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Images, UC QuakeStudies

A Civil Defence staff member completing a Level 1 Rapid Assessment inspection form for a damaged house. Some of the brickwork has collapsed from the outer wall and the awnings over the windows have collapsed.

Images, UC QuakeStudies

A Civil Defence staff member completing a Level 1 Rapid Assessment inspection form for a damaged house. Some of the brickwork has collapsed from the outer wall of the house and the awnings over the windows have collapsed.

Images, UC QuakeStudies

A Civil Defence staff member talking on his cell phone, he is holding clipboard with a form titled 'Christchurch Eq rapid assessment form level 1'. The brickwork of the house has crumbled and the broken windows have been boarded up.

Research papers, The University of Auckland Library

Two days after the 22 February 2011 M6.3 earthquake in Christchurch, New Zealand, three of the authors conducted a transect of the central city, with the goal of deriving an estimate of building damage levels. Although smaller in magnitude than the M7.1 4 September 2010 Darfield earthquake, the ground accelerations, ground deformation and damage levels in Christchurch central city were more severe in February 2011, and the central city was closed down to the general public. Written and photographic notes of 295 buildings were taken, including construction type, damage level, and whether the building would likely need to be demolished. The results of the transect compared favourably to Civil Defence rapid assessments made over the following month. Now, more than one year and two major aftershocks after the February 2011 earthquake these initial estimates are compared to the current demolition status to provide an updated understanding of the state of central Christchurch.

Research papers, The University of Auckland Library

Ingham and Biggs were in Christchurch during the M6.3, 22 February 2011 earthquake and Moon arrived the next day. They were enlisted by officials to provide rapid assessment of buildings within the Central Business District (CBD). In addition, they were asked to: 1) provide a rapid assessment of the numbers and types of buildings that had been damaged, and 2) identify indicator buildings that represent classes of structures that can be used to monitor changing conditions for each class following continuing aftershocks and subsequent damage. This paper explains how transect methodology was incorporated into the rapid damage assessment that was performed 48 hours after the earthquake. Approximately 300 buildings were assessed using exterior Level 1 reporting techniques. That data was used to draw conclusions on the condition of the entire CBD of approximately 4400 buildings. In the context of a disaster investigation, a transect involves traveling a selected path assessing the condition of the buildings and documenting the class of each building, and using the results in conjunction with prior knowledge relating to the overall population of buildings affected in the area of the study. Read More: http://ascelibrary.org/doi/abs/10.1061/9780784412640.033

Research papers, The University of Auckland Library

The paper proposes a simple method for quick post-earthquake assessment of damage and condition of a stock of bridges in a transportation network using seismic data recorded by a strong motion array. The first part of the paper is concerned with using existing free field strong motion recorders to predict peak ground acceleration (PGA) at an arbitrary bridge site. Two methods are developed using artificial neural networks (a single network and a committee of neural networks) considering influential parameters, such as seismic magnitude, hypocentral depth and epicentral distance. The efficiency of the proposed method is explored using actual strong motion records from the devastating 2010 Darfield and 2011 Christchurch earthquakes in New Zealand. In the second part, two simple ideas are outlined how to infer the likely damage to a bridge using either the predicted PGA and seismic design spectrum, or a broader set of seismic metrics, structural parameters and damage indices.

Research papers, The University of Auckland Library

Quick and reliable assessment of the condition of bridges in a transportation network after an earthquake can greatly assist immediate post-disaster response and long-term recovery. However, experience shows that available resources, such as qualified inspectors and engineers, will typically be stretched for such tasks. Structural health monitoring (SHM) systems can therefore make a real difference in this context. SHM, however, needs to be deployed in a strategic manner and integrated into the overall disaster response plans and actions to maximize its benefits. This study presents, in its first part, a framework of how this can be achieved. Since it will not be feasible, or indeed necessary, to use SHM on every bridge, it is necessary to prioritize bridges within individual networks for SHM deployment. A methodology for such prioritization based on structural and geotechnical seismic risks affecting bridges and their importance within a network is proposed in the second part. An example using the methodology application to selected bridges in the medium-sized transportation network of Wellington, New Zealand is provided. The third part of the paper is concerned with using monitoring data for quick assessment of bridge condition and damage after an earthquake. Depending on the bridge risk profile, it is envisaged that data will be obtained from either local or national seismic monitoring arrays or SHM systems installed on bridges. A method using artificial neural networks is proposed for using data from a seismic array to infer key ground motion parameters at an arbitrary bridges site. The methodology is applied to seismic data collected in Christchurch, New Zealand. Finally, how such ground motion parameters can be used in bridge damage and condition assessment is outlined. AM - Accepted manuscript

