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Research papers, The University of Auckland Library

The Screw Driving Sounding (SDS) method developed in Japan is a relatively new insitu testing technique to characterise soft shallow sites, typically those required for residential house construction. An SDS machine drills a rod into the ground in several loading steps while the rod is continuously rotated. Several parameters, such as torque, load and speed of penetration, are recorded at every rotation of the rod. The SDS method has been introduced in New Zealand, and the results of its application for characterising local sites are discussed in this study. A total of 164 SDS tests were conducted in Christchurch, Wellington and Auckland to validate/adjust the methodologies originally developed based on the Japanese practice. Most of the tests were conducted at sites where cone penetration tests (CPT), standard penetration tests (SPT) and borehole logs were available; the comparison of SDS results with existing information showed that the SDS method has great potential as an in-situ testing method for classifying the soils. By compiling the SDS data from 3 different cities and comparing them with the borehole logs, a soil classification chart was generated for identifying the soil type based on SDS parameters. Also, a correlation between fines content and SDS parameters was developed and a procedure for estimating angle of internal friction of sand using SDS parameters was investigated. Furthermore, a correlation was made between the tip resistance of the CPT and the SDS data for different percentages of fines content. The relationship between the SPT N value and a SDS parameter was also proposed. This thesis also presents a methodology for identifying the liquefiable layers of soil using SDS data. SDS tests were performed in both liquefied and non-liquefied areas in Christchurch to find a representative parameter and relationship for predicting the liquefaction potential of soil. Plots were drawn of the cyclic shear stress ratios (CSR) induced by the earthquakes and the corresponding energy of penetration during SDS tests. By identifying liquefied or unliquefied layers using three different popular CPT-based methods, boundary lines corresponding to the various probabilities of liquefaction happening were developed for different ranges of fines contents using logistic regression analysis, these could then be used for estimating the liquefaction potential of soil directly from the SDS data. Finally, the drilling process involved in screw driving sounding was simulated using Abaqus software. Analysis results proved that the model successfully captured the drilling process of the SDS machine in sand. In addition, a chart to predict peak friction angles of sandy sites based on measured SDS parameters for various vertical effective stresses was formulated. As a simple, fast and economical test, the SDS method can be a reliable alternative insitu test for soil and site characterisation, especially for residential house construction.

Research papers, University of Canterbury Library

Abstract This study provides a simplified methodology for pre-event data collection to support a faster and more accurate seismic loss estimation. Existing pre-event data collection frameworks are reviewed. Data gathered after the Canterbury earthquake sequences are analysed to evaluate the relative importance of different sources of building damage. Conclusions drawns are used to explore new approaches to conduct pre-event building assessment.

Research papers, University of Canterbury Library

Geospatial liquefaction models aim to predict liquefaction using data that is free and readily-available. This data includes (i) common ground-motion intensity measures; and (ii) geospatial parameters (e.g., among many, distance to rivers, distance to coast, and Vs30 estimated from topography) which are used to infer characteristics of the subsurface without in-situ testing. Since their recent inception, such models have been used to predict geohazard impacts throughout New Zealand (e.g., in conjunction with regional ground-motion simulations). While past studies have demonstrated that geospatial liquefaction-models show great promise, the resolution and accuracy of the geospatial data underlying these models is notably poor. As an example, mapped rivers and coastlines often plot hundreds of meters from their actual locations. This stems from the fact that geospatial models aim to rapidly predict liquefaction anywhere in the world and thus utilize the lowest common denominator of available geospatial data, even though higher quality data is often available (e.g., in New Zealand). Accordingly, this study investigates whether the performance of geospatial models can be improved using higher-quality input data. This analysis is performed using (i) 15,101 liquefaction case studies compiled from the 2010-2016 Canterbury Earthquakes; and (ii) geospatial data readily available in New Zealand. In particular, we utilize alternative, higher-quality data to estimate: locations of rivers and streams; location of coastline; depth to ground water; Vs30; and PGV. Most notably, a region-specific Vs30 model improves performance (Figs. 3-4), while other data variants generally have little-to-no effect, even when the “standard” and “high-quality” values differ significantly (Fig. 2). This finding is consistent with the greater sensitivity of geospatial models to Vs30, relative to any other input (Fig. 5), and has implications for modeling in locales worldwide where high quality geospatial data is available.

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

Overview of SeisFinder SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database. The basic SeisFinder architecture is shown in Figure 1.

Research papers, University of Canterbury Library

The 14 November 2016 Kaikōura earthquake had major impacts on New Zealand's transport system. Road, rail and port infrastructure was damaged, creating substantial disruption for transport operators, residents, tourists, and business owners in the Canterbury, Marlborough and Wellington regions, with knock-on consequences elsewhere. During both the response and recovery phases, a large amount of information and data relating to the transport system was generated, managed, analysed, and exchanged within and between organisations to assist decision making. To improve information and data exchanges and related decision making in the transport sector during future events and guide new resilience strategies, we present key findings from a recent post-earthquake assessment. The research involved 35 different stakeholder groups and was conducted for the Ministry of Transport. We consider what transport information was available, its usefulness, where it was sourced from, mechanisms for data transfer between organisations, and suggested approaches for continued monitoring.

