 252 cavity-walls were identified and utilised to study typical construction details and seismic performance. The majority (72%, 182) of the observed damage to URM cavity-wall construction was a result of out-of-plane type wall failures. Three types of out-of-plane wall failure were recognised: (1) overturning response, (2) one-way bending, and (3) two-way bending. In-plane damage was less widely observed (28%) and commonly included diagonal shear cracking through mortar bed joints or bricks. The collected data was used to develop an overview of the most commonly-encountered construction details and to identify typical deficiencies in earthquake response that can be addressed via the selection and implementation of appropriate mitigation interventions. http://www.journals.elsevier.com/structures
