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.
Seismic isolation is an effective technology for significantly reducing damage to buildings and building contents. However, its application to light-frame wood buildings has so far been unable to overcome cost and technical barriers such as susceptibility to movement during high-wind loading. The precursor to research in the field of isolation of residential buildings was the 1994 Northridge Earthquake (6.7 MW) in the United States and the 1995 Kobe Earthquake (6.9 MW) in Japan. While only a small number of lives were lost in residential buildings in these events, the economic impact was significant with over half of earthquake recovery costs given to repair and reconstruction of residential building damage. A value case has been explored to highlight the benefits of seismically isolated residential buildings compared to a standard fixed-base dwellings for the Wellington region. Loss data generated by insurance claim information from the 2011 Christchurch Earthquake has been used by researchers to determine vulnerability functions for the current light-frame wood building stock. By further considering the loss attributed to drift and acceleration sensitive components, and a simplified single degree of freedom (SDOF) building model, a method for determining vulnerability functions for seismic isolated buildings was developed. Vulnerability functions were then applied directly in a loss assessment using the GNS developed software, RiskScape. Vulnerability was shown to dramatically reduce for isolated buildings compared to an equivalent fixed-base building and as a result, the monetary savings in a given earthquake scenario were significant. This work is expected to drive further interest for development of solutions for the seismic isolation of residential dwellings, of which one option is further considered and presented herein.
Based on a qualitative study of four organisations involving 47 respondents following the extensive 2010 – 2011 earthquakes in Christchurch, New Zealand, this paper presents some guidance for human resource practitioners dealing with post-disaster recovery. A key issue is the need for the human resource function to reframe its practices in a post-disaster context, developing a specific focus on understanding and addressing changing employee needs, and monitoring the leadership behaviour of supervisors. This article highlights the importance of flexible organisational responses based around a set of key principles concerning communication and employee perceptions of company support.
Disasters are rare events with major consequences; yet comparatively little is known about managing employee needs in disaster situations. Based on case studies of four organisations following the devastating earthquakes of 2010 - 2011 in Christchurch, New Zealand, this paper presents a framework using redefined notions of employee needs and expectations, and charting the ways in which these influence organisational recovery and performance. Analysis of in-depth interview data from 47 respondents in four organisations highlighted the evolving nature of employee needs and the crucial role of middle management leadership in mitigating the effects of disasters. The findings have counterintuitive implications for human resource functions in a disaster, suggesting that organisational justice forms a central framework for managing organisational responses to support and engage employees for promoting business recovery.
essential systems upon which the well-being and functioning of societies depend. They deliver a service or a good to the population using a network, a combination of spatially-distributed links and nodes. As they are interconnected, network elements’ functionality is also interdependent. In case of a failure of one component, many others could be momentarily brought out-of-service. Further problems arise for buried infrastructure when it comes to buried infrastructure in earthquake and liquefaction-prone areas for the following reasons: • Technically more demanding inspections than those required for surface horizontal infrastructure • Infrastructure subject to both permanent ground displacement and transient ground deformation • Increase in network maintenance costs (i.e. deterioration due to ageing material and seismic hazard) These challenges suggest careful studies on network resilience will yield significant benefits. For these reasons, the potable water network of Christchurch city (Figure 1) has been selected for its well-characterized topology and its extensive repair dataset.
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.
Despite the relatively low seismicity, a large earthquake in the Waikato region is expected to have a high impact, when the fourth-largest regional population and economy and the high density critical infrastructure systems in this region are considered. Furthermore, Waikato has a deep soft sedimentary basin, which increases the regional seismic hazard due to trapping and amplification of seismic waves and generation of localized surface waves within the basin. This phenomenon is known as the “Basin Effect”, and has been attributed to the increased damage in several historic earthquakes, including the 2010-2011 Canterbury earthquakes. In order to quantitatively model the basin response and improve the understanding of regional seismic hazard, geophysical methods will be used to develop shear wave velocity profiles across the Waikato basin. Active surface wave methods involve the deployment of linear arrays of geophones to record the surface waves generated by a sledge hammer. Passive surface wave methods involve the deployment of two-dimensional seismometer arrays to record ambient vibrations. At each site, the planned testing includes one active test and two to four passive arrays. The obtained data are processed to develop dispersion curves, which describe surface wave propagation velocity as a function of frequency (or wavelength). Dispersion curves are then inverted using the Geopsy software package to develop a suite of shear wave velocity profiles. Currently, more than ten sites in Waikato are under consideration for this project. This poster presents the preliminary results from the two sites that have been tested. The shear wave velocity profiles from all sites will be used to produce a 3D velocity model for the Waikato basin, a part of QuakeCoRE flagship programme 1.
This poster presents work to date on ground motion simulation validation and inversion for the Canterbury, New Zealand region. Recent developments have focused on the collection of different earthquake sources and the verification of the SPECFEM3D software package in forward and inverse simulations. SPECFEM3D is an open source software package which simulates seismic wave propagation and performs adjoint tomography based upon the spectral-element method. Figure 2: Fence diagrams of shear wave velocities highlighting the salient features of the (a) 1D Canterbury velocity model, and (b) 3D Canterbury velocity model. Figure 5: Seismic sources and strong motion stations in the South Island of New Zealand, and corresponding ray paths of observed ground motions. Figure 3: Domain used for the 19th October 2010 Mw 4.8 case study event including the location of the seismic source and strong motion stations. By understanding the predictive and inversion capabilities of SPECFEM3D, the current 3D Canterbury Velocity Model can be iteratively improved to better predict the observed ground motions. This is achieved by minimizing the misfit between observed and simulated ground motions using the built-in optimization algorithm. Figure 1 shows the Canterbury Velocity Model domain considered including the locations of small-to-moderate Mw events [3-4.5], strong motion stations, and ray paths of observed ground motions. The area covered by the ray paths essentially indicates the area of the model which will be most affected by the waveform inversion. The seismic sources used in the ground motion simulations are centroid moment tensor solutions obtained from GeoNet. All earthquake ruptures are modelled as point sources with a Gaussian source time function. The minimum Mw limit is enforced to ensure good signal-to-noise ratio and well constrained source parameters. The maximum Mw limit is enforced to ensure the point source approximation is valid and to minimize off-fault nonlinear effects.
