Disasters are often followed by a large-scale stimulus supporting the economy through the built environment, which can last years. During this time, official economic indicators tend to suggest the economy is doing well, but as activity winds down, the sentiment can quickly change. In response to the damaging 2011 earthquakes in Canterbury, New Zealand, the regional economy outpaced national economic growth rates for several years during the rebuild. The repair work on the built environment created years of elevated building activity. However, after the peak of the rebuilding activity, as economic and employment growth retracts below national growth, we are left with the question of how the underlying economy performs during large scale stimulus activity in the built environment. This paper assesses the performance of the underlying economy by quantifying the usual, demand-driven level of building activity at this time. Applying an Input–Output approach and excluding the economic benefit gained from the investment stimulus reveals the performance of the underlying economy. The results reveal a strong growing underlying economy, and while convergence was expected as the stimulus slowed down, the results found that growth had already crossed over for some time. The results reveal that the investment stimulus provides an initial 1.5% to 2% growth buffer from the underlying economy before the growth rates cross over. This supports short-term economic recovery and enables the underlying economy to transition away from a significant rebuild stimulus. Once the growth crosses over, five years after the disaster, economic growth in the underlying economy remains buoyant even if official regional economic data suggest otherwise.
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.
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.
Hybrid broadband simulation methods typically compute high-frequency portion of ground-motions using a simplified-physics approach (commonly known as “stochastic method”) using the same 1D velocity profile, anelastic attenuation profile and site-attenuation (κ0) value for all sites. However, these parameters relating to Earth structure are known to vary spatially. In this study we modify this conventional approach for high-frequency ground-shaking by using site-specific input parameters (referred to as “site-specific”) and analyze improvements over using same parameters for all sites (referred to as “generic”). First, we theoretically understand how different 1D velocity profiles, anelastic attenuation profiles and site-attenuation (κ0) values affects the Fourier Acceleration Spectrum (FAS). Then, we apply site-specific method to simulate 10 events from the 2010-2011 Canterbury earthquake sequence to assess performance against the generic approach in predicting recorded ground-motions. Our initial results suggest that the site-specific method yields a lower simulation standard deviation than generic case.
The influence of nonlinear soil-foundation-structure interaction (SFSI) on the performance of multi-storey buildings during earthquake events has become increasingly important in earthquake resistant design. For buildings on shallow foundations, SFSI refers to nonlinear geometric effects associated with uplift of the foundation from the supporting soil as well as nonlinear soil deformation effects. These effects can potentially be beneficial for structural performance, reducing forces transmitted from ground shaking to the structure. However, there is also the potential consequence of residual settlement and rotation of the foundation. This Thesis investigates the influence of SFSI in the performance of multi-storey buildings on shallow foundations through earthquake observations, experimental testing, and development of spring-bed numerical models that can be incorporated into integrated earthquake resistant design procedures. Observations were made following the 22 February 2011 Christchurch Earthquake in New Zealand of a number of multi-storey buildings on shallow foundations that performed satisfactorily. This was predominantly the case in areas where shallow foundations, typically large raft foundations, were founded on competent gravel and where there was no significant manifestation of liquefaction at the ground surface. The properties of these buildings and the soils they are founded on directed experimental work that was conducted to investigate the mechanisms by which SFSI may have influenced the behaviour of these types of structure-foundation systems. Centrifuge experiments were undertaken at the University of Dundee, Scotland using a range of structure-foundation models and a layer of dense cohesionless soil to simulate the situation in Christchurch where multi-storey buildings on shallow foundations performed well. Three equivalent single degree of freedom (SDOF) models representing 3, 5, and 7 storey buildings with identical large raft foundations were subjected to a range of dynamic Ricker wavelet excitations and Christchurch Earthquake records to investigate the influence of SFSI on the response of the equivalent buildings. The experimental results show that nonlinear SFSI has a significant influence on structural response and overall foundation deformations, even though the large raft foundations on competent soil meant that there was a significant reserve of bearing capacity available and nonlinear deformations may have been considered to have had minimal effect. Uplift of the foundation from the supporting soil was observed across a wide range of input motion amplitudes and was particularly significant as the amplitude of motion increased. Permanent soil deformation represented by foundation settlement and residual rotation was also observed but mainly for the larger input motions. However, the absolute extent of uplift and permanent soil deformation was very small compared to the size of the foundation meaning the serviceability of the building would still likely be maintained during large earthquake events. Even so, the small extent of SFSI resulted in attenuation of the response of the structure as the equivalent period of vibration was lengthened and the equivalent damping in the system increased. The experimental work undertaken was used to validate and enhance numerical modelling techniques that are simple yet sophisticated and promote interaction between geotechnical and structural specialists involved in the design of multi-storey buildings. Spring-bed modelling techniques were utilised as they provide a balance between ease of use, and thus ease of interaction with structural specialists who have these techniques readily available in practice, and theoretically rigorous solutions. Fixed base and elastic spring-bed models showed they were unable to capture the behaviour of the structure-foundation models tested in the centrifuge experiments. SFSI spring-bed models were able to more accurately capture the behaviour but recommendations were proposed for the parameters used to define the springs so that the numerical models closely matched experimental results. From the spring-bed modelling and results of centrifuge experiments, an equivalent linear design procedure was proposed along with a procedure and recommendations for the implementation of nonlinear SFSI spring-bed models in practice. The combination of earthquake observations, experimental testing, and simplified numerical analysis has shown how SFSI is influential in the earthquake performance of multi-storey buildings on shallow foundations and should be incorporated into earthquake resistant design of these structures.
