Search

found 7038 results

Images for damaged; more images...

Images, UC QuakeStudies

Damage to Lyttelton following the 22 February 2011 earthquake. Forbes' Store on Norwich Quay with a broken awning and damage visible on the brick walls. Scaffolding placed around the building since the 4 September 2010 earthquake has tumbled during the 22 February 2011 earthquake.

Images, UC QuakeStudies

Damage to Lyttelton following the 22 February 2011 earthquake. Forbes' Store on Norwich Quay with a broken awning and damage visible on the brick walls. Scaffolding placed around the building since the 4 September 2010 earthquake has tumbled during the 22 February 2011 earthquake.

Research papers, The University of Auckland Library

This thesis presents an assessment of historic seismic performance of the New Zealand stopbank network from the 1968 Inangahua earthquake through to the 2016 Kaikōura earthquake. An overview of the types of stopbanks and the main aspects of the design and construction of earthen stopbanks was presented. Stopbanks are structures that are widely used on the banks of rivers and other water bodies to protect against the impact of flood events. Earthen stopbanks are found to be the most used for such protection measures. Different stopbank damage or failure modes that may occur due to flooding or earthquake excitation were assessed with a focus on past earthquakes internationally, and examples of these damage and failure modes were presented. Stopbank damage and assessment reports were collated from available reconnaissance literature to develop the first geospatial database of stopbank damage observed in past earthquakes in New Zealand. Damage was observed in four earthquakes over the past 50 years, with a number of earthquakes resulting in no stopbank damage. The damage database therefore focussed on the Edgecumbe, Darfield, Christchurch and Kaikōura earthquakes. Cracking of the crest and liquefaction-induced settlement were the most common forms of damage observed. To understand the seismic demand on the stopbank network in past earthquakes, geospatial analyses were undertaken to approximate the peak ground acceleration (PGA) across the stopbank network for ten large earthquakes that have occurred in New Zealand over the past 50 years. The relationship between the demand, represented by the peak ground acceleration (PGA) and damage is discussed and key trends identified. Comparison of the seismic demand and the distribution of damage suggested that the seismic performance of the New Zealand stopbank network has been generally good across all events considered. Although a significant length of the stopbank networks were exposed to high levels of shaking in past events, the overall damage length was a small percentage of this. The key aspect controlling performance was the performance of the underlying foundation soils and the effect of this on the stopbank structure and stability.

Research papers, University of Canterbury Library

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.