Research papers, The University of Auckland Library

Territorial authorities in New Zealand are responding to regulatory and market forces in the wake of the 2011 Christchurch earthquake to assess and retrofit buildings determined to be particularly vulnerable to earthquakes. Pending legislation may shorten the permissible timeframes on such seismic improvement programmes, but Auckland Council’s Property Department is already engaging in a proactive effort to assess its portfolio of approximately 3500 buildings, prioritise these assets for retrofit, and forecast construction costs for improvements. Within the programme structure, the following varied and often competing factors must be accommodated: * The council’s legal, fiscal, and ethical obligations to the people of Auckland per building regulations, health and safety protocols, and economic growth and urban development planning strategies; * The council’s functional priorities for service delivery; * Varied and numerous stakeholders across the largest territorial region in New Zealand in both population and landmass; * Heritage preservation and community and cultural values; and * Auckland’s prominent economic role in New Zealand’s economy which requires Auckland’s continued economic production post-disaster. Identifying those buildings most at risk to an earthquake in such a large and varied portfolio has warranted a rapid field assessment programme supplemented by strategically chosen detailed assessments. Furthermore, Auckland Council will benefit greatly in time and resources by choosing retrofit solutions, techniques, and technologies applicable to a large number of buildings with similar configurations and materials. From a research perspective, the number and variety of buildings within the council’s property portfolio will provide valuable data for risk modellers on building typologies in Auckland, which are expected to be fairly representative of the New Zealand building stock as a whole.

Research papers, University of Canterbury Library

Semi-empirical models based on in-situ geotechnical tests have become the standard of practice for predicting soil liquefaction. Since the inception of the “simplified” cyclic-stress model in 1971, variants based on various in-situ tests have been developed, including the Cone Penetration Test (CPT). More recently, prediction models based soley on remotely-sensed data were developed. Similar to systems that provide automated content on earthquake impacts, these “geospatial” models aim to predict liquefaction for rapid response and loss estimation using readily-available data. This data includes (i) common ground-motion intensity measures (e.g., PGA), which can either be provided in near-real-time following an earthquake, or predicted for a future event; and (ii) geospatial parameters derived from digital elevation models, which are used to infer characteristics of the subsurface relevent to liquefaction. However, the predictive capabilities of geospatial and geotechnical models have not been directly compared, which could elucidate techniques for improving the geospatial models, and which would provide a baseline for measuring improvements. Accordingly, this study assesses the realtive efficacy of liquefaction models based on geospatial vs. CPT data using 9,908 case-studies from the 2010-2016 Canterbury earthquakes. While the top-performing models are CPT-based, the geospatial models perform relatively well given their simplicity and low cost. Although further research is needed (e.g., to improve upon the performance of current models), the findings of this study suggest that geospatial models have the potential to provide valuable first-order predictions of liquefaction occurence and consequence. Towards this end, performance assessments of geospatial vs. geotechnical models are ongoing for more than 20 additional global earthquakes.

Research papers, The University of Auckland Library

The 2010 Darfield earthquake is the largest earthquake on record to have occurred within 40 km of a major city and not cause any fatalities. In this paper the authors have reflected on their experiences in Christchurch following the earthquake with a view to what worked, what didn’t, and what lessons can be learned from this for the benefit of Australian earthquake preparedness. Owing to the fact that most of the observed building damage occurred in Unreinforced Masonry (URM) construction, this paper focuses in particular on the authors’ experience conducting rapid building damage assessment during the first 72 hours following the earthquake and more detailed examination of the performance of unreinforced masonry buildings with and without seismic retrofit interventions.

Research papers, The University of Auckland Library

This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.