The Manchester Courts building was a heritage building located in central Christchurch (New Zealand) that was damaged in the Mw 7.1 Darfield earthquake on 4 September 2010 and subsequently demolished as a risk reduction exercise. Because the building was heritage listed, the decision to demolish the building resulted in strong objections from heritage supporters who were of the opinion that the building had sufficient residual strength to survive possible aftershock earthquakes. On 22 February 2011 Christchurch was struck by a severe aftershock, leading to the question of whether building demolition had proven to be the correct risk reduction strategy. Finite element analysis was used to undertake a performance-based assessment, validating the accuracy of the model using the damage observed in the building before its collapse. In addition, soil-structure interaction was introduced into the research due to the comparatively low shear wave velocity of the soil. The demolition of a landmark heritage building was a tragedy that Christchurch will never recover from, but the decision was made considering safety, societal, economic and psychological aspects in order to protect the city and its citizens. The analytical results suggest that the Manchester Courts building would have collapsed during the 2011 Christchurch earthquake, and that the collapse of the building would have resulted in significant fatalities.
The Canterbury earthquake series of 2010/2011 has turned the city of Christchurch into a full scale natural laboratory testing the structural and non-structural response of buildings under moderate to very severe earthquake shaking. The lessons learned from this, which have come at great cost socially and economically, are extremely valuable in increasing our understanding of whole building performance in severe earthquakes. Given current initiatives underway on both sides of the Tasman towards developing joint Australasian steel and composite steel/concrete design and construction standards that would span a very wide range of geological conditions and seismic zones, these lessons are relevant to both countries. This paper focusses on the performance of steel framed buildings in Christchurch city, with greatest emphasis on multi-storey buildings, but also covering single storey steel framed buildings and light steel framed housing. It addresses such issues as the magnitude and structural impact of the earthquake series, importance of good detailing, lack of observed column base hinging, the excellent performance of composite floors and it will briefly cover research underway to quantify some of these effects for use in design.
The 2010-2011 Canterbury earthquake sequence, and the resulting extensive data sets on damaged buildings that have been collected, provide a unique opportunity to exercise and evaluate previously published seismic performance assessment procedures. This poster provides an overview of the authors’ methodology to perform evaluations with two such assessment procedures, namely the P-58 guidelines and the REDi Rating System. P-58, produced by the Federal Emergency Management Agency (FEMA) in the United States, aims to facilitate risk assessment and decision-making by quantifying earthquake ground shaking, structural demands, component damage and resulting consequences in a logical framework. The REDi framework, developed by the engineering firm ARUP, aids stakeholders in implementing resilience-based earthquake design. Preliminary results from the evaluations are presented. These have the potential to provide insights on the ability of the assessment procedures to predict impacts using “real-world” data. However, further work remains to critically analyse these results and to broaden the scope of buildings studied and of impacts predicted.
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.
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.
Validation is an essential step to assess the applicability of simulated ground motions for utilization in engineering practice, and a comprehensive analysis should include both simple intensity measures (PGA, SA, etc), as well as the seismic response of a range of complex systems obtained by response history analysis. In order to enable a spectrum of complex structural systems to be considered in systematic validation of ground motion simulations in a routine fashion, an automated workflow was developed. Such a workflow enables validation of simulated ground motions in terms of different complex model responses by considering various ground motion sets and different ground motion simulation methods. The automated workflow converts the complex validation process into a routine one by providing a platform to perform the validation process promptly as a built-in process of simulation post-processing. As a case study, validation of simulated ground motions was investigated via the automated workflow by comparing the dynamic responses of three steel special moment frame (SMRF) subjected to the 40 observed and 40 simulated ground motions of 22 February 2011 Christchurch earthquake. The seismic responses of the structures are principally quantified via the peak floor acceleration and maximum inter-storey drift ratio. Overall, the results indicate a general agreement in seismic demands obtained using the recorded and simulated ensembles of ground motions and provide further evidence that simulated ground motions can be used in code-based structural performance assessments in-place of, or in combination with, ensembles of recorded ground motions.