Orientation: Large-scale events such as disasters, wars and pandemics disrupt the economy by diverging resource allocation, which could alter employment growth within the economy during recovery.
Research purpose: The literature on the disaster–economic nexus predominantly considers the aggregate performance of the economy, including the stimulus injection. This research assesses the employment transition following a disaster by removing this stimulus injection and evaluating the economy’s performance during recovery.
Motivation for the study: The underlying economy’s performance without the stimulus’ benefit remains primarily unanswered. A single disaster event is used to assess the employment transition to guide future stimulus response for disasters.
Research approach/design and method: Canterbury, New Zealand, was affected by a series of earthquakes in 2010–2011 and is used as a single case study. Applying the historical construction–economic relationship, a counterfactual level of economic activity is quantified and compared with official results. Using an input–output model to remove the economy-wide impact from the elevated activity reveals the performance of the underlying economy and employment transition during recovery.
Main findings: The results indicate a return to a demand-driven level of building activity 10 years after the disaster. Employment transition is characterised by two distinct periods. The first 5 years are stimulus-driven, while the 5 years that follow are demand-driven from the underlying economy. After the initial period of elevated building activity, construction repositioned to its long-term level near 5% of value add. Practical/managerial implications: The level of building activity could be used to confidently assess the performance of regional economies following a destructive disaster. The study results argue for an incentive to redevelop the affected area as quickly as possible to mitigate the negative effect of the destruction and provide a stimulus for the economy. Contribution/value-add: This study contributes to a growing stream of regional disaster economics research that assesses the economic effect using a single case study.
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.
A video of a presentation by Jane Murray and Stephen Timms during the Social Recovery Stream of the 2016 People in Disasters Conference. The presentation is titled, "Land Use Recovery Plan: How an impact assessment process engaged communities in recovery planning".The abstract for this presentation reads as follows: In response to the Canterbury earthquakes, the Minister for Canterbury Earthquake Recovery directed Environment Canterbury (Canterbury's regional council) to prepare a Land Use Recovery Plan that would provide a spatial planning framework for Greater Christchurch and aid recovery from the Canterbury earthquakes. The Land Use Recovery Plan sets a policy and planning framework necessary to rebuild existing communities and develop new communities. As part of preparing the plan, an integrated assessment was undertaken to address wellbeing and sustainability concerns. This ensured that social impacts of the plan were likely to achieve better outcomes for communities. The process enabled a wide range of community and sector stakeholders to provide input at the very early stages of drafting the document. The integrated assessment considered the treatment of major land use issues in the plan, e.g. overall distribution of activities across the city, integrated transport routes, housing typography, social housing, employment and urban design, all of which have a key impact on health and wellbeing. Representatives from the Canterbury Health in All Policies Partnership were involved in designing a three-part assessment process that would provide a framework for the Land Use Recovery Plan writers to assess and improve the plan in terms of wellbeing and sustainability concerns. The detail of these assessment stages, and the influence that they had on the draft plan, will be outlined in the presentation. In summary, the three stages involved: developing key wellbeing and sustainability concerns that could form a set of criteria, analysing the preliminary draft of the Land Use Recovery Plan against the criteria in a broad sector workshop, and analysing the content and recommendations of the Draft Plan. This demonstrates the importance of integrated assessment influencing the Land Use Recovery Plan that in turn influences other key planning documents such as the District Plan. This process enabled a very complex document with wide-ranging implications to be broken down, enabling many groups, individuals and organisations to have their say in the recovery process. There is also a range of important lessons for recovery that can be applied to other projects and actions in a disaster recovery situation.