Research papers, University of Canterbury Library

Liquefaction-induced lateral spreading during the 2011 Christchurch earthquake in New Zealand was severe and extensive, and data regarding the displacements associated with the lateral spreading provides an excellent opportunity to better understand the factors that influence these movements. Horizontal displacements measured from optical satellite imagery and subsurface data from the New Zealand Geotechnical Database (NZGD) were used to investigate four distinct lateral spread areas along the Avon River in Christchurch. These areas experienced displacements between 0.5 and 2 m, with the inland extent of displacement ranging from 100 m to over 600 m. Existing empirical and semi-empirical displacement models tend to under estimate displacements at some sites and over estimate at others. The integrated datasets indicate that the areas with more severe and spatially extensive displacements are associated with thicker and more laterally continuous deposits of liquefiable soil. In some areas, the inland extent of displacements is constrained by geologic boundaries and geomorphic features, as expressed by distinct topographic breaks. In other areas the extent of displacement is influenced by the continuity of liquefiable strata or by the presence of layers that may act as vertical seepage barriers. These observations demonstrate the need to integrate geologic/geomorphic analyses with geotechnical analyses when assessing the potential for lateral spreading movements.

Research papers, University of Canterbury Library

Motivation This poster aims to present fragility functions for pipelines buried in liquefaction-prone soils. Existing fragility models used to quantify losses can be based on old data or use complex metrics. Addressing these issues, the proposed functions are based on the Christchurch network and soil and utilizes the Canterbury earthquake sequence (CES) data, partially represented in Figure 1. Figure 1 (a) presents the pipe failure dataset, which describes the date, location and pipe on which failures occurred. Figure 1 (b) shows the simulated ground motion intensity median of the 22nd February 2011 earthquake. To develop the model, the network and soil characteristics have also been utilized.

Research papers, University of Canterbury Library

We present the initial findings from a study of adaptive resilience of lifelines organisations providing essential infrastructure services, in Christchurch, New Zealand following the earthquakes of 2010-2011. Qualitative empirical data was collected from 200 individuals in 11 organisations. Analysis using a grounded theory method identified four major factors that aid organisational response, recovery and renewal following major disruptive events. Our data suggest that quality of top and middle-level leadership, quality of external linkages, level of internal collaboration, ability to learn from experience, and staff well-being and engagement influence adaptive resilience. Our data also suggest that adaptive resilience is a process or capacity, not an outcome and that it is contextual. Post-disaster capacity/resources and post-disaster environment influence the nature of adaptive resilience.

Research papers, The University of Auckland Library

The full scale, in-situ investigations of instrumented buildings present an excellent opportunity to observe their dynamic response in as-built environment, which includes all the real physical properties of a structure under study and its surroundings. The recorded responses can be used for better understanding of behavior of structures by extracting their dynamic characteristics. It is significantly valuable to examine the behavior of buildings under different excitation scenarios. The trends in dynamic characteristics, such as modal frequencies and damping ratios, thus developed can provide quantitative data for the variations in the behavior of buildings. Moreover, such studies provide invaluable information for the development and calibration of realistic models for the prediction of seismic response of structures in model updating and structural health monitoring studies. This thesis comprises two parts. The first part presents an evaluation of seismic responses of two instrumented three storey RC buildings under a selection of 50 earthquakes and behavioral changes after Ms=7.1 Darfield (2010) and Ms=6.3 Christchurch (2011) earthquakes for an instrumented eight story RC building. The dynamic characteristics of the instrumented buildings were identified using state-of-the-art N4SID system identification technique. Seismic response trends were developed for the three storey instrumented buildings in light of the identified frequencies and the peak response accelerations (PRA). Frequencies were observed to decrease with excitation level while no trends are discernible for the damping ratios. Soil-structure interaction (SSI) effects were also determined to ascertain their contribution in the seismic response. For the eight storey building, it was found through system identification that strong nonlinearities in the structural response occurred and manifested themselves in all identified natural frequencies of the building that exhibited a marked decrease during the strong motion duration compared to the pre-Darfield earthquakes. Evidence of foundation rocking was also found that led to a slight decrease in the identified modal frequencies. Permanent stiffness loss was also observed after the strong motion events. The second part constitutes developing and calibrating finite element model (FEM) of the instrumented three storey RC building with a shear core. A three dimensional FEM of the building is developed in stages to analyze the effect of structural, non-structural components (NSCs) and SSI on the building dynamics. Further to accurately replicate the response of the building following the response trends developed in the first part of the thesis, sensitivity based model updating technique was applied. The FEMs were calibrated by tuning the updating parameters which are stiffnesses of concrete, NSCs and soil. The updating parameters were found to generally follow decreasing trends with the excitation level. Finally, the updated FEM was used in time history analyses to assess the building seismic performance at the serviceability limit state shaking. Overall, this research will contribute towards better understanding and prediction of the behavior of structures subjected to ground motion.

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.

Articles, UC QuakeStudies

An example of the five year rebuild schedule map created as part of the prioritisation process detailing where and when construction would start. The data behind this map was updated every quarter.