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Images for modelling; more images...

Images, UC QuakeStudies

A photograph of components of a model of the ChristChurch Cathedral being built from LEGO by Sam Butcher. Sam comments "Fixing a large variety of bits that were wrong/annoying/cheating (not purist) about the last model. The new one is set AFTER the Feb 22 earthquake. This newer, and much stronger model is also completely modular for easier transport. Obviously still a WIP, I'm currently waiting for a pretty large bricklink order at the moment, and will probably need to place a couple more after that too. Side of the new technical design for the Cathedral Rose Window".

Images, UC QuakeStudies

A photograph of components of a model of the ChristChurch Cathedral being built from LEGO by Sam Butcher. Sam comments "Fixing a large variety of bits that were wrong/annoying/cheating (not purist) about the last model. The new one is set AFTER the Feb 22 earthquake. This newer, and much stronger model is also completely modular for easier transport. Obviously still a WIP, I'm currently waiting for a pretty large bricklink order at the moment, and will probably need to place a couple more after that too. The new base, which breaks into two sections each 48x70 studs".

Images, UC QuakeStudies

A photograph of components of a model of the ChristChurch Cathedral being built from LEGO by Sam Butcher. Sam comments "Fixing a large variety of bits that were wrong/annoying/cheating (not purist) about the last model. The new one is set AFTER the Feb 22 earthquake. This newer, and much stronger model is also completely modular for easier transport. Obviously still a WIP, I'm currently waiting for a pretty large bricklink order at the moment, and will probably need to place a couple more after that too. The new base, which breaks into two sections each 48x70 studs".

Research papers, University of Canterbury Library

Background This study examines the performance of site response analysis via nonlinear total-stress 1D wave-propagation for modelling site effects in physics-based ground motion simulations of the 2010-2011 Canterbury, New Zealand earthquake sequence. This approach allows for explicit modeling of 3D ground motion phenomena at the regional scale, as well as detailed nonlinear site effects at the local scale. The approach is compared to a more commonly used empirical VS30 (30 m time-averaged shear wave velocity)-based method for computing site amplification as proposed by Graves and Pitarka (2010, 2015), and to empirical ground motion prediction via a ground motion model (GMM).

Research papers, University of Canterbury Library

In this paper we apply Full waveform tomography (FWT) based on the Adjoint-Wavefield (AW) method to iteratively invert a 3-D geophysical velocity model for the Canterbury region (Lee, 2017) from a simple initial model. The seismic wavefields was generated using numerical solution of the 3-D elastodynamic/ visco- elastodynamic equations (EMOD3D was adopted (Graves, 1996)), and through the AW method, gradients of model parameters (compression and shear wave velocity) were computed by implementing the cross-adjoint of forward and backward wavefields. The reversed-in-time displacement residual was utilized as the adjoint source. For inversion, we also account for the near source/ station effects, gradient precondition, smoothening (Gaussian filter in spatial domain) and optimal step length. Simulation-to-observation misfit measurements based on 191 sources at 78 seismic stations in the Canterbury region (Figure 1) were used into our inversion. The inversion process includes multiple frequency bands, starting from 0-0.05Hz, and advancing to higher frequency bands (0-0.1Hz and 0-0.2Hz). Each frequency band was used for up to 10 iterations or no optimal step length found. After 3 FWT inversion runs, the simulated seismograms computed using our final model show a good matching with the observed seismograms at frequencies from 0 - 0.2 Hz and the normalized least-squared misfit error has been significantly reduced. Over all, the synthetic study of FWT shows a good application to improve the crustal velocity models from the existed geological models and the seismic data of the different earthquake events happened in the Canterbury region.

Articles, UC QuakeStudies

A document that outlines how timely and accurate information relating to estimating, actual project costs, future commitments, and total forecast cost, will be managed and reported for each project phase in the programme.

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.

Research papers, University of Canterbury Library

This study explores the nature of smaller businesses’ resilience following two major earthquakes that severely disrupted their place of doing business. Data from the owners of ten smaller businesses are qualitative and longitudinal, spanning the period 2011 through 2018, providing first-hand narrative accounts of their responses in the earthquakes’ aftermath. All ten owners showed some individual resilience; six businesses survived through to 2018, of which three have recovered strongly. All three owned their premises; operated business-tobusiness models; and were able to adapt and continue to follow path-extension strategies. All the other businesses had direct business-to-customer models operating from leased premises, typically in major retail malls. Four eventually recognised path-exhaustion at different times and so did not survive through to 2018. We conclude however that post-disaster recovery is best explained in terms of business model resilience. Even the most resilient of individual owners will struggle to survive if their business model is either not resilient or cannot be made so. Individual resilience is necessary but not sufficient.

Research papers, University of Canterbury Library

Well-validated liquefaction constitutive models are increasingly important as non-linear time history analyses become relatively more common in industry for key projects. Previous validation efforts of PM4Sand, a plasticity model specifically for liquefaction, have generally focused on centrifuge tests; however, pore pressure transducers installed at several free-field sites during the Canterbury Earthquake Sequence (CES) in Christchurch, New Zealand provide a relatively unique dataset to validate against. This study presents effective stress site response analyses performed in the finite difference software FLAC to examine the capability of PM4Sand to capture the generation of excess pore pressures during earthquakes. The characterization of the subsurface is primarily based on extensive cone penetration tests (CPT) carried out in Christchurch. Correlations based on penetration resistances are used to estimate soil parameters, such as relative density and shear wave velocity, which affect liquefaction behaviour. The resulting free-field FLAC model is used to estimate time histories of excess pore pressure, which are compared with records during several earthquakes in the CES to assess the suitability of PM4Sand.

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